Anticoagulation Stewardship Efforts Via Indication Reviews at a Veterans Affairs Health Care System

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Anticoagulation Stewardship Efforts Via Indication Reviews at a Veterans Affairs Health Care System

Due to the underlying mechanism of atrial fibrillation (Afib), clots can form within the left atrial appendage. Clots that become dislodged may lead to ischemic stroke and possibly death. The 2023 guidelines for atrial fibrillation from the American College of Cardiology and American Heart Association recommend anticoagulation therapy for patients with an Afib diagnosis and a CHA2DS2-VASc (congestive heart failure, hypertension, age ≥ 75 years, diabetes, stroke/vascular disease, age 65 to 74 years, and female sex) score pertinent for ≥ 1 non–sex-related factor (score ≥ 2 for women; ≥ 1 for men) to prevent stroke-related complications. The CHA2DS2-VASc score is a 9-point scoring tool based on comorbidities and conditions that increase risk of stroke in patients with Afib. Each value correlates to an annualized stroke risk percentage that increases as the score increases.

In clinical practice, patients meeting these thresholds are indicated for anticoagulation and are considered for indefinite use unless ≥ 1 of the following conditions are present: bleeding risk outweighs the stroke prevention benefit, Afib is episodic (< 48 hours) or a nonpharmacologic intervention, such as a left atrial appendage occlusion (LAAO) device is present.1

In patients with a diagnosed venous thromboembolism (VTE), such as deep vein thrombosis or pulmonary embolism, anticoagulation is used to treat the current thrombosis and prevent embolization that can ultimately lead to death. The 2021 guideline for VTE from the American College of Chest Physicians identifies certain risk factors that increase risk for VTE and categorizes them as transient or persistent. Transient risk factors include hospitalization > 3 days, major trauma, surgery, cast immobilization, hormone therapy, pregnancy, or prolonged travel > 8 hours. Persistent risk factors include malignancy, thrombophilia, and certain medications.

The guideline recommends therapy durations based on event frequency, the presence and classification of provoking risk factors, and bleeding risk. As the risk of recurrent thrombosis and other potential complications is greatest in the first 3 to 6 months after a diagnosed event, at least 3 months anticoagulation therapy is recommended following VTE diagnosis. At the 3-month mark, all regimens are suggested to be re-evaluated and considered for extended treatment duration if the event was unprovoked, recurrent, secondary to a persistent risk factor, or low bleed risk.2Anticoagulation is an important guideline-recommended pharmacologic intervention for various disease states, although its use is not without risks. The Institute for Safe Medication Practices has classified oral anticoagulants as high-alert medications. This designation was made because anticoagulant medications have the potential to cause harm when used or omitted in error and lead to life-threatening bleed or thrombotic complications.3Anticoagulation stewardship ensures that anticoagulation therapy is appropriately initiated, maintained, and discontinued when indicated. Because of the potential for harm, anticoagulation stewardship is an important part of Afib and VTE management. Pharmacists can help verify and evaluate anticoagulation therapies. Research suggests that pharmacist-led anticoagulation stewardship efforts may play a role in ensuring safer patient outcomes.4The purpose of this quality improvement (QI) study was to implement pharmacist-led anticoagulation stewardship practices at Veterans Affairs Phoenix Health Care System (VAPHCS) to identify veterans with Afib not currently on anticoagulation, as well as to identify veterans with a history of VTE events who have completed a sufficient treatment duration.

Methods

Anticoagulation stewardship efforts were implemented in 2 cohorts of patients: those with Afib who may be indicated to initiate anticoagulation, and those with a history of VTE events who may be indicated to consider anticoagulation discontinuation. Patient records were reviewed using a standardized note template, and recommendations to either initiate or discontinue anticoagulation therapy were documented. The VAPHCS Research Service reviewed this study and determined that it was not research and was exempt from institutional review board review.

Atrial Fibrillation Cohort

A population health dashboard created by the Stroke Prevention in Atrial Fibrillation/Flutter Targeting the uNTreated: a focus on health care disparities (SPAFF-TNT-D) national VA study team was used to identify veterans at VAPHCS with a diagnosis of Afib without an active VA prescription for an anticoagulant. The dashboard filtered and produced data points from the medical record that correlated to the components of the CHA2DS2-VASc score. All veterans identified by the dashboard with scores of 7 or 8 were included. No patients had a score of 9. Comprehensive chart reviews of available VA and non–VA-provided care records were conducted by the investigators, and a standardized note template designed by the SPAFF-TNT-D team (eAppendix 1) was used to document findings within the electronic health record (EHR). If anticoagulation was deemed to be indicated, the assigned primary care practitioner (PCP) as listed in the EHR was alerted to the note by the investigators for further evaluation and consideration of prescribing anticoagulation.

Venous Thromboembolism Cohort

VAPHCS pharmacy informatics pulled data that included veterans with documented VTE and an active VA anticoagulant prescription between November 2022 and November 2023. Veterans were reviewed in chronological order based on when the anticoagulant prescription was written. All veterans were included until an equal number of charts were reviewed in both the Afib and VTE cohorts. Comprehensive chart review of available VA- and non–VA-provided care records was conducted by the investigators, and a standardized note template as designed by the investigators (eAppendix 2) was used to document findings within the EHR. If the duration of anticoagulation therapy was deemed sufficient, the assigned anticoagulation clinical pharmacist practitioner (CPP) was alerted to the note by the investigators for further evaluation and consideration of discontinuing anticoagulation.

EHR reviews were conducted in October and November 2023 and lasted about 10 to 20 minutes per patient. To evaluate completeness and accuracy of the documented findings within the EHR, both investigators reviewed and cosigned the completed note template and verified the correct PCP was alerted to the recommendation for appropriate continuity of care. Results were reviewed in March 2024.

Outcomes

Atrial fibrillation cohort. The primary outcome was the number of veterans with Afib who were recommended to start anticoagulation therapy. Additional outcomes evaluated included the number of interventions completed, action taken by PCPs in response to the provided recommendation, and reasons provided by the investigators for not recommending initiation of anticoagulation therapy in specific veteran cases.

Venous thromboembolism cohort. The primary outcome was the number of veterans with a history of VTE events recommended to discontinue anticoagulation therapy. Additional outcomes included number of interventions completed, action taken by the anticoagulation CPP in response to the provided recommendation, and reasons provided by the investigators for not recommending discontinuation of anticoagulation therapy in specific veteran cases.

Analysis

Sample size was determined by the inclusion criteria and was not designed to attain statistical power. Data embedded in the Afib cohort standardized note template, also known as health factors, were later used for data analysis. Recommendations in the VTE cohort were manually tracked and recorded by the investigators. Results for this study were analyzed using descriptive statistics.

Results

A total of 114 veterans were reviewed and included in this study: 57 in each cohort. Seven recommendations were made regarding anticoagulation initiation for patients with Afib and 7 were made for anticoagulation discontinuation for patients with VTE (Table 1).

FDP04211410_T1

In the Afib cohort, 1 veteran was successfully initiated on anticoagulation therapy and 1 veteran was deemed appropriate for initiation of anticoagulation but was not reachable. Of the 5 recommendations with no action taken, 4 PCPs acknowledged the alert with no further documentation, and 1 PCP deferred the decision to cardiology with no further documentation. In the VTE cohort, 3 veterans successfully discontinued anticoagulation therapy and 2 veterans were further evaluated by the anticoagulation CPP and deemed appropriate to continue therapy based on potential for malignancy. Of the 2 recommendations with no action taken, 1 anticoagulation CPP acknowledged the alert with no further documentation and 1 anticoagulation CPP suggested further evaluation by PCP with no further documentation.

In the Afib cohort, a nonpharmacologic approach was defined as documentation of a LAAO device. An inaccurate diagnosis was defined as an Afib diagnosis being used in a previous visit, although there was no further confirmation of diagnosis via chart review. Veterans classified as already being on anticoagulation had documentation of non–VA-written anticoagulant prescriptions or receiving a supply of anticoagulants from a facility such as a nursing home. Anticoagulation was defined as unfavorable if a documented risk/benefit conversation was found via EHR review. Anticoagulation was defined as not indicated if the Afib was documented as transient, episodic, or historical (Table 2).

FDP04211410_T2

In the VTE cohort, no recommendations for discontinuation were made for veterans indicated to continue anticoagulation due to a concurrent Afib diagnosis. Chronic or recurrent events were defined as documentation of multiple VTE events and associated dates in the EHR. Persistent risk factors included malignancy or medications contributing to hypercoagulable states. Thrombophilia was defined as having documentation of a diagnosis in the EHR. An unprovoked event was defined as VTE without any documented transient risk factors (eg, hospitalization, trauma, surgery, cast immobilization, hormone therapy, pregnancy, or prolonged travel). Anticoagulation had already been discontinued in 1 veteran after the data were collected but before chart review occurred (Table 3).

FDP04211410_T3

Discussion

Pharmacy-led indication reviews resulted in appropriate recommendations for anticoagulation use in veterans with Afib and a history of VTE events. Overall, 12.3% of chart reviews in each cohort resulted in a recommendation being made, which was similar to the rate found by Koolian et al.5 In that study, 10% of recommendations were related to initiation or interruption of anticoagulation. This recommendation category consisted of several subcategories, including “suggesting therapeutic anticoagulation when none is currently ordered” and “suggesting anticoagulation cessation if no longer indicated,” but specific numerical prevalence was not provided.5

Online dashboard use allowed for greater population health management and identification of veterans with Afib who were not on active anticoagulation, providing opportunities to prevent stroke-related complications. Wang et al completed a similarly designed study that included a population health tool to identify patients with Afib who were not on anticoagulation and implemented pharmacist-led chart review and facilitation of recommendations to the responsible clinician. This study reviewed 1727 patients and recommended initiation of anticoagulation therapy for 75 (4.3%).6 The current study had a higher percentage of patients with recommendations for changes despite its smaller size.

Evaluating the duration of therapy for anticoagulation in veterans with a history of VTE events provided an opportunity to reduce unnecessary exposure to anticoagulation and minimize bleeding risks. Using a chart review process and standardized note template enabled the documentation of pertinent information that could be readily reviewed by the PCP. This process is a step toward ensuring VAPHCS PCPs provide guideline-recommended care and actively prevent stroke and bleeding complications. Adoption of this process into the current VAPHCS Anticoagulation Clinic workflow for review of veterans with either Afib or VTE could lead to more EHRs being reviewed and recommendations made, ultimately improving patient outcomes. 

Therapeutic interventions based on the recommendations were completed for 1 of 7 veterans (14%) and 3 of 7 veterans (43%) in the Afib and VTE cohorts, respectively. The prevalence of completed interventions in this anticoagulation stewardship study was higher than those in Wang et al, who found only 9% of their recommendations resulted in PCPs considering action related to anticoagulation, and only 4% were successfully initiated.6

In the Afib cohort, veterans identified by the dashboard with a CHA2DS2-VASc of 7 or 8 were prioritized for review. Reviewing these veterans ensured that patients with the highest stroke risk were sufficiently evaluated and started on anticoagulation as needed to reduce stroke-related complications. In contrast, because these veterans had higher CHA2DS2-VASc scores, they may have already been evaluated for anticoagulation in the past and had a documented rationale for not being placed on anticoagulation (LAAO device placement was the most common rationale). Focusing on veterans with a lower CHA2DS2-VASc score such as 1 for men or 2 for women could potentially include more opportunities for recommendations. Although stroke risk may be lower in this population compared with those with higher CHA2DS2-VASc scores, guideline-recommended anticoagulation use may be missed for these patients. 

In the VTE cohort, veterans with an anticoagulant prescription written 12 months before data collection were prioritized for review. Reviewing these veterans ensured that anticoagulation therapy met guideline recommendations of at least 3 months, with potential for extended duration upon further evaluation by a provider at that time. Based on collected results, most veterans were already reevaluated and had documented reasons why anticoagulation was still indicated; concurrent Afib was most common followed by chronic or recurrent VTE. Reviewing veterans with more recent prescriptions just over the recommended 3-month duration could potentially include more opportunities for recommendations to be made. It is more likely that by 3 months another PCP had not already weighed in on the duration of therapy, and the anticoagulation CPP could ensure a thorough review is conducted with guideline-based recommendations.

Most published literature on anticoagulation stewardship efforts is focused on inpatient management and policy changes, or concentrate on attributes of therapy such as appropriate dosing and drug interactions. This study highlighted that gaps in care related to anticoagulation use and discontinuation are present in the VAPHCS population and can be appropriately addressed via pharmacist-led indication reviews. Future studies designed to focus on initiating anticoagulation where appropriate, and discontinuing where a sufficient treatment period has been completed, are warranted to minimize this gap in care and allow health systems to work toward process changes to ensure safe and optimized care is provided for the patients they serve.

Limitations

In the Afib cohort, 5 of 7 recommendations (71%) had no further action taken by the PCP, which may represent a barrier to care. In contrast, 2 of 7 recommendations (29%) had no further action in the VTE cohort. It is possible that the difference can be attributed to the anticoagulation CPP receiving VTE alerts and PCPs receiving Afib alerts. The anticoagulation CPP was familiar with this QI study and may have better understood the purpose of the chart review and the need to provide a timely response. PCPs may have been less likely to take action because they were unfamiliar with the anticoagulation stewardship initiative and standardized note template or overwhelmed by too many EHR alerts.

The lack of PCP response to a virtual alert or message also was observed by Wang et al, whereas Koolian et al reported higher intervention completion rates, with verbal recommendations being made to the responsible clinicians. To further ensure these pertinent recommendations for anticoagulation initiation in veterans with Afib are properly reviewed and evaluated, future research could include intentional follow-up with the PCP regarding the alert, PCP-specific education about the anticoagulation stewardship initiative and the role of the standardized note template, and collaboration with PCPs to identify alternative ways to relay recommendations in a way that would ensure the completion of appropriate and timely review.

Conclusions

This study identified gaps in care related to anticoagulation needs in the VAPHCS veteran population. Utilizing a standardized indication review process allows pharmacists to evaluate anticoagulant use for both appropriate indication and duration of therapy. Providing recommendations via chart review notes and alerting respective PCPs and CPPs results in veterans receiving safe and optimized care regarding their anticoagulation needs.

References
  1. Joglar JA, Chung MK, Armbruster AL, et al. 2023 ACC/AHA/ACCP/HRS guideline for the diagnosis and management of atrial fibrillation: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2024;149:e1-e156. doi:10.1161/CIR.0000000000001193
  2. Stevens SM, Woller SC, Kreuziger LB, et al. Antithrombotic therapy for VTE disease: second update of the CHEST guideline and expert panel report. Chest. 2021;160:e545-e608. doi:10.1016/j.chest.2021.07.055
  3. Institute for Safe Medication Practices (ISMP). List of high-alert medications in community/ambulatory care settings. ISMP. September 30, 2021. Accessed September 11, 2025. https://home.ecri.org/blogs/ismp-resources/high-alert-medications-in-community-ambulatory-care-settings
  4. Burnett AE, Barnes GD. A call to action for anticoagulation stewardship. Res Pract Thromb Haemost. 2022;6:e12757. doi:10.1002/rth2.12757
  5. Koolian M, Wiseman D, Mantzanis H, et al. Anticoagulation stewardship: descriptive analysis of a novel approach to appropriate anticoagulant prescription. Res Pract Thromb Haemost. 2022;6:e12758. doi:10.1002/rth2.12758
  6. Wang SV, Rogers JR, Jin Y, et al. Stepped-wedge randomised trial to evaluate population health intervention designed to increase appropriate anticoagulation in patients with atrial fibrillation. BMJ Qual Saf. 2019;28:835-842. doi:10.1136/bmjqs-2019-009367
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Alexandra Brown, PharmDa; Annie Tam, PharmDa

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Author affiliations aVeterans Affairs Phoenix Health Care System, Arizona

Author disclosures The authors report no actual or potential conflicts of interest with regard to this article.

Fed Pract. 2025;42(11). Published online November 15. doi:10.12788/fp.0648

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Alexandra Brown, PharmDa; Annie Tam, PharmDa

Correspondence: Alexandra Brown ([email protected])

Author affiliations aVeterans Affairs Phoenix Health Care System, Arizona

Author disclosures The authors report no actual or potential conflicts of interest with regard to this article.

Fed Pract. 2025;42(11). Published online November 15. doi:10.12788/fp.0648

Author and Disclosure Information

Alexandra Brown, PharmDa; Annie Tam, PharmDa

Correspondence: Alexandra Brown ([email protected])

Author affiliations aVeterans Affairs Phoenix Health Care System, Arizona

Author disclosures The authors report no actual or potential conflicts of interest with regard to this article.

Fed Pract. 2025;42(11). Published online November 15. doi:10.12788/fp.0648

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Due to the underlying mechanism of atrial fibrillation (Afib), clots can form within the left atrial appendage. Clots that become dislodged may lead to ischemic stroke and possibly death. The 2023 guidelines for atrial fibrillation from the American College of Cardiology and American Heart Association recommend anticoagulation therapy for patients with an Afib diagnosis and a CHA2DS2-VASc (congestive heart failure, hypertension, age ≥ 75 years, diabetes, stroke/vascular disease, age 65 to 74 years, and female sex) score pertinent for ≥ 1 non–sex-related factor (score ≥ 2 for women; ≥ 1 for men) to prevent stroke-related complications. The CHA2DS2-VASc score is a 9-point scoring tool based on comorbidities and conditions that increase risk of stroke in patients with Afib. Each value correlates to an annualized stroke risk percentage that increases as the score increases.

In clinical practice, patients meeting these thresholds are indicated for anticoagulation and are considered for indefinite use unless ≥ 1 of the following conditions are present: bleeding risk outweighs the stroke prevention benefit, Afib is episodic (< 48 hours) or a nonpharmacologic intervention, such as a left atrial appendage occlusion (LAAO) device is present.1

In patients with a diagnosed venous thromboembolism (VTE), such as deep vein thrombosis or pulmonary embolism, anticoagulation is used to treat the current thrombosis and prevent embolization that can ultimately lead to death. The 2021 guideline for VTE from the American College of Chest Physicians identifies certain risk factors that increase risk for VTE and categorizes them as transient or persistent. Transient risk factors include hospitalization > 3 days, major trauma, surgery, cast immobilization, hormone therapy, pregnancy, or prolonged travel > 8 hours. Persistent risk factors include malignancy, thrombophilia, and certain medications.

The guideline recommends therapy durations based on event frequency, the presence and classification of provoking risk factors, and bleeding risk. As the risk of recurrent thrombosis and other potential complications is greatest in the first 3 to 6 months after a diagnosed event, at least 3 months anticoagulation therapy is recommended following VTE diagnosis. At the 3-month mark, all regimens are suggested to be re-evaluated and considered for extended treatment duration if the event was unprovoked, recurrent, secondary to a persistent risk factor, or low bleed risk.2Anticoagulation is an important guideline-recommended pharmacologic intervention for various disease states, although its use is not without risks. The Institute for Safe Medication Practices has classified oral anticoagulants as high-alert medications. This designation was made because anticoagulant medications have the potential to cause harm when used or omitted in error and lead to life-threatening bleed or thrombotic complications.3Anticoagulation stewardship ensures that anticoagulation therapy is appropriately initiated, maintained, and discontinued when indicated. Because of the potential for harm, anticoagulation stewardship is an important part of Afib and VTE management. Pharmacists can help verify and evaluate anticoagulation therapies. Research suggests that pharmacist-led anticoagulation stewardship efforts may play a role in ensuring safer patient outcomes.4The purpose of this quality improvement (QI) study was to implement pharmacist-led anticoagulation stewardship practices at Veterans Affairs Phoenix Health Care System (VAPHCS) to identify veterans with Afib not currently on anticoagulation, as well as to identify veterans with a history of VTE events who have completed a sufficient treatment duration.

Methods

Anticoagulation stewardship efforts were implemented in 2 cohorts of patients: those with Afib who may be indicated to initiate anticoagulation, and those with a history of VTE events who may be indicated to consider anticoagulation discontinuation. Patient records were reviewed using a standardized note template, and recommendations to either initiate or discontinue anticoagulation therapy were documented. The VAPHCS Research Service reviewed this study and determined that it was not research and was exempt from institutional review board review.

Atrial Fibrillation Cohort

A population health dashboard created by the Stroke Prevention in Atrial Fibrillation/Flutter Targeting the uNTreated: a focus on health care disparities (SPAFF-TNT-D) national VA study team was used to identify veterans at VAPHCS with a diagnosis of Afib without an active VA prescription for an anticoagulant. The dashboard filtered and produced data points from the medical record that correlated to the components of the CHA2DS2-VASc score. All veterans identified by the dashboard with scores of 7 or 8 were included. No patients had a score of 9. Comprehensive chart reviews of available VA and non–VA-provided care records were conducted by the investigators, and a standardized note template designed by the SPAFF-TNT-D team (eAppendix 1) was used to document findings within the electronic health record (EHR). If anticoagulation was deemed to be indicated, the assigned primary care practitioner (PCP) as listed in the EHR was alerted to the note by the investigators for further evaluation and consideration of prescribing anticoagulation.

Venous Thromboembolism Cohort

VAPHCS pharmacy informatics pulled data that included veterans with documented VTE and an active VA anticoagulant prescription between November 2022 and November 2023. Veterans were reviewed in chronological order based on when the anticoagulant prescription was written. All veterans were included until an equal number of charts were reviewed in both the Afib and VTE cohorts. Comprehensive chart review of available VA- and non–VA-provided care records was conducted by the investigators, and a standardized note template as designed by the investigators (eAppendix 2) was used to document findings within the EHR. If the duration of anticoagulation therapy was deemed sufficient, the assigned anticoagulation clinical pharmacist practitioner (CPP) was alerted to the note by the investigators for further evaluation and consideration of discontinuing anticoagulation.

EHR reviews were conducted in October and November 2023 and lasted about 10 to 20 minutes per patient. To evaluate completeness and accuracy of the documented findings within the EHR, both investigators reviewed and cosigned the completed note template and verified the correct PCP was alerted to the recommendation for appropriate continuity of care. Results were reviewed in March 2024.

Outcomes

Atrial fibrillation cohort. The primary outcome was the number of veterans with Afib who were recommended to start anticoagulation therapy. Additional outcomes evaluated included the number of interventions completed, action taken by PCPs in response to the provided recommendation, and reasons provided by the investigators for not recommending initiation of anticoagulation therapy in specific veteran cases.

Venous thromboembolism cohort. The primary outcome was the number of veterans with a history of VTE events recommended to discontinue anticoagulation therapy. Additional outcomes included number of interventions completed, action taken by the anticoagulation CPP in response to the provided recommendation, and reasons provided by the investigators for not recommending discontinuation of anticoagulation therapy in specific veteran cases.

Analysis

Sample size was determined by the inclusion criteria and was not designed to attain statistical power. Data embedded in the Afib cohort standardized note template, also known as health factors, were later used for data analysis. Recommendations in the VTE cohort were manually tracked and recorded by the investigators. Results for this study were analyzed using descriptive statistics.

Results

A total of 114 veterans were reviewed and included in this study: 57 in each cohort. Seven recommendations were made regarding anticoagulation initiation for patients with Afib and 7 were made for anticoagulation discontinuation for patients with VTE (Table 1).

FDP04211410_T1

In the Afib cohort, 1 veteran was successfully initiated on anticoagulation therapy and 1 veteran was deemed appropriate for initiation of anticoagulation but was not reachable. Of the 5 recommendations with no action taken, 4 PCPs acknowledged the alert with no further documentation, and 1 PCP deferred the decision to cardiology with no further documentation. In the VTE cohort, 3 veterans successfully discontinued anticoagulation therapy and 2 veterans were further evaluated by the anticoagulation CPP and deemed appropriate to continue therapy based on potential for malignancy. Of the 2 recommendations with no action taken, 1 anticoagulation CPP acknowledged the alert with no further documentation and 1 anticoagulation CPP suggested further evaluation by PCP with no further documentation.

In the Afib cohort, a nonpharmacologic approach was defined as documentation of a LAAO device. An inaccurate diagnosis was defined as an Afib diagnosis being used in a previous visit, although there was no further confirmation of diagnosis via chart review. Veterans classified as already being on anticoagulation had documentation of non–VA-written anticoagulant prescriptions or receiving a supply of anticoagulants from a facility such as a nursing home. Anticoagulation was defined as unfavorable if a documented risk/benefit conversation was found via EHR review. Anticoagulation was defined as not indicated if the Afib was documented as transient, episodic, or historical (Table 2).

FDP04211410_T2

In the VTE cohort, no recommendations for discontinuation were made for veterans indicated to continue anticoagulation due to a concurrent Afib diagnosis. Chronic or recurrent events were defined as documentation of multiple VTE events and associated dates in the EHR. Persistent risk factors included malignancy or medications contributing to hypercoagulable states. Thrombophilia was defined as having documentation of a diagnosis in the EHR. An unprovoked event was defined as VTE without any documented transient risk factors (eg, hospitalization, trauma, surgery, cast immobilization, hormone therapy, pregnancy, or prolonged travel). Anticoagulation had already been discontinued in 1 veteran after the data were collected but before chart review occurred (Table 3).

FDP04211410_T3

Discussion

Pharmacy-led indication reviews resulted in appropriate recommendations for anticoagulation use in veterans with Afib and a history of VTE events. Overall, 12.3% of chart reviews in each cohort resulted in a recommendation being made, which was similar to the rate found by Koolian et al.5 In that study, 10% of recommendations were related to initiation or interruption of anticoagulation. This recommendation category consisted of several subcategories, including “suggesting therapeutic anticoagulation when none is currently ordered” and “suggesting anticoagulation cessation if no longer indicated,” but specific numerical prevalence was not provided.5

Online dashboard use allowed for greater population health management and identification of veterans with Afib who were not on active anticoagulation, providing opportunities to prevent stroke-related complications. Wang et al completed a similarly designed study that included a population health tool to identify patients with Afib who were not on anticoagulation and implemented pharmacist-led chart review and facilitation of recommendations to the responsible clinician. This study reviewed 1727 patients and recommended initiation of anticoagulation therapy for 75 (4.3%).6 The current study had a higher percentage of patients with recommendations for changes despite its smaller size.

Evaluating the duration of therapy for anticoagulation in veterans with a history of VTE events provided an opportunity to reduce unnecessary exposure to anticoagulation and minimize bleeding risks. Using a chart review process and standardized note template enabled the documentation of pertinent information that could be readily reviewed by the PCP. This process is a step toward ensuring VAPHCS PCPs provide guideline-recommended care and actively prevent stroke and bleeding complications. Adoption of this process into the current VAPHCS Anticoagulation Clinic workflow for review of veterans with either Afib or VTE could lead to more EHRs being reviewed and recommendations made, ultimately improving patient outcomes. 

Therapeutic interventions based on the recommendations were completed for 1 of 7 veterans (14%) and 3 of 7 veterans (43%) in the Afib and VTE cohorts, respectively. The prevalence of completed interventions in this anticoagulation stewardship study was higher than those in Wang et al, who found only 9% of their recommendations resulted in PCPs considering action related to anticoagulation, and only 4% were successfully initiated.6

In the Afib cohort, veterans identified by the dashboard with a CHA2DS2-VASc of 7 or 8 were prioritized for review. Reviewing these veterans ensured that patients with the highest stroke risk were sufficiently evaluated and started on anticoagulation as needed to reduce stroke-related complications. In contrast, because these veterans had higher CHA2DS2-VASc scores, they may have already been evaluated for anticoagulation in the past and had a documented rationale for not being placed on anticoagulation (LAAO device placement was the most common rationale). Focusing on veterans with a lower CHA2DS2-VASc score such as 1 for men or 2 for women could potentially include more opportunities for recommendations. Although stroke risk may be lower in this population compared with those with higher CHA2DS2-VASc scores, guideline-recommended anticoagulation use may be missed for these patients. 

In the VTE cohort, veterans with an anticoagulant prescription written 12 months before data collection were prioritized for review. Reviewing these veterans ensured that anticoagulation therapy met guideline recommendations of at least 3 months, with potential for extended duration upon further evaluation by a provider at that time. Based on collected results, most veterans were already reevaluated and had documented reasons why anticoagulation was still indicated; concurrent Afib was most common followed by chronic or recurrent VTE. Reviewing veterans with more recent prescriptions just over the recommended 3-month duration could potentially include more opportunities for recommendations to be made. It is more likely that by 3 months another PCP had not already weighed in on the duration of therapy, and the anticoagulation CPP could ensure a thorough review is conducted with guideline-based recommendations.

Most published literature on anticoagulation stewardship efforts is focused on inpatient management and policy changes, or concentrate on attributes of therapy such as appropriate dosing and drug interactions. This study highlighted that gaps in care related to anticoagulation use and discontinuation are present in the VAPHCS population and can be appropriately addressed via pharmacist-led indication reviews. Future studies designed to focus on initiating anticoagulation where appropriate, and discontinuing where a sufficient treatment period has been completed, are warranted to minimize this gap in care and allow health systems to work toward process changes to ensure safe and optimized care is provided for the patients they serve.

Limitations

In the Afib cohort, 5 of 7 recommendations (71%) had no further action taken by the PCP, which may represent a barrier to care. In contrast, 2 of 7 recommendations (29%) had no further action in the VTE cohort. It is possible that the difference can be attributed to the anticoagulation CPP receiving VTE alerts and PCPs receiving Afib alerts. The anticoagulation CPP was familiar with this QI study and may have better understood the purpose of the chart review and the need to provide a timely response. PCPs may have been less likely to take action because they were unfamiliar with the anticoagulation stewardship initiative and standardized note template or overwhelmed by too many EHR alerts.

The lack of PCP response to a virtual alert or message also was observed by Wang et al, whereas Koolian et al reported higher intervention completion rates, with verbal recommendations being made to the responsible clinicians. To further ensure these pertinent recommendations for anticoagulation initiation in veterans with Afib are properly reviewed and evaluated, future research could include intentional follow-up with the PCP regarding the alert, PCP-specific education about the anticoagulation stewardship initiative and the role of the standardized note template, and collaboration with PCPs to identify alternative ways to relay recommendations in a way that would ensure the completion of appropriate and timely review.

Conclusions

This study identified gaps in care related to anticoagulation needs in the VAPHCS veteran population. Utilizing a standardized indication review process allows pharmacists to evaluate anticoagulant use for both appropriate indication and duration of therapy. Providing recommendations via chart review notes and alerting respective PCPs and CPPs results in veterans receiving safe and optimized care regarding their anticoagulation needs.

Due to the underlying mechanism of atrial fibrillation (Afib), clots can form within the left atrial appendage. Clots that become dislodged may lead to ischemic stroke and possibly death. The 2023 guidelines for atrial fibrillation from the American College of Cardiology and American Heart Association recommend anticoagulation therapy for patients with an Afib diagnosis and a CHA2DS2-VASc (congestive heart failure, hypertension, age ≥ 75 years, diabetes, stroke/vascular disease, age 65 to 74 years, and female sex) score pertinent for ≥ 1 non–sex-related factor (score ≥ 2 for women; ≥ 1 for men) to prevent stroke-related complications. The CHA2DS2-VASc score is a 9-point scoring tool based on comorbidities and conditions that increase risk of stroke in patients with Afib. Each value correlates to an annualized stroke risk percentage that increases as the score increases.

In clinical practice, patients meeting these thresholds are indicated for anticoagulation and are considered for indefinite use unless ≥ 1 of the following conditions are present: bleeding risk outweighs the stroke prevention benefit, Afib is episodic (< 48 hours) or a nonpharmacologic intervention, such as a left atrial appendage occlusion (LAAO) device is present.1

In patients with a diagnosed venous thromboembolism (VTE), such as deep vein thrombosis or pulmonary embolism, anticoagulation is used to treat the current thrombosis and prevent embolization that can ultimately lead to death. The 2021 guideline for VTE from the American College of Chest Physicians identifies certain risk factors that increase risk for VTE and categorizes them as transient or persistent. Transient risk factors include hospitalization > 3 days, major trauma, surgery, cast immobilization, hormone therapy, pregnancy, or prolonged travel > 8 hours. Persistent risk factors include malignancy, thrombophilia, and certain medications.

The guideline recommends therapy durations based on event frequency, the presence and classification of provoking risk factors, and bleeding risk. As the risk of recurrent thrombosis and other potential complications is greatest in the first 3 to 6 months after a diagnosed event, at least 3 months anticoagulation therapy is recommended following VTE diagnosis. At the 3-month mark, all regimens are suggested to be re-evaluated and considered for extended treatment duration if the event was unprovoked, recurrent, secondary to a persistent risk factor, or low bleed risk.2Anticoagulation is an important guideline-recommended pharmacologic intervention for various disease states, although its use is not without risks. The Institute for Safe Medication Practices has classified oral anticoagulants as high-alert medications. This designation was made because anticoagulant medications have the potential to cause harm when used or omitted in error and lead to life-threatening bleed or thrombotic complications.3Anticoagulation stewardship ensures that anticoagulation therapy is appropriately initiated, maintained, and discontinued when indicated. Because of the potential for harm, anticoagulation stewardship is an important part of Afib and VTE management. Pharmacists can help verify and evaluate anticoagulation therapies. Research suggests that pharmacist-led anticoagulation stewardship efforts may play a role in ensuring safer patient outcomes.4The purpose of this quality improvement (QI) study was to implement pharmacist-led anticoagulation stewardship practices at Veterans Affairs Phoenix Health Care System (VAPHCS) to identify veterans with Afib not currently on anticoagulation, as well as to identify veterans with a history of VTE events who have completed a sufficient treatment duration.

Methods

Anticoagulation stewardship efforts were implemented in 2 cohorts of patients: those with Afib who may be indicated to initiate anticoagulation, and those with a history of VTE events who may be indicated to consider anticoagulation discontinuation. Patient records were reviewed using a standardized note template, and recommendations to either initiate or discontinue anticoagulation therapy were documented. The VAPHCS Research Service reviewed this study and determined that it was not research and was exempt from institutional review board review.

Atrial Fibrillation Cohort

A population health dashboard created by the Stroke Prevention in Atrial Fibrillation/Flutter Targeting the uNTreated: a focus on health care disparities (SPAFF-TNT-D) national VA study team was used to identify veterans at VAPHCS with a diagnosis of Afib without an active VA prescription for an anticoagulant. The dashboard filtered and produced data points from the medical record that correlated to the components of the CHA2DS2-VASc score. All veterans identified by the dashboard with scores of 7 or 8 were included. No patients had a score of 9. Comprehensive chart reviews of available VA and non–VA-provided care records were conducted by the investigators, and a standardized note template designed by the SPAFF-TNT-D team (eAppendix 1) was used to document findings within the electronic health record (EHR). If anticoagulation was deemed to be indicated, the assigned primary care practitioner (PCP) as listed in the EHR was alerted to the note by the investigators for further evaluation and consideration of prescribing anticoagulation.

Venous Thromboembolism Cohort

VAPHCS pharmacy informatics pulled data that included veterans with documented VTE and an active VA anticoagulant prescription between November 2022 and November 2023. Veterans were reviewed in chronological order based on when the anticoagulant prescription was written. All veterans were included until an equal number of charts were reviewed in both the Afib and VTE cohorts. Comprehensive chart review of available VA- and non–VA-provided care records was conducted by the investigators, and a standardized note template as designed by the investigators (eAppendix 2) was used to document findings within the EHR. If the duration of anticoagulation therapy was deemed sufficient, the assigned anticoagulation clinical pharmacist practitioner (CPP) was alerted to the note by the investigators for further evaluation and consideration of discontinuing anticoagulation.

EHR reviews were conducted in October and November 2023 and lasted about 10 to 20 minutes per patient. To evaluate completeness and accuracy of the documented findings within the EHR, both investigators reviewed and cosigned the completed note template and verified the correct PCP was alerted to the recommendation for appropriate continuity of care. Results were reviewed in March 2024.

Outcomes

Atrial fibrillation cohort. The primary outcome was the number of veterans with Afib who were recommended to start anticoagulation therapy. Additional outcomes evaluated included the number of interventions completed, action taken by PCPs in response to the provided recommendation, and reasons provided by the investigators for not recommending initiation of anticoagulation therapy in specific veteran cases.

Venous thromboembolism cohort. The primary outcome was the number of veterans with a history of VTE events recommended to discontinue anticoagulation therapy. Additional outcomes included number of interventions completed, action taken by the anticoagulation CPP in response to the provided recommendation, and reasons provided by the investigators for not recommending discontinuation of anticoagulation therapy in specific veteran cases.

Analysis

Sample size was determined by the inclusion criteria and was not designed to attain statistical power. Data embedded in the Afib cohort standardized note template, also known as health factors, were later used for data analysis. Recommendations in the VTE cohort were manually tracked and recorded by the investigators. Results for this study were analyzed using descriptive statistics.

Results

A total of 114 veterans were reviewed and included in this study: 57 in each cohort. Seven recommendations were made regarding anticoagulation initiation for patients with Afib and 7 were made for anticoagulation discontinuation for patients with VTE (Table 1).

FDP04211410_T1

In the Afib cohort, 1 veteran was successfully initiated on anticoagulation therapy and 1 veteran was deemed appropriate for initiation of anticoagulation but was not reachable. Of the 5 recommendations with no action taken, 4 PCPs acknowledged the alert with no further documentation, and 1 PCP deferred the decision to cardiology with no further documentation. In the VTE cohort, 3 veterans successfully discontinued anticoagulation therapy and 2 veterans were further evaluated by the anticoagulation CPP and deemed appropriate to continue therapy based on potential for malignancy. Of the 2 recommendations with no action taken, 1 anticoagulation CPP acknowledged the alert with no further documentation and 1 anticoagulation CPP suggested further evaluation by PCP with no further documentation.

In the Afib cohort, a nonpharmacologic approach was defined as documentation of a LAAO device. An inaccurate diagnosis was defined as an Afib diagnosis being used in a previous visit, although there was no further confirmation of diagnosis via chart review. Veterans classified as already being on anticoagulation had documentation of non–VA-written anticoagulant prescriptions or receiving a supply of anticoagulants from a facility such as a nursing home. Anticoagulation was defined as unfavorable if a documented risk/benefit conversation was found via EHR review. Anticoagulation was defined as not indicated if the Afib was documented as transient, episodic, or historical (Table 2).

FDP04211410_T2

In the VTE cohort, no recommendations for discontinuation were made for veterans indicated to continue anticoagulation due to a concurrent Afib diagnosis. Chronic or recurrent events were defined as documentation of multiple VTE events and associated dates in the EHR. Persistent risk factors included malignancy or medications contributing to hypercoagulable states. Thrombophilia was defined as having documentation of a diagnosis in the EHR. An unprovoked event was defined as VTE without any documented transient risk factors (eg, hospitalization, trauma, surgery, cast immobilization, hormone therapy, pregnancy, or prolonged travel). Anticoagulation had already been discontinued in 1 veteran after the data were collected but before chart review occurred (Table 3).

FDP04211410_T3

Discussion

Pharmacy-led indication reviews resulted in appropriate recommendations for anticoagulation use in veterans with Afib and a history of VTE events. Overall, 12.3% of chart reviews in each cohort resulted in a recommendation being made, which was similar to the rate found by Koolian et al.5 In that study, 10% of recommendations were related to initiation or interruption of anticoagulation. This recommendation category consisted of several subcategories, including “suggesting therapeutic anticoagulation when none is currently ordered” and “suggesting anticoagulation cessation if no longer indicated,” but specific numerical prevalence was not provided.5

Online dashboard use allowed for greater population health management and identification of veterans with Afib who were not on active anticoagulation, providing opportunities to prevent stroke-related complications. Wang et al completed a similarly designed study that included a population health tool to identify patients with Afib who were not on anticoagulation and implemented pharmacist-led chart review and facilitation of recommendations to the responsible clinician. This study reviewed 1727 patients and recommended initiation of anticoagulation therapy for 75 (4.3%).6 The current study had a higher percentage of patients with recommendations for changes despite its smaller size.

Evaluating the duration of therapy for anticoagulation in veterans with a history of VTE events provided an opportunity to reduce unnecessary exposure to anticoagulation and minimize bleeding risks. Using a chart review process and standardized note template enabled the documentation of pertinent information that could be readily reviewed by the PCP. This process is a step toward ensuring VAPHCS PCPs provide guideline-recommended care and actively prevent stroke and bleeding complications. Adoption of this process into the current VAPHCS Anticoagulation Clinic workflow for review of veterans with either Afib or VTE could lead to more EHRs being reviewed and recommendations made, ultimately improving patient outcomes. 

Therapeutic interventions based on the recommendations were completed for 1 of 7 veterans (14%) and 3 of 7 veterans (43%) in the Afib and VTE cohorts, respectively. The prevalence of completed interventions in this anticoagulation stewardship study was higher than those in Wang et al, who found only 9% of their recommendations resulted in PCPs considering action related to anticoagulation, and only 4% were successfully initiated.6

In the Afib cohort, veterans identified by the dashboard with a CHA2DS2-VASc of 7 or 8 were prioritized for review. Reviewing these veterans ensured that patients with the highest stroke risk were sufficiently evaluated and started on anticoagulation as needed to reduce stroke-related complications. In contrast, because these veterans had higher CHA2DS2-VASc scores, they may have already been evaluated for anticoagulation in the past and had a documented rationale for not being placed on anticoagulation (LAAO device placement was the most common rationale). Focusing on veterans with a lower CHA2DS2-VASc score such as 1 for men or 2 for women could potentially include more opportunities for recommendations. Although stroke risk may be lower in this population compared with those with higher CHA2DS2-VASc scores, guideline-recommended anticoagulation use may be missed for these patients. 

In the VTE cohort, veterans with an anticoagulant prescription written 12 months before data collection were prioritized for review. Reviewing these veterans ensured that anticoagulation therapy met guideline recommendations of at least 3 months, with potential for extended duration upon further evaluation by a provider at that time. Based on collected results, most veterans were already reevaluated and had documented reasons why anticoagulation was still indicated; concurrent Afib was most common followed by chronic or recurrent VTE. Reviewing veterans with more recent prescriptions just over the recommended 3-month duration could potentially include more opportunities for recommendations to be made. It is more likely that by 3 months another PCP had not already weighed in on the duration of therapy, and the anticoagulation CPP could ensure a thorough review is conducted with guideline-based recommendations.

Most published literature on anticoagulation stewardship efforts is focused on inpatient management and policy changes, or concentrate on attributes of therapy such as appropriate dosing and drug interactions. This study highlighted that gaps in care related to anticoagulation use and discontinuation are present in the VAPHCS population and can be appropriately addressed via pharmacist-led indication reviews. Future studies designed to focus on initiating anticoagulation where appropriate, and discontinuing where a sufficient treatment period has been completed, are warranted to minimize this gap in care and allow health systems to work toward process changes to ensure safe and optimized care is provided for the patients they serve.

Limitations

In the Afib cohort, 5 of 7 recommendations (71%) had no further action taken by the PCP, which may represent a barrier to care. In contrast, 2 of 7 recommendations (29%) had no further action in the VTE cohort. It is possible that the difference can be attributed to the anticoagulation CPP receiving VTE alerts and PCPs receiving Afib alerts. The anticoagulation CPP was familiar with this QI study and may have better understood the purpose of the chart review and the need to provide a timely response. PCPs may have been less likely to take action because they were unfamiliar with the anticoagulation stewardship initiative and standardized note template or overwhelmed by too many EHR alerts.

The lack of PCP response to a virtual alert or message also was observed by Wang et al, whereas Koolian et al reported higher intervention completion rates, with verbal recommendations being made to the responsible clinicians. To further ensure these pertinent recommendations for anticoagulation initiation in veterans with Afib are properly reviewed and evaluated, future research could include intentional follow-up with the PCP regarding the alert, PCP-specific education about the anticoagulation stewardship initiative and the role of the standardized note template, and collaboration with PCPs to identify alternative ways to relay recommendations in a way that would ensure the completion of appropriate and timely review.

Conclusions

This study identified gaps in care related to anticoagulation needs in the VAPHCS veteran population. Utilizing a standardized indication review process allows pharmacists to evaluate anticoagulant use for both appropriate indication and duration of therapy. Providing recommendations via chart review notes and alerting respective PCPs and CPPs results in veterans receiving safe and optimized care regarding their anticoagulation needs.

References
  1. Joglar JA, Chung MK, Armbruster AL, et al. 2023 ACC/AHA/ACCP/HRS guideline for the diagnosis and management of atrial fibrillation: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2024;149:e1-e156. doi:10.1161/CIR.0000000000001193
  2. Stevens SM, Woller SC, Kreuziger LB, et al. Antithrombotic therapy for VTE disease: second update of the CHEST guideline and expert panel report. Chest. 2021;160:e545-e608. doi:10.1016/j.chest.2021.07.055
  3. Institute for Safe Medication Practices (ISMP). List of high-alert medications in community/ambulatory care settings. ISMP. September 30, 2021. Accessed September 11, 2025. https://home.ecri.org/blogs/ismp-resources/high-alert-medications-in-community-ambulatory-care-settings
  4. Burnett AE, Barnes GD. A call to action for anticoagulation stewardship. Res Pract Thromb Haemost. 2022;6:e12757. doi:10.1002/rth2.12757
  5. Koolian M, Wiseman D, Mantzanis H, et al. Anticoagulation stewardship: descriptive analysis of a novel approach to appropriate anticoagulant prescription. Res Pract Thromb Haemost. 2022;6:e12758. doi:10.1002/rth2.12758
  6. Wang SV, Rogers JR, Jin Y, et al. Stepped-wedge randomised trial to evaluate population health intervention designed to increase appropriate anticoagulation in patients with atrial fibrillation. BMJ Qual Saf. 2019;28:835-842. doi:10.1136/bmjqs-2019-009367
References
  1. Joglar JA, Chung MK, Armbruster AL, et al. 2023 ACC/AHA/ACCP/HRS guideline for the diagnosis and management of atrial fibrillation: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2024;149:e1-e156. doi:10.1161/CIR.0000000000001193
  2. Stevens SM, Woller SC, Kreuziger LB, et al. Antithrombotic therapy for VTE disease: second update of the CHEST guideline and expert panel report. Chest. 2021;160:e545-e608. doi:10.1016/j.chest.2021.07.055
  3. Institute for Safe Medication Practices (ISMP). List of high-alert medications in community/ambulatory care settings. ISMP. September 30, 2021. Accessed September 11, 2025. https://home.ecri.org/blogs/ismp-resources/high-alert-medications-in-community-ambulatory-care-settings
  4. Burnett AE, Barnes GD. A call to action for anticoagulation stewardship. Res Pract Thromb Haemost. 2022;6:e12757. doi:10.1002/rth2.12757
  5. Koolian M, Wiseman D, Mantzanis H, et al. Anticoagulation stewardship: descriptive analysis of a novel approach to appropriate anticoagulant prescription. Res Pract Thromb Haemost. 2022;6:e12758. doi:10.1002/rth2.12758
  6. Wang SV, Rogers JR, Jin Y, et al. Stepped-wedge randomised trial to evaluate population health intervention designed to increase appropriate anticoagulation in patients with atrial fibrillation. BMJ Qual Saf. 2019;28:835-842. doi:10.1136/bmjqs-2019-009367
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Comprehensive Genomic Profiles of Melanoma in Veterans Compared to Reference Databases

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Comprehensive Genomic Profiles of Melanoma in Veterans Compared to Reference Databases

The veteran population, with its unique and diverse types of exposure and military service experiences, faces distinct health factors compared with the general population. These factors can be categorized into exposures during military service and those occurring postservice. While the latter phase incorporates psychological issues that may arise while transitioning to civilian life, the service period is associated with major physical, chemical, and psychological exposures that can impact veterans’ health. Carcinogenesis related to military exposures is concerning, and different types of malignancies have been associated with military exposures.1 The 2022 introduction of the Cancer Moonshot initiative served as a breeding ground for multiple projects aimed at investigation of exposure-related carcinogenesis, prompting increased attention and efforts to linking specific exposures to specific malignancies.2

Melanoma is the deadliest skin cancer, accounting for 1.3% of all cancer deaths.3 Although it may only account for 1% to 5% of skin cancer diagnoses, its incidence in the United States’ population has been increasing.4,5 There were 97,610 estimated new cases of melanoma in 2023, according to the National Cancer Institute.6

The incidence of melanoma may be higher in the military population compared with the general population.7 Melanoma is the fourth-most common cancer diagnosed in veterans.8

Several demographic characteristics of the US military population are associated with higher melanoma incidence and poorer prognosis, including male sex, older age, and White race. Apart from sun exposure—a known risk factor for melanoma development—other factors, such as service branch, seem to contribute to risk, with the highest melanoma rates noted in the Air Force.9 According to a study by Chang et al, veterans have a higher risk of stage III (18%) or stage IV (13%) melanoma at initial diagnosis.8

Molecular testing of metastatic melanoma is currently the standard of care for guiding the use of US Food and Drug Administration-approved targeted therapies such as BRAF, MEK, and KIT inhibitors. This comparative analysis details the melanoma comprehensive genomic profiles observed at a large US Department of Veterans Affairs (VA) medical center (VAMC) and those reported in reference databases.

Methods

A query to select all metastatic melanomas sent for comprehensive genomic profiling from the Kansas City VAMC (KCVAMC), identified 35 cases from 2019 through 2023 as the study population. The health records of these patients were reviewed to collect demographic information, military service history, melanoma history, other medical, social, and family histories. The comprehensive genomic profiling reports were reviewed to collect the reported pathogenic variants, microsatellite instability (MSI) status, and tumor mutational burden (TMB) for each case.

The Catalogue of Somatic Mutations in Cancer (COSMIC) was used to identify the most commonly mutated genes in melanomas from The Cancer Genome Atlas for the general population.4,5 The literature was consulted to determine the MSI status and TMB in melanomas from The Cancer Genome Atlas for separate reference populations.6,7 The frequency of MSI-high (MSI-H) status, TMB ≥ 10 mutations/megabase (mut/Mb), and mutations in each of the 20 most commonly mutated genes was determined and compared between melanomas from The Cancer Genome Atlas and KCVAMC cases. Corresponding P  values were calculated to identify significant differences. Values were calculated for the entire sample as well as a subgroup with Agent Orange (AO) exposure. The study was approved by the KCVAMC Institutional Review Board.

Results

The mean (SD) age of study participants was 72.9 (9.4) years (range, 39-90 years). The mean (SD) duration of military service was 1654 (1421) days (about 4 years, 6 months, and 10 days). Of the 35 patients included, 22 (63%) served during the Vietnam era (November 1, 1965, to April 30, 1975) and 2 (6%) served during the Persian Gulf War era (August 2, 1990, to February 28, 1991). Seventeen veterans (49%) served in the Army, 9 in the Navy (26%), 5 in the Air Force (14%), and 4 in the Marine Corps (11%). Definitive AO exposure was noted in 13 patients (37%) (Table 1).

0825FED-AVAHO-Mel-T1

Of the 35 patients, 24 (69%) had metastatic disease and the primary site of melanoma was unknown in 14 patients (40%). One patient (Patient 32) had an intraocular melanoma. The primary site was the trunk for 11 patients (31%), the face/head for 7 patients (20%) and extremities for 3 patients (9%). Eight patients (23%) were pT3 stage (thickness > 2 mm but < 4 mm), 7 patients (20%) were pT4 stage (thickness > 4 mm), and 5 patients (14%) were pT1 (thickness ≥ 1 mm). One patient had a primary lesion at pT2 stage, and 1 had a Tis stage lesion. Three patients (9%) had a family history of melanoma in a first-degree relative.

The list of genes mutated in melanoma cells in the study population is provided in the eAppendix.10,11 Twenty-seven patients (77%) had mutations in TERT promoter, 15 (43%) in CDKN2A/B, 13 (37%) in BRAF, 11 (31%) in NF1, 9 (26%) in TP53, and 8 (23%) in NRAS (Table 2). The majority of mutations in TERT promoter were c.- 146C>T (18 of 27 patients [67%]), whereas c.-124C>T was the second-most common (8 of 27 patients [30%]). The 2 observed mutations in the 13 patients with BRAF mutations were V600E and V600K, with almost equal distribution (54% and 46%, respectively). The mean (SD) TMB was 33.2 (39) mut/Mb (range, 1-203 mut/Mb). Ten patients (29%) had a TMB < 10 mut/Mb, whereas 24 (69%) had a TMB > 10 mut/Mb. The TMB could not be determined in 1 case. The frequency of TMB-high tumors in the study population compared with frequency in the reference population is shown in Table 3.12 Only 3 patients (0.64%) in the reference population had MSI-H tumors, and the microsatellite status could not be determined in those tumors (Table 4).13 Table 5 outlines statistically significant findings.

0825FED-AVAHO-Mel-T20825FED-AVAHO-Mel-T30825FED-AVAHO-Mel-T40825FED-AVAHO-Mel-T5
Agent Orange Subgroup

AO was a tactical herbicide used by the US military, named for the orange band around the storage barrels. Possible mutagenic properties of AO have been attributed to its byproduct, dioxin. Among the most common cancers known to be associated with AO exposure are bladder and prostate carcinoma and hematopoietic neoplasms. The association between genetic alterations and AO exposure was studied in veterans with prostate cancer.14 However, to our knowledge, insufficient information is available to determine whether an association exists between exposure to herbicides used in Vietnam or the contaminant dioxin and melanoma. Because a significant proportion of this study population had a well-documented history of AO exposure (37.1%), we were able to analyze them as a subgroup and to separately compare their mutation frequency with the general population.

Results were notable for different distributions of the most frequently mutated genes in the AO subgroup compared with the whole study population. As such, TERT promoter remained the most frequently mutated gene (92%), followed by CDKN2A/B (46%); however, frequency of mutations in NF1 (46%) outnumbered those of BRAF (31%), the fourth-most common mutation. Moreover, when compared with the general melanoma population, a significantly higher frequency of mutations in the NF1 gene was observed in the AO subgroup—not the entire study population.

Discussion

Given that veterans constitute a distinct population, there is reasonable interest in investigating characteristic health issues related to military service. Skin cancer—melanoma in particular—has been researched recently in a veteran population. The differences in demographics, tumor characteristics, and melanoma- specific survival in veterans compared with the general population have already been assessed. According to Chang et al, compared with the general population, veterans are more likely to present with metastatic disease and have lower 5-year survival rates.8

Melanoma is one of the most highly mutated malignancies.15 Fortunately, the most common mutation in melanoma, BRAF V600E, is now considered therapeutically targetable. However, there are still many mutations that are less often discussed and not well understood. Regardless of therapeutic implications, all mutations observed in melanoma are worth investigating because a tumor’s genomic profile also can provide prognostic and etiologic information. Developing comprehensive descriptions of melanoma mutational profiles in specific populations is critical to advancing etiologic understanding and informing prevention strategies.

Our results demonstrate the high prevalence of TERT promoter mutations with characteristic ultraviolet signature (C>T) in the study population. This aligns with general evidence that TERT promoter mutations are common in cutaneous melanomas: 77% of this study sample and up to 86% of all mutations are TERT promoter mutations, according to Davis et al.15 TERT promoter mutations are positively associated with the initiation, invasion, and metastasis of melanoma. In certain subtypes, there is evidence that the presence of TERT promoter mutations is significantly associated with risk for extranodal metastasis and death.16 The second-most common mutated gene in the veteran study population was CDKN2A/B (43%), and the third-most mutated gene was BRAF (37%).

In chronically sun-exposed skin NF1, NRAS, and occasionally BRAF V600K mutations tend to predominate. BRAF V600E mutations, on the other hand, are rare in these melanomas.15 In our study population, the most prevalent melanoma site was the trunk (31%), which is considered a location with an intermittent pattern of sun exposure.17

This study population also had a higher frequency of CDKN2A/B mutations. High frequencies of CDKN2A/B mutations have been reported in familial melanomas, but only 1 patient with CDKN2A/B mutations had a known family history of melanoma.15 Tumors in the study population showed significantly lower frequency of mutations in ROS1, GRIN2A, KDR, KMT2C (MLL3), KMT2D (MLL2), LRP1B, PTPRT, PTCH1, FAT4, and PREX2 (P < .05).

In this study the subgroup of veterans with AO exposure differed from the whole study population. As such, CDKN2A/B mutations were observed with the same frequency as NF1 mutations (46% each); however, BRAF mutations constituted only 31% of the mutations. In addition, the frequency of NF1 mutations was significantly higher in the AO subgroup compared with the general population, but not in the whole study population.

Our sample also differed from the reference population by showing a significantly higher frequency of TMB-high (ie, ≥ 10 mut/Mb) tumors (71% vs 49%; P = .01).12 Interestingly, no significant difference in the frequency of TMB-high tumors was observed between the AO subgroup and the reference population (69% vs 49%; P = .16). There also was no statistically significant difference between the frequency of MSI-H tumors in our study population and the reference population (P = .64).13

One patient in the study population had uveal melanoma. Mutations encountered in this patient’s tumor differed from the general mutational profile of tumors. None of the 21 mutations depicted in Table 2 were present in this sample.10,11 On the other hand, those mutations frequently observed in intraocular melanomas, BAP1 and GNA11, were present in this patient.18 Additionally, this particular melanoma possessed mutations in genes RICTOR, RAD21, and PIK3R1.

Limitations

This study population consisted exclusively of male patients, introducing sex as a potential confounder in analyzing differences between the study population and the general population. As noted in a 2020 systematic review, there were no sex-based differences in the frequency of mutations in BRAF, NRAS, and KIT genes.19

Regarding NF1 mutations, only NF1-mutated acral and mucosal melanomas were more frequently observed in female patients, whereas nonacral NF1-mutated melanomas were more frequently observed in male patients.20 However, there is currently no clear evidence of whether the mutational landscapes of cutaneous melanoma differ by sex.21 Among the 11 cases with NF1-mutatation, site of origin was known in 6, 5 of which originated at nonacral sites. Although the AO subgroup also consisted entirely of male patients, this does not explain the observed increased frequency of NF1 mutations relative to the general population. No such difference was observed between the whole study population, which also consisted exclusively of male patients, and the general population. The similar frequencies of nonacral location in the whole study population (3 acral, 18 nonacral, 14 unknown site of origin) and AO subgroup (1 acral, 7 nonacral, 5 unknown site of origin) preclude location as an explanation.

The Cancer Genome Atlas Network proposed a framework for genomic classification of melanoma into 4 subtypes based on the pattern of the most prevalent significantly mutated genes: mutant BRAF, mutant RAS, mutant NF1, and triple–wild-type. According to that study, BRAF mutations were indeed associated with younger age, in contrast to the NF1-mutant genomic subtype, which was more prevalent in older individuals with higher TMB.22 This emphasizes the need to interpret the potential association of AO exposure and NF1 mutation in melanoma with caution, although additional studies are required to observe the difference between the veteran population and age-matched general population.

On the other hand, Yu et al reported no significant differences of TMB values between patients aged < 60 and ≥ 60 years with melanoma.23 In short, the observed differences we report in our limited study warrant additional investigation with larger sample sizes, sex-matched controlling, and age-matched controlling. The study was limited by its small sample size and the single location.

Conclusion

The genomic profile of melanomas in the veteran population appears to be similar to that of the general population with a few possible differences. Melanomas in the veteran study population showed a higher frequency of CDKN2A/B mutations; lower frequency of ROS1, GRIN2A, KDR, KMT2C (MLL3), KMT2D (MLL2), LRP1B, PTPRT, PTCH1, FAT4, and PREX2 mutations; and higher TMB. In addition, melanomas in the AO subgroup showed higher frequencies of NF1 mutations. The significance of such findings remains to be determined by further investigation.

References
  1. Bytnar JA, McGlynn KA, et al. Cancer incidence in the US military: An updated analysis. Cancer. 2024;130(1):96-106. doi:10.1002/cncr.34978
  2. Singer DS. A new phase of the Cancer Moonshot to end cancer as we know it. Nat Med. 2022;28(7):1345-1347. doi:10.1038/s41591-022-01881-5
  3. Koczkodaj P, Sulkowska U, Didkowska J, et al. Melanoma mortality trends in 28 European countries: a retrospective analysis for the years 1960-2020. Cancers (Basel). 2023;15(5):1514. Published 2023 Feb 28. doi:10.3390/cancers15051514
  4. Okobi OE, Abreo E, Sams NP, et al. Trends in melanoma incidence, prevalence, stage at diagnosis, and survival: an analysis of the United States Cancer Statistics (USCS) database. Cureus. 2024;16(10):e70697. doi:10.7759/cureus.70697
  5. Bartling SJ, Rivard SC, Meyerle JH. Melanoma in an active duty marine. Mil Med. 2017;182:e2034-e2039. doi:10.7205/MILMED-D-17-00127
  6. American Cancer Society. Cancer facts & figures 2023. American Cancer Society; 2023. Accessed June 20, 2025. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2023/2023-cancer-facts-and-figures.pdf
  7. Rezaei SJ, Kim J, Onyeka S, et al. Skin cancer and other dermatologic conditions among US veterans. JAMA Dermatol. 2024;160(10):1107-1111. doi:10.1001/jamadermatol.2024.3043
  8. Chang MS, La J, Trepanowski N, et al. Increased relative proportions of advanced melanoma among veterans: a comparative analysis with the Surveillance, Epidemiology, and End Results registry. J Am Acad Dermatol. 2022;87:72-79. doi:10.1016/j.jaad.2022.02.063
  9. Riemenschneider K, Liu J, Powers JG. Skin cancer in the military: a systematic review of melanoma and nonmelanoma skin cancer incidence, prevention, and screening among active duty and veteran personnel. J Am Acad Dermatol. 2018;78:1185-1192. doi:10.1016/j.jaad.2017.11.062
  10. Huang FW, Hodis E, Xu MJ, et al. Highly recurrent TERT promoter mutations in human melanoma. Science. 2013;339:957-959. doi:10.1126/science.1229259
  11. Tate JG, Bamford S, Jubb HC, et al. COSMIC: the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 2019;47:D941-D947. doi:10.1093/nar/gky1015
  12. Li M, Gao X, Wang X. Identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data. Front Immunol. 2023;14:1090838. doi:10.3389/fimmu.2023.1090838
  13. Bonneville R, Krook MA, Kautto EA, et al. Landscape of microsatellite instability across 39 cancer types. JCO Precis Oncol. 2017;2017:PO.17.00073. doi:10.1200/PO.17.00073
  14. Lui AJ, Pagadala MS, Zhong AY, et al. Agent Orange exposure and prostate cancer risk in the Million Veteran Program. medRxiv [Preprint]. 2023:2023.06.14.23291413. doi:10.1101/2023.06.14.23291413
  15. Davis EJ, Johnson DB, Sosman JA, et al. Melanoma: what do all the mutations mean? Cancer. 2018;124:3490-3499. doi:10.1002/cncr.31345
  16. Guo Y, Chen Y, Zhang L, et al. TERT promoter mutations and telomerase in melanoma. J Oncol. 2022;2022:6300329. doi:10.1155/2022/6300329
  17. Whiteman DC, Stickley M, Watt P, et al. Anatomic site, sun exposure, and risk of cutaneous melanoma. J Clin Oncol. 2006;24:3172-3177. doi:10.1200/JCO.2006.06.1325
  18. Decatur CL, Ong E, Garg N, et al. Driver mutations in uveal melanoma: associations with gene expression profile and patient outcomes. JAMA Ophthalmol. 2016;134:728-733. doi:10.1001/jamaophthalmol.2016.0903
  19. Gutiérrez-Castañeda LD, Nova JA, Tovar-Parra JD. Frequency of mutations in BRAF, NRAS, and KIT in different populations and histological subtypes of melanoma: a systemic review. Melanoma Res. 2020;30:62- 70. doi:10.1097/CMR.0000000000000628
  20. Thielmann CM, Chorti E, Matull J, et al. NF1-mutated melanomas reveal distinct clinical characteristics depending on tumour origin and respond favourably to immune checkpoint inhibitors. Eur J Cancer. 2021;159:113-124. doi:10.1016/j.ejca.2021.09.035
  21. D’Ecclesiis O, Caini S, Martinoli C, et al. Gender-dependent specificities in cutaneous melanoma predisposition, risk factors, somatic mutations, prognostic and predictive factors: a systematic review. Int J Environ Res Public Health. 2021;18:7945. doi:10.3390/ijerph18157945
  22. Cancer Genome Atlas Network. Genomic classification of cutaneous melanoma. Cell. 2015;161:1681-1696. doi:10.1016/j.cell.2015.05.044
  23. Yu Z, Wang J, Feng L, et al. Association of tumor mutational burden with age in solid tumors. J Clin Oncol. 2020;38:e13590-e13590. doi:10.1200/JCO.2020.38.15_suppl.e13590
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Daniel Mettman, MDa; Margaryta Stoieva, MDb; Maryam Abdo, MBChBb

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bUniversity of Kansas Medical Center, Kansas City

Author disclosures: The authors report no actual or potential conflicts of interest with regard to this article.

Correspondence: Margaryta Stoieva ([email protected])

Fed Pract. 2025;42(suppl 3). Published online August 15. doi:10.12788/fp.0607

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bUniversity of Kansas Medical Center, Kansas City

Author disclosures: The authors report no actual or potential conflicts of interest with regard to this article.

Correspondence: Margaryta Stoieva ([email protected])

Fed Pract. 2025;42(suppl 3). Published online August 15. doi:10.12788/fp.0607

Author and Disclosure Information

Daniel Mettman, MDa; Margaryta Stoieva, MDb; Maryam Abdo, MBChBb

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Author disclosures: The authors report no actual or potential conflicts of interest with regard to this article.

Correspondence: Margaryta Stoieva ([email protected])

Fed Pract. 2025;42(suppl 3). Published online August 15. doi:10.12788/fp.0607

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Article PDF

The veteran population, with its unique and diverse types of exposure and military service experiences, faces distinct health factors compared with the general population. These factors can be categorized into exposures during military service and those occurring postservice. While the latter phase incorporates psychological issues that may arise while transitioning to civilian life, the service period is associated with major physical, chemical, and psychological exposures that can impact veterans’ health. Carcinogenesis related to military exposures is concerning, and different types of malignancies have been associated with military exposures.1 The 2022 introduction of the Cancer Moonshot initiative served as a breeding ground for multiple projects aimed at investigation of exposure-related carcinogenesis, prompting increased attention and efforts to linking specific exposures to specific malignancies.2

Melanoma is the deadliest skin cancer, accounting for 1.3% of all cancer deaths.3 Although it may only account for 1% to 5% of skin cancer diagnoses, its incidence in the United States’ population has been increasing.4,5 There were 97,610 estimated new cases of melanoma in 2023, according to the National Cancer Institute.6

The incidence of melanoma may be higher in the military population compared with the general population.7 Melanoma is the fourth-most common cancer diagnosed in veterans.8

Several demographic characteristics of the US military population are associated with higher melanoma incidence and poorer prognosis, including male sex, older age, and White race. Apart from sun exposure—a known risk factor for melanoma development—other factors, such as service branch, seem to contribute to risk, with the highest melanoma rates noted in the Air Force.9 According to a study by Chang et al, veterans have a higher risk of stage III (18%) or stage IV (13%) melanoma at initial diagnosis.8

Molecular testing of metastatic melanoma is currently the standard of care for guiding the use of US Food and Drug Administration-approved targeted therapies such as BRAF, MEK, and KIT inhibitors. This comparative analysis details the melanoma comprehensive genomic profiles observed at a large US Department of Veterans Affairs (VA) medical center (VAMC) and those reported in reference databases.

Methods

A query to select all metastatic melanomas sent for comprehensive genomic profiling from the Kansas City VAMC (KCVAMC), identified 35 cases from 2019 through 2023 as the study population. The health records of these patients were reviewed to collect demographic information, military service history, melanoma history, other medical, social, and family histories. The comprehensive genomic profiling reports were reviewed to collect the reported pathogenic variants, microsatellite instability (MSI) status, and tumor mutational burden (TMB) for each case.

The Catalogue of Somatic Mutations in Cancer (COSMIC) was used to identify the most commonly mutated genes in melanomas from The Cancer Genome Atlas for the general population.4,5 The literature was consulted to determine the MSI status and TMB in melanomas from The Cancer Genome Atlas for separate reference populations.6,7 The frequency of MSI-high (MSI-H) status, TMB ≥ 10 mutations/megabase (mut/Mb), and mutations in each of the 20 most commonly mutated genes was determined and compared between melanomas from The Cancer Genome Atlas and KCVAMC cases. Corresponding P  values were calculated to identify significant differences. Values were calculated for the entire sample as well as a subgroup with Agent Orange (AO) exposure. The study was approved by the KCVAMC Institutional Review Board.

Results

The mean (SD) age of study participants was 72.9 (9.4) years (range, 39-90 years). The mean (SD) duration of military service was 1654 (1421) days (about 4 years, 6 months, and 10 days). Of the 35 patients included, 22 (63%) served during the Vietnam era (November 1, 1965, to April 30, 1975) and 2 (6%) served during the Persian Gulf War era (August 2, 1990, to February 28, 1991). Seventeen veterans (49%) served in the Army, 9 in the Navy (26%), 5 in the Air Force (14%), and 4 in the Marine Corps (11%). Definitive AO exposure was noted in 13 patients (37%) (Table 1).

0825FED-AVAHO-Mel-T1

Of the 35 patients, 24 (69%) had metastatic disease and the primary site of melanoma was unknown in 14 patients (40%). One patient (Patient 32) had an intraocular melanoma. The primary site was the trunk for 11 patients (31%), the face/head for 7 patients (20%) and extremities for 3 patients (9%). Eight patients (23%) were pT3 stage (thickness > 2 mm but < 4 mm), 7 patients (20%) were pT4 stage (thickness > 4 mm), and 5 patients (14%) were pT1 (thickness ≥ 1 mm). One patient had a primary lesion at pT2 stage, and 1 had a Tis stage lesion. Three patients (9%) had a family history of melanoma in a first-degree relative.

The list of genes mutated in melanoma cells in the study population is provided in the eAppendix.10,11 Twenty-seven patients (77%) had mutations in TERT promoter, 15 (43%) in CDKN2A/B, 13 (37%) in BRAF, 11 (31%) in NF1, 9 (26%) in TP53, and 8 (23%) in NRAS (Table 2). The majority of mutations in TERT promoter were c.- 146C>T (18 of 27 patients [67%]), whereas c.-124C>T was the second-most common (8 of 27 patients [30%]). The 2 observed mutations in the 13 patients with BRAF mutations were V600E and V600K, with almost equal distribution (54% and 46%, respectively). The mean (SD) TMB was 33.2 (39) mut/Mb (range, 1-203 mut/Mb). Ten patients (29%) had a TMB < 10 mut/Mb, whereas 24 (69%) had a TMB > 10 mut/Mb. The TMB could not be determined in 1 case. The frequency of TMB-high tumors in the study population compared with frequency in the reference population is shown in Table 3.12 Only 3 patients (0.64%) in the reference population had MSI-H tumors, and the microsatellite status could not be determined in those tumors (Table 4).13 Table 5 outlines statistically significant findings.

0825FED-AVAHO-Mel-T20825FED-AVAHO-Mel-T30825FED-AVAHO-Mel-T40825FED-AVAHO-Mel-T5
Agent Orange Subgroup

AO was a tactical herbicide used by the US military, named for the orange band around the storage barrels. Possible mutagenic properties of AO have been attributed to its byproduct, dioxin. Among the most common cancers known to be associated with AO exposure are bladder and prostate carcinoma and hematopoietic neoplasms. The association between genetic alterations and AO exposure was studied in veterans with prostate cancer.14 However, to our knowledge, insufficient information is available to determine whether an association exists between exposure to herbicides used in Vietnam or the contaminant dioxin and melanoma. Because a significant proportion of this study population had a well-documented history of AO exposure (37.1%), we were able to analyze them as a subgroup and to separately compare their mutation frequency with the general population.

Results were notable for different distributions of the most frequently mutated genes in the AO subgroup compared with the whole study population. As such, TERT promoter remained the most frequently mutated gene (92%), followed by CDKN2A/B (46%); however, frequency of mutations in NF1 (46%) outnumbered those of BRAF (31%), the fourth-most common mutation. Moreover, when compared with the general melanoma population, a significantly higher frequency of mutations in the NF1 gene was observed in the AO subgroup—not the entire study population.

Discussion

Given that veterans constitute a distinct population, there is reasonable interest in investigating characteristic health issues related to military service. Skin cancer—melanoma in particular—has been researched recently in a veteran population. The differences in demographics, tumor characteristics, and melanoma- specific survival in veterans compared with the general population have already been assessed. According to Chang et al, compared with the general population, veterans are more likely to present with metastatic disease and have lower 5-year survival rates.8

Melanoma is one of the most highly mutated malignancies.15 Fortunately, the most common mutation in melanoma, BRAF V600E, is now considered therapeutically targetable. However, there are still many mutations that are less often discussed and not well understood. Regardless of therapeutic implications, all mutations observed in melanoma are worth investigating because a tumor’s genomic profile also can provide prognostic and etiologic information. Developing comprehensive descriptions of melanoma mutational profiles in specific populations is critical to advancing etiologic understanding and informing prevention strategies.

Our results demonstrate the high prevalence of TERT promoter mutations with characteristic ultraviolet signature (C>T) in the study population. This aligns with general evidence that TERT promoter mutations are common in cutaneous melanomas: 77% of this study sample and up to 86% of all mutations are TERT promoter mutations, according to Davis et al.15 TERT promoter mutations are positively associated with the initiation, invasion, and metastasis of melanoma. In certain subtypes, there is evidence that the presence of TERT promoter mutations is significantly associated with risk for extranodal metastasis and death.16 The second-most common mutated gene in the veteran study population was CDKN2A/B (43%), and the third-most mutated gene was BRAF (37%).

In chronically sun-exposed skin NF1, NRAS, and occasionally BRAF V600K mutations tend to predominate. BRAF V600E mutations, on the other hand, are rare in these melanomas.15 In our study population, the most prevalent melanoma site was the trunk (31%), which is considered a location with an intermittent pattern of sun exposure.17

This study population also had a higher frequency of CDKN2A/B mutations. High frequencies of CDKN2A/B mutations have been reported in familial melanomas, but only 1 patient with CDKN2A/B mutations had a known family history of melanoma.15 Tumors in the study population showed significantly lower frequency of mutations in ROS1, GRIN2A, KDR, KMT2C (MLL3), KMT2D (MLL2), LRP1B, PTPRT, PTCH1, FAT4, and PREX2 (P < .05).

In this study the subgroup of veterans with AO exposure differed from the whole study population. As such, CDKN2A/B mutations were observed with the same frequency as NF1 mutations (46% each); however, BRAF mutations constituted only 31% of the mutations. In addition, the frequency of NF1 mutations was significantly higher in the AO subgroup compared with the general population, but not in the whole study population.

Our sample also differed from the reference population by showing a significantly higher frequency of TMB-high (ie, ≥ 10 mut/Mb) tumors (71% vs 49%; P = .01).12 Interestingly, no significant difference in the frequency of TMB-high tumors was observed between the AO subgroup and the reference population (69% vs 49%; P = .16). There also was no statistically significant difference between the frequency of MSI-H tumors in our study population and the reference population (P = .64).13

One patient in the study population had uveal melanoma. Mutations encountered in this patient’s tumor differed from the general mutational profile of tumors. None of the 21 mutations depicted in Table 2 were present in this sample.10,11 On the other hand, those mutations frequently observed in intraocular melanomas, BAP1 and GNA11, were present in this patient.18 Additionally, this particular melanoma possessed mutations in genes RICTOR, RAD21, and PIK3R1.

Limitations

This study population consisted exclusively of male patients, introducing sex as a potential confounder in analyzing differences between the study population and the general population. As noted in a 2020 systematic review, there were no sex-based differences in the frequency of mutations in BRAF, NRAS, and KIT genes.19

Regarding NF1 mutations, only NF1-mutated acral and mucosal melanomas were more frequently observed in female patients, whereas nonacral NF1-mutated melanomas were more frequently observed in male patients.20 However, there is currently no clear evidence of whether the mutational landscapes of cutaneous melanoma differ by sex.21 Among the 11 cases with NF1-mutatation, site of origin was known in 6, 5 of which originated at nonacral sites. Although the AO subgroup also consisted entirely of male patients, this does not explain the observed increased frequency of NF1 mutations relative to the general population. No such difference was observed between the whole study population, which also consisted exclusively of male patients, and the general population. The similar frequencies of nonacral location in the whole study population (3 acral, 18 nonacral, 14 unknown site of origin) and AO subgroup (1 acral, 7 nonacral, 5 unknown site of origin) preclude location as an explanation.

The Cancer Genome Atlas Network proposed a framework for genomic classification of melanoma into 4 subtypes based on the pattern of the most prevalent significantly mutated genes: mutant BRAF, mutant RAS, mutant NF1, and triple–wild-type. According to that study, BRAF mutations were indeed associated with younger age, in contrast to the NF1-mutant genomic subtype, which was more prevalent in older individuals with higher TMB.22 This emphasizes the need to interpret the potential association of AO exposure and NF1 mutation in melanoma with caution, although additional studies are required to observe the difference between the veteran population and age-matched general population.

On the other hand, Yu et al reported no significant differences of TMB values between patients aged < 60 and ≥ 60 years with melanoma.23 In short, the observed differences we report in our limited study warrant additional investigation with larger sample sizes, sex-matched controlling, and age-matched controlling. The study was limited by its small sample size and the single location.

Conclusion

The genomic profile of melanomas in the veteran population appears to be similar to that of the general population with a few possible differences. Melanomas in the veteran study population showed a higher frequency of CDKN2A/B mutations; lower frequency of ROS1, GRIN2A, KDR, KMT2C (MLL3), KMT2D (MLL2), LRP1B, PTPRT, PTCH1, FAT4, and PREX2 mutations; and higher TMB. In addition, melanomas in the AO subgroup showed higher frequencies of NF1 mutations. The significance of such findings remains to be determined by further investigation.

The veteran population, with its unique and diverse types of exposure and military service experiences, faces distinct health factors compared with the general population. These factors can be categorized into exposures during military service and those occurring postservice. While the latter phase incorporates psychological issues that may arise while transitioning to civilian life, the service period is associated with major physical, chemical, and psychological exposures that can impact veterans’ health. Carcinogenesis related to military exposures is concerning, and different types of malignancies have been associated with military exposures.1 The 2022 introduction of the Cancer Moonshot initiative served as a breeding ground for multiple projects aimed at investigation of exposure-related carcinogenesis, prompting increased attention and efforts to linking specific exposures to specific malignancies.2

Melanoma is the deadliest skin cancer, accounting for 1.3% of all cancer deaths.3 Although it may only account for 1% to 5% of skin cancer diagnoses, its incidence in the United States’ population has been increasing.4,5 There were 97,610 estimated new cases of melanoma in 2023, according to the National Cancer Institute.6

The incidence of melanoma may be higher in the military population compared with the general population.7 Melanoma is the fourth-most common cancer diagnosed in veterans.8

Several demographic characteristics of the US military population are associated with higher melanoma incidence and poorer prognosis, including male sex, older age, and White race. Apart from sun exposure—a known risk factor for melanoma development—other factors, such as service branch, seem to contribute to risk, with the highest melanoma rates noted in the Air Force.9 According to a study by Chang et al, veterans have a higher risk of stage III (18%) or stage IV (13%) melanoma at initial diagnosis.8

Molecular testing of metastatic melanoma is currently the standard of care for guiding the use of US Food and Drug Administration-approved targeted therapies such as BRAF, MEK, and KIT inhibitors. This comparative analysis details the melanoma comprehensive genomic profiles observed at a large US Department of Veterans Affairs (VA) medical center (VAMC) and those reported in reference databases.

Methods

A query to select all metastatic melanomas sent for comprehensive genomic profiling from the Kansas City VAMC (KCVAMC), identified 35 cases from 2019 through 2023 as the study population. The health records of these patients were reviewed to collect demographic information, military service history, melanoma history, other medical, social, and family histories. The comprehensive genomic profiling reports were reviewed to collect the reported pathogenic variants, microsatellite instability (MSI) status, and tumor mutational burden (TMB) for each case.

The Catalogue of Somatic Mutations in Cancer (COSMIC) was used to identify the most commonly mutated genes in melanomas from The Cancer Genome Atlas for the general population.4,5 The literature was consulted to determine the MSI status and TMB in melanomas from The Cancer Genome Atlas for separate reference populations.6,7 The frequency of MSI-high (MSI-H) status, TMB ≥ 10 mutations/megabase (mut/Mb), and mutations in each of the 20 most commonly mutated genes was determined and compared between melanomas from The Cancer Genome Atlas and KCVAMC cases. Corresponding P  values were calculated to identify significant differences. Values were calculated for the entire sample as well as a subgroup with Agent Orange (AO) exposure. The study was approved by the KCVAMC Institutional Review Board.

Results

The mean (SD) age of study participants was 72.9 (9.4) years (range, 39-90 years). The mean (SD) duration of military service was 1654 (1421) days (about 4 years, 6 months, and 10 days). Of the 35 patients included, 22 (63%) served during the Vietnam era (November 1, 1965, to April 30, 1975) and 2 (6%) served during the Persian Gulf War era (August 2, 1990, to February 28, 1991). Seventeen veterans (49%) served in the Army, 9 in the Navy (26%), 5 in the Air Force (14%), and 4 in the Marine Corps (11%). Definitive AO exposure was noted in 13 patients (37%) (Table 1).

0825FED-AVAHO-Mel-T1

Of the 35 patients, 24 (69%) had metastatic disease and the primary site of melanoma was unknown in 14 patients (40%). One patient (Patient 32) had an intraocular melanoma. The primary site was the trunk for 11 patients (31%), the face/head for 7 patients (20%) and extremities for 3 patients (9%). Eight patients (23%) were pT3 stage (thickness > 2 mm but < 4 mm), 7 patients (20%) were pT4 stage (thickness > 4 mm), and 5 patients (14%) were pT1 (thickness ≥ 1 mm). One patient had a primary lesion at pT2 stage, and 1 had a Tis stage lesion. Three patients (9%) had a family history of melanoma in a first-degree relative.

The list of genes mutated in melanoma cells in the study population is provided in the eAppendix.10,11 Twenty-seven patients (77%) had mutations in TERT promoter, 15 (43%) in CDKN2A/B, 13 (37%) in BRAF, 11 (31%) in NF1, 9 (26%) in TP53, and 8 (23%) in NRAS (Table 2). The majority of mutations in TERT promoter were c.- 146C>T (18 of 27 patients [67%]), whereas c.-124C>T was the second-most common (8 of 27 patients [30%]). The 2 observed mutations in the 13 patients with BRAF mutations were V600E and V600K, with almost equal distribution (54% and 46%, respectively). The mean (SD) TMB was 33.2 (39) mut/Mb (range, 1-203 mut/Mb). Ten patients (29%) had a TMB < 10 mut/Mb, whereas 24 (69%) had a TMB > 10 mut/Mb. The TMB could not be determined in 1 case. The frequency of TMB-high tumors in the study population compared with frequency in the reference population is shown in Table 3.12 Only 3 patients (0.64%) in the reference population had MSI-H tumors, and the microsatellite status could not be determined in those tumors (Table 4).13 Table 5 outlines statistically significant findings.

0825FED-AVAHO-Mel-T20825FED-AVAHO-Mel-T30825FED-AVAHO-Mel-T40825FED-AVAHO-Mel-T5
Agent Orange Subgroup

AO was a tactical herbicide used by the US military, named for the orange band around the storage barrels. Possible mutagenic properties of AO have been attributed to its byproduct, dioxin. Among the most common cancers known to be associated with AO exposure are bladder and prostate carcinoma and hematopoietic neoplasms. The association between genetic alterations and AO exposure was studied in veterans with prostate cancer.14 However, to our knowledge, insufficient information is available to determine whether an association exists between exposure to herbicides used in Vietnam or the contaminant dioxin and melanoma. Because a significant proportion of this study population had a well-documented history of AO exposure (37.1%), we were able to analyze them as a subgroup and to separately compare their mutation frequency with the general population.

Results were notable for different distributions of the most frequently mutated genes in the AO subgroup compared with the whole study population. As such, TERT promoter remained the most frequently mutated gene (92%), followed by CDKN2A/B (46%); however, frequency of mutations in NF1 (46%) outnumbered those of BRAF (31%), the fourth-most common mutation. Moreover, when compared with the general melanoma population, a significantly higher frequency of mutations in the NF1 gene was observed in the AO subgroup—not the entire study population.

Discussion

Given that veterans constitute a distinct population, there is reasonable interest in investigating characteristic health issues related to military service. Skin cancer—melanoma in particular—has been researched recently in a veteran population. The differences in demographics, tumor characteristics, and melanoma- specific survival in veterans compared with the general population have already been assessed. According to Chang et al, compared with the general population, veterans are more likely to present with metastatic disease and have lower 5-year survival rates.8

Melanoma is one of the most highly mutated malignancies.15 Fortunately, the most common mutation in melanoma, BRAF V600E, is now considered therapeutically targetable. However, there are still many mutations that are less often discussed and not well understood. Regardless of therapeutic implications, all mutations observed in melanoma are worth investigating because a tumor’s genomic profile also can provide prognostic and etiologic information. Developing comprehensive descriptions of melanoma mutational profiles in specific populations is critical to advancing etiologic understanding and informing prevention strategies.

Our results demonstrate the high prevalence of TERT promoter mutations with characteristic ultraviolet signature (C>T) in the study population. This aligns with general evidence that TERT promoter mutations are common in cutaneous melanomas: 77% of this study sample and up to 86% of all mutations are TERT promoter mutations, according to Davis et al.15 TERT promoter mutations are positively associated with the initiation, invasion, and metastasis of melanoma. In certain subtypes, there is evidence that the presence of TERT promoter mutations is significantly associated with risk for extranodal metastasis and death.16 The second-most common mutated gene in the veteran study population was CDKN2A/B (43%), and the third-most mutated gene was BRAF (37%).

In chronically sun-exposed skin NF1, NRAS, and occasionally BRAF V600K mutations tend to predominate. BRAF V600E mutations, on the other hand, are rare in these melanomas.15 In our study population, the most prevalent melanoma site was the trunk (31%), which is considered a location with an intermittent pattern of sun exposure.17

This study population also had a higher frequency of CDKN2A/B mutations. High frequencies of CDKN2A/B mutations have been reported in familial melanomas, but only 1 patient with CDKN2A/B mutations had a known family history of melanoma.15 Tumors in the study population showed significantly lower frequency of mutations in ROS1, GRIN2A, KDR, KMT2C (MLL3), KMT2D (MLL2), LRP1B, PTPRT, PTCH1, FAT4, and PREX2 (P < .05).

In this study the subgroup of veterans with AO exposure differed from the whole study population. As such, CDKN2A/B mutations were observed with the same frequency as NF1 mutations (46% each); however, BRAF mutations constituted only 31% of the mutations. In addition, the frequency of NF1 mutations was significantly higher in the AO subgroup compared with the general population, but not in the whole study population.

Our sample also differed from the reference population by showing a significantly higher frequency of TMB-high (ie, ≥ 10 mut/Mb) tumors (71% vs 49%; P = .01).12 Interestingly, no significant difference in the frequency of TMB-high tumors was observed between the AO subgroup and the reference population (69% vs 49%; P = .16). There also was no statistically significant difference between the frequency of MSI-H tumors in our study population and the reference population (P = .64).13

One patient in the study population had uveal melanoma. Mutations encountered in this patient’s tumor differed from the general mutational profile of tumors. None of the 21 mutations depicted in Table 2 were present in this sample.10,11 On the other hand, those mutations frequently observed in intraocular melanomas, BAP1 and GNA11, were present in this patient.18 Additionally, this particular melanoma possessed mutations in genes RICTOR, RAD21, and PIK3R1.

Limitations

This study population consisted exclusively of male patients, introducing sex as a potential confounder in analyzing differences between the study population and the general population. As noted in a 2020 systematic review, there were no sex-based differences in the frequency of mutations in BRAF, NRAS, and KIT genes.19

Regarding NF1 mutations, only NF1-mutated acral and mucosal melanomas were more frequently observed in female patients, whereas nonacral NF1-mutated melanomas were more frequently observed in male patients.20 However, there is currently no clear evidence of whether the mutational landscapes of cutaneous melanoma differ by sex.21 Among the 11 cases with NF1-mutatation, site of origin was known in 6, 5 of which originated at nonacral sites. Although the AO subgroup also consisted entirely of male patients, this does not explain the observed increased frequency of NF1 mutations relative to the general population. No such difference was observed between the whole study population, which also consisted exclusively of male patients, and the general population. The similar frequencies of nonacral location in the whole study population (3 acral, 18 nonacral, 14 unknown site of origin) and AO subgroup (1 acral, 7 nonacral, 5 unknown site of origin) preclude location as an explanation.

The Cancer Genome Atlas Network proposed a framework for genomic classification of melanoma into 4 subtypes based on the pattern of the most prevalent significantly mutated genes: mutant BRAF, mutant RAS, mutant NF1, and triple–wild-type. According to that study, BRAF mutations were indeed associated with younger age, in contrast to the NF1-mutant genomic subtype, which was more prevalent in older individuals with higher TMB.22 This emphasizes the need to interpret the potential association of AO exposure and NF1 mutation in melanoma with caution, although additional studies are required to observe the difference between the veteran population and age-matched general population.

On the other hand, Yu et al reported no significant differences of TMB values between patients aged < 60 and ≥ 60 years with melanoma.23 In short, the observed differences we report in our limited study warrant additional investigation with larger sample sizes, sex-matched controlling, and age-matched controlling. The study was limited by its small sample size and the single location.

Conclusion

The genomic profile of melanomas in the veteran population appears to be similar to that of the general population with a few possible differences. Melanomas in the veteran study population showed a higher frequency of CDKN2A/B mutations; lower frequency of ROS1, GRIN2A, KDR, KMT2C (MLL3), KMT2D (MLL2), LRP1B, PTPRT, PTCH1, FAT4, and PREX2 mutations; and higher TMB. In addition, melanomas in the AO subgroup showed higher frequencies of NF1 mutations. The significance of such findings remains to be determined by further investigation.

References
  1. Bytnar JA, McGlynn KA, et al. Cancer incidence in the US military: An updated analysis. Cancer. 2024;130(1):96-106. doi:10.1002/cncr.34978
  2. Singer DS. A new phase of the Cancer Moonshot to end cancer as we know it. Nat Med. 2022;28(7):1345-1347. doi:10.1038/s41591-022-01881-5
  3. Koczkodaj P, Sulkowska U, Didkowska J, et al. Melanoma mortality trends in 28 European countries: a retrospective analysis for the years 1960-2020. Cancers (Basel). 2023;15(5):1514. Published 2023 Feb 28. doi:10.3390/cancers15051514
  4. Okobi OE, Abreo E, Sams NP, et al. Trends in melanoma incidence, prevalence, stage at diagnosis, and survival: an analysis of the United States Cancer Statistics (USCS) database. Cureus. 2024;16(10):e70697. doi:10.7759/cureus.70697
  5. Bartling SJ, Rivard SC, Meyerle JH. Melanoma in an active duty marine. Mil Med. 2017;182:e2034-e2039. doi:10.7205/MILMED-D-17-00127
  6. American Cancer Society. Cancer facts & figures 2023. American Cancer Society; 2023. Accessed June 20, 2025. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2023/2023-cancer-facts-and-figures.pdf
  7. Rezaei SJ, Kim J, Onyeka S, et al. Skin cancer and other dermatologic conditions among US veterans. JAMA Dermatol. 2024;160(10):1107-1111. doi:10.1001/jamadermatol.2024.3043
  8. Chang MS, La J, Trepanowski N, et al. Increased relative proportions of advanced melanoma among veterans: a comparative analysis with the Surveillance, Epidemiology, and End Results registry. J Am Acad Dermatol. 2022;87:72-79. doi:10.1016/j.jaad.2022.02.063
  9. Riemenschneider K, Liu J, Powers JG. Skin cancer in the military: a systematic review of melanoma and nonmelanoma skin cancer incidence, prevention, and screening among active duty and veteran personnel. J Am Acad Dermatol. 2018;78:1185-1192. doi:10.1016/j.jaad.2017.11.062
  10. Huang FW, Hodis E, Xu MJ, et al. Highly recurrent TERT promoter mutations in human melanoma. Science. 2013;339:957-959. doi:10.1126/science.1229259
  11. Tate JG, Bamford S, Jubb HC, et al. COSMIC: the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 2019;47:D941-D947. doi:10.1093/nar/gky1015
  12. Li M, Gao X, Wang X. Identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data. Front Immunol. 2023;14:1090838. doi:10.3389/fimmu.2023.1090838
  13. Bonneville R, Krook MA, Kautto EA, et al. Landscape of microsatellite instability across 39 cancer types. JCO Precis Oncol. 2017;2017:PO.17.00073. doi:10.1200/PO.17.00073
  14. Lui AJ, Pagadala MS, Zhong AY, et al. Agent Orange exposure and prostate cancer risk in the Million Veteran Program. medRxiv [Preprint]. 2023:2023.06.14.23291413. doi:10.1101/2023.06.14.23291413
  15. Davis EJ, Johnson DB, Sosman JA, et al. Melanoma: what do all the mutations mean? Cancer. 2018;124:3490-3499. doi:10.1002/cncr.31345
  16. Guo Y, Chen Y, Zhang L, et al. TERT promoter mutations and telomerase in melanoma. J Oncol. 2022;2022:6300329. doi:10.1155/2022/6300329
  17. Whiteman DC, Stickley M, Watt P, et al. Anatomic site, sun exposure, and risk of cutaneous melanoma. J Clin Oncol. 2006;24:3172-3177. doi:10.1200/JCO.2006.06.1325
  18. Decatur CL, Ong E, Garg N, et al. Driver mutations in uveal melanoma: associations with gene expression profile and patient outcomes. JAMA Ophthalmol. 2016;134:728-733. doi:10.1001/jamaophthalmol.2016.0903
  19. Gutiérrez-Castañeda LD, Nova JA, Tovar-Parra JD. Frequency of mutations in BRAF, NRAS, and KIT in different populations and histological subtypes of melanoma: a systemic review. Melanoma Res. 2020;30:62- 70. doi:10.1097/CMR.0000000000000628
  20. Thielmann CM, Chorti E, Matull J, et al. NF1-mutated melanomas reveal distinct clinical characteristics depending on tumour origin and respond favourably to immune checkpoint inhibitors. Eur J Cancer. 2021;159:113-124. doi:10.1016/j.ejca.2021.09.035
  21. D’Ecclesiis O, Caini S, Martinoli C, et al. Gender-dependent specificities in cutaneous melanoma predisposition, risk factors, somatic mutations, prognostic and predictive factors: a systematic review. Int J Environ Res Public Health. 2021;18:7945. doi:10.3390/ijerph18157945
  22. Cancer Genome Atlas Network. Genomic classification of cutaneous melanoma. Cell. 2015;161:1681-1696. doi:10.1016/j.cell.2015.05.044
  23. Yu Z, Wang J, Feng L, et al. Association of tumor mutational burden with age in solid tumors. J Clin Oncol. 2020;38:e13590-e13590. doi:10.1200/JCO.2020.38.15_suppl.e13590
References
  1. Bytnar JA, McGlynn KA, et al. Cancer incidence in the US military: An updated analysis. Cancer. 2024;130(1):96-106. doi:10.1002/cncr.34978
  2. Singer DS. A new phase of the Cancer Moonshot to end cancer as we know it. Nat Med. 2022;28(7):1345-1347. doi:10.1038/s41591-022-01881-5
  3. Koczkodaj P, Sulkowska U, Didkowska J, et al. Melanoma mortality trends in 28 European countries: a retrospective analysis for the years 1960-2020. Cancers (Basel). 2023;15(5):1514. Published 2023 Feb 28. doi:10.3390/cancers15051514
  4. Okobi OE, Abreo E, Sams NP, et al. Trends in melanoma incidence, prevalence, stage at diagnosis, and survival: an analysis of the United States Cancer Statistics (USCS) database. Cureus. 2024;16(10):e70697. doi:10.7759/cureus.70697
  5. Bartling SJ, Rivard SC, Meyerle JH. Melanoma in an active duty marine. Mil Med. 2017;182:e2034-e2039. doi:10.7205/MILMED-D-17-00127
  6. American Cancer Society. Cancer facts & figures 2023. American Cancer Society; 2023. Accessed June 20, 2025. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2023/2023-cancer-facts-and-figures.pdf
  7. Rezaei SJ, Kim J, Onyeka S, et al. Skin cancer and other dermatologic conditions among US veterans. JAMA Dermatol. 2024;160(10):1107-1111. doi:10.1001/jamadermatol.2024.3043
  8. Chang MS, La J, Trepanowski N, et al. Increased relative proportions of advanced melanoma among veterans: a comparative analysis with the Surveillance, Epidemiology, and End Results registry. J Am Acad Dermatol. 2022;87:72-79. doi:10.1016/j.jaad.2022.02.063
  9. Riemenschneider K, Liu J, Powers JG. Skin cancer in the military: a systematic review of melanoma and nonmelanoma skin cancer incidence, prevention, and screening among active duty and veteran personnel. J Am Acad Dermatol. 2018;78:1185-1192. doi:10.1016/j.jaad.2017.11.062
  10. Huang FW, Hodis E, Xu MJ, et al. Highly recurrent TERT promoter mutations in human melanoma. Science. 2013;339:957-959. doi:10.1126/science.1229259
  11. Tate JG, Bamford S, Jubb HC, et al. COSMIC: the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 2019;47:D941-D947. doi:10.1093/nar/gky1015
  12. Li M, Gao X, Wang X. Identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data. Front Immunol. 2023;14:1090838. doi:10.3389/fimmu.2023.1090838
  13. Bonneville R, Krook MA, Kautto EA, et al. Landscape of microsatellite instability across 39 cancer types. JCO Precis Oncol. 2017;2017:PO.17.00073. doi:10.1200/PO.17.00073
  14. Lui AJ, Pagadala MS, Zhong AY, et al. Agent Orange exposure and prostate cancer risk in the Million Veteran Program. medRxiv [Preprint]. 2023:2023.06.14.23291413. doi:10.1101/2023.06.14.23291413
  15. Davis EJ, Johnson DB, Sosman JA, et al. Melanoma: what do all the mutations mean? Cancer. 2018;124:3490-3499. doi:10.1002/cncr.31345
  16. Guo Y, Chen Y, Zhang L, et al. TERT promoter mutations and telomerase in melanoma. J Oncol. 2022;2022:6300329. doi:10.1155/2022/6300329
  17. Whiteman DC, Stickley M, Watt P, et al. Anatomic site, sun exposure, and risk of cutaneous melanoma. J Clin Oncol. 2006;24:3172-3177. doi:10.1200/JCO.2006.06.1325
  18. Decatur CL, Ong E, Garg N, et al. Driver mutations in uveal melanoma: associations with gene expression profile and patient outcomes. JAMA Ophthalmol. 2016;134:728-733. doi:10.1001/jamaophthalmol.2016.0903
  19. Gutiérrez-Castañeda LD, Nova JA, Tovar-Parra JD. Frequency of mutations in BRAF, NRAS, and KIT in different populations and histological subtypes of melanoma: a systemic review. Melanoma Res. 2020;30:62- 70. doi:10.1097/CMR.0000000000000628
  20. Thielmann CM, Chorti E, Matull J, et al. NF1-mutated melanomas reveal distinct clinical characteristics depending on tumour origin and respond favourably to immune checkpoint inhibitors. Eur J Cancer. 2021;159:113-124. doi:10.1016/j.ejca.2021.09.035
  21. D’Ecclesiis O, Caini S, Martinoli C, et al. Gender-dependent specificities in cutaneous melanoma predisposition, risk factors, somatic mutations, prognostic and predictive factors: a systematic review. Int J Environ Res Public Health. 2021;18:7945. doi:10.3390/ijerph18157945
  22. Cancer Genome Atlas Network. Genomic classification of cutaneous melanoma. Cell. 2015;161:1681-1696. doi:10.1016/j.cell.2015.05.044
  23. Yu Z, Wang J, Feng L, et al. Association of tumor mutational burden with age in solid tumors. J Clin Oncol. 2020;38:e13590-e13590. doi:10.1200/JCO.2020.38.15_suppl.e13590
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Colorectal Cancer Characteristics and Mortality From Propensity Score-Matched Cohorts of Urban and Rural Veterans

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Colorectal Cancer Characteristics and Mortality From Propensity Score-Matched Cohorts of Urban and Rural Veterans

Colorectal cancer (CRC) is the second-leading cause of cancer-related deaths in the United States, with an estimated 52,550 deaths in 2023.1 However, the disease burden varies among different segments of the population.2 While both CRC incidence and mortality have been decreasing due to screening and advances in treatment, there are disparities in incidence and mortality across the sociodemographic spectrum including race, ethnicity, education, and income.1-4 While CRC incidence is decreasing for older adults, it is increasing among those aged < 55 years.5 The incidence of CRC in adults aged 40 to 54 years has increased by 0.5% to 1.3% annually since the mid-1990s.6 The US Preventive Services Task Force now recommends starting CRC screening at age 45 years for asymptomatic adults with average risk.7

Disparities also exist across geographical boundaries and living environment. Rural Americans faces additional challenges in health and lifestyle that can affect CRC outcomes. Compared to their urban counterparts, rural residents are more likely to be older, have lower levels of education, higher levels of poverty, lack health insurance, and less access to health care practitioners (HCPs).8-10 Geographic proximity, defined as travel time or physical distance to a health facility, has been recognized as a predictor of inferior outcomes.11 These aspects of rural living may pose challenges for accessing care for CRC screening and treatment.11-13 National and local studies have shown disparities in CRC screening rates, incidence, and mortality between rural and urban populations.14-16

It is unclear whether rural/urban disparities persist under the Veterans Health Administration (VHA) health care delivery model. This study examined differences in baseline characteristics and mortality between rural and urban veterans newly diagnosed with CRC. We also focused on a subpopulation aged ≤ 45 years.

Methods

This study extracted national data from the US Department of Veterans Affairs (VA) Corporate Data Warehouse (CDW) hosted in the VA Informatics and Computing Infrastructure (VINCI) environment. VINCI is an initiative to improve access to VA data and facilitate the analysis of these data while ensuring veterans’ privacy and data security.17 CDW is the VHA business intelligence information repository, which extracts data from clinical and nonclinical sources following prescribed and validated protocols. Data extracted included demographics, diagnosis, and procedure codes for both inpatient and outpatient encounters, vital signs, and vital status. This study used data previously extracted from a national cohort of veterans that encompassed all patients who received a group of commonly prescribed medications, such as statins, proton pump inhibitors, histamine-2 blockers, acetaminophen-containing products, and hydrocortisone-containing skin applications. This cohort encompassed 8,648,754 veterans, from whom 2,460,727 had encounters during fiscal years (FY) 2016 to 2021 (study period). The cohort was used to ensure that subjects were VHA patients, allowing them to adequately capture their clinical profiles.

Patients were identified as rural or urban based on their residence address at the date of their first diagnosis of CRC. The Geospatial Service Support Center (GSSC) aggregates and updates veterans’ residence address records for all enrolled veterans from the National Change of Address database. The data contain 1 record per enrollee. GSSC Geocoded Enrollee File contains enrollee addresses and their rurality indicators, categorized as urban, rural, or highly rural.18 Rurality is defined by the Rural Urban Commuting Area (RUCA) categories developed by the Department of Agriculture and the Health Resources and Services Administration of the US Department of Health and Human Services.19 Urban areas had RUCA codes of 1.0 to 1.1, and highly rural areas had RUCA scores of 10.0. All other areas were classified as rural. Since the proportion of veterans from highly rural areas was small, we included residents from highly rural areas in the rural residents’ group.

Inclusion and Exclusion Criteria

All veterans newly diagnosed with CRC from FY 2016 to 2021 were included. We used the ninth and tenth clinical modification revisions of the International Classification of Diseases (ICD-9-CM and ICD-10-CM) to define CRC diagnosis (Supplemental materials).4,20 To ensure that patients were newly diagnosed with CRC, this study excluded patients with a previous ICD-9-CM code for CRC diagnosis since FY 2003.

Comorbidities were identified using diagnosis and procedure codes from inpatient and outpatient encounters, which were used to calculate the Charlson Comorbidity Index (CCI) at the time of CRC diagnosis using the weighted method described by Schneeweiss et al.21 We defined CRC high-risk conditions and CRC screening tests, including flexible sigmoidoscopy and stool tests, as described in previous studies (Supplemental materials).20

The main outcome was total mortality. The date of death was extracted from the VHA Death Ascertainment File, which contains mortality data from the Master Person Index file in CDW and the Social Security Administration Death Master File. We used the date of death from any cause, as cause of death was not available.

A propensity score (PS) was created to match rural (including highly rural) and urban residents at a ratio of 1:1. Using a standard procedure described in prior publications, multivariable logistic regression used all baseline characteristics to estimate the PS and perform nearest-number matching without replacement.22,23 A caliper of 0.01 maximized the matched cohort size and achieved balance (Supplemental materials). We then examined the balance of baseline characteristics between PS-matched groups.

Analyses

Cox proportional hazards regression analysis estimated the hazard ratio (HR) of death in rural residents compared to urban residents in the PS-matched cohort. The outcome event was the date of death during the study’s follow-up period (defined as period from first CRC diagnosis to death or study end), with censoring at the study’s end date (September 30, 2021). The proportional hazards assumption was assessed by inspecting the Kaplan-Meier curves. Multiple analyses examined the HR of total mortality in the PS-matched cohort, stratified by sex, race, and ethnicity. We also examined the HR of total mortality stratified by duration of follow-up.

Another PS-matching analysis among veterans aged ≤ 45 years was performed using the same techniques described earlier in this article. We performed a Cox proportional hazards regression analysis to compare mortality in PS-matched urban and rural veterans aged ≤ 45 years. The HR of death in all veterans aged ≤ 45 years (before PS-matching) was estimated using Cox proportional hazard regression analysis, adjusting for PS.

Dichotomous variables were compared using X2 tests and continuous variables were compared using t tests. Baseline characteristics with missing values were converted into categorical variables and the proportion of subjects with missing values was equalized between treatment groups after PS-matching. For subgroup analysis, we examined the HR of total mortality in each subgroup using separate Cox proportional hazards regression models similar to the primary analysis but adjusted for PS. Due to multiple comparisons in the subgroup analysis, the findings should be considered exploratory. Statistical tests were 2-tailed, and significance was defined as P < .05. Data management and statistical analyses were conducted from June 2022 to January 2023 using STATA, Version 17. The VA Orlando Healthcare System Institutional Review Board approved the study and waived requirements for informed consent because only deidentified data were used.

Results

After excluding 49 patients (Supplemental materials, available at doi:10.12788/fp.0560), we identified 30,219 veterans with newly diagnosed CRC between FY 2016 to 2021 (Table 1). Of these, 19,422 (64.3%) resided in urban areas and 10,797 (35.7%) resided in rural areas (Table 2). The mean (SD) duration from the first CRC diagnosis to death or study end was 832 (640) days, and the median (IQR) was 723 (246–1330) days. Overall, incident CRC diagnoses were numerically highest in FY 2016 and lowest in FY 2020 (Figure 1). Patients with CRC in rural areas vs urban areas were significantly older (mean, 71.2 years vs 70.8 years, respectively; P < .001), more likely to be male (96.7% vs 95.7%, respectively; P < .001), more likely to be White (83.6% vs 67.8%, respectively; P < .001) and more likely to be non-Hispanic (92.2% vs 87.5%, respectively; P < .001). In terms of general health, rural veterans with CRC were more likely to be overweight or obese (81.5% rural vs 78.5% urban; P < .001) but had fewer mean comorbidities as measured by CCI (5.66 rural vs 5.90 urban; P < .001). A higher proportion of rural veterans with CRC had received stool-based (fecal occult blood test or fecal immunochemical test) CRC screening tests (61.6% rural vs 57.2% urban; P < .001). Fewer rural patients presented with systemic symptoms or signs within 1 year of CRC diagnosis (54.4% rural vs 57.5% urban, P < .001). Among urban patients with CRC, 6959 (35.8%) deaths were observed, compared with 3766 (34.9%) among rural patients (P = .10).

0525FED-AVAHO-CRC_T10525FED-AVAHO-CRC_T20525FED-AVAHO-CRC_F1

There were 21,568 PS-matched veterans: 10,784 in each group. In the PS-matched cohort, baseline characteristics were similar between veterans in urban and rural communities, including age, sex, race/ethnicity, body mass index, and comorbidities. Among rural patients with CRC, 3763 deaths (34.9%) were observed compared with 3702 (34.3%) among urban veterans. There was no significant difference in the HR of mortality between rural and urban CRC residents (HR, 1.01; 95% CI, 0.97-1.06; P = .53) (Figure 2).

0525FED-AVAHO-CRC_F20525FED-AVAHO-CRC_F30525FED-AVAHO-CRC_F4

Among veterans aged ≤ 45 years, 551 were diagnosed with CRC (391 urban and 160 rural). We PS-matched 142 pairs of urban and rural veterans without residual differences in baseline characteristics (eAppendix 1). There was no significant difference in the HR of mortality between rural and urban veterans aged ≤ 45 years (HR, 0.97; 95% CI, 0.57-1.63; P = .90) (Figure 2). Similarly, no difference in mortality was observed adjusting for PS between all rural and urban veterans aged ≤ 45 years (HR, 1.03; 95% CI, 0.67-1.59; P = .88).

0525FED-AVAHO-CRC_eApp1

There was no difference in total mortality between rural and urban veterans in any subgroup except for American Indian or Alaska Native veterans (HR, 2.41; 95% CI, 1.29-4.50; P = .006) (eAppendix 2).

0525FED-AVAHO-CRC_eApp2

Discussion

This study examined characteristics of patients with CRC between urban and rural areas among veterans who were VHA patients. Similar to other studies, rural veterans with CRC were older, more likely to be White, and were obese, but exhibited fewer comorbidities (lower CCI and lower incidence of congestive heart failure, dementia, hemiplegia, kidney diseases, liver diseases and AIDS, but higher incidence of chronic obstructive lung disease).8,16 The incidence of CRC in this study population was lowest in FY 2020, which was reported by the Centers for Disease Control and Prevention and is attributed to COVID-19 pandemic disruption of health services.24 The overall mortality in this study was similar to rates reported in other studies from the VA Central Cancer Registry.4 In the PS-matched cohort, where baseline characteristics were similar between urban and rural patients with CRC, we found no disparities in CRC-specific mortality between veterans in rural and urban areas. Additionally, when analysis was restricted to veterans aged ≤ 45 years, the results remained consistent.

Subgroup analyses showed no significant difference in mortality between rural and urban areas by sex, race or ethnicity, except rural American Indian or Alaska Native veterans who had double the mortality of their urban counterparts (HR, 2.41; 95% CI, 1.29-4.50; P = .006). This finding is difficult to interpret due to the small number of events and the wide CI. While with a Bonferroni correction the adjusted P value was .08, which is not statistically significant, a previous study found that although CRC incidence was lower overall in American Indian or Alaska Native populations compared to non-Hispanic White populations, CRC incidence was higher among American Indian or Alaska Native individuals in some areas such as Alaska and the Northern Plains.25,26 Studies have noted that rural American Indian/Alaska Native populations experience greater poverty, less access to broadband internet, and limited access to care, contributing to poorer cancer outcomes and lower survival.27 Thus, the finding of disparity in mortality between rural and urban American Indian or Alaska Native veterans warrants further study.

Other studies have raised concerns that CRC disproportionately affects adults in rural areas with higher mortality rates.14-16 These disparities arise from sociodemographic factors and modifiable risk factors, including physical activity, dietary patterns, access to cancer screening, and gaps in quality treatment resources.16,28 These factors operate at multiple levels: from individual, local health system, to community and policy.2,27 For example, a South Carolina study (1996–2016) found that residents in rural areas were more likely to be diagnosed with advanced CRC, possibly indicating lower rates of CRC screening in rural areas. They also had higher likelihood of death from CRC.15 However, the study did not include any clinical parameters, such as comorbidities or obesity. A statewide, population-based study in Utah showed that rural men experienced a lower CRC survival in their unadjusted analysis.16 However, the study was small, with only 3948 urban and 712 rural residents. Additionally, there was no difference in total mortality in the whole cohort (HR, 0.96; 95% CI, 0.86-1.07) or in CRC-specific death (HR, 0.93; 95% CI, 0.81-1.08). A nationwide study also showed that CRC mortality rates were 8% higher in nonmetropolitan or rural areas than in the most urbanized areas containing large metropolitan counties.29 However, this study did not include descriptions of clinical confounders, such as comorbidities, making it difficult to ascertain whether the difference in CRC mortality was due to rurality or differences in baseline risk characteristics.

In this study, the lack of CRC-specific mortality disparities may be attributed to the structures and practices of VHA health care. Recent studies have noted that mortality of several chronic medical conditions treated at the VHA was lower than at non-VHA hospitals.30,31 One study that measured the quality of nonmetastatic CRC care based on National Comprehensive Cancer Network guidelines showed that > 72% of VHA patients received guideline-concordant care for each diagnostic and therapeutic measure, except for follow-up colonoscopy timing, which appear to be similar or superior to that of the private sector.30,32,33 Some of the VA initiative for CRC screening may bypass the urban-rurality divide such as the mailed fecal immunochemical test program for CRC. This program was implemented at the onset of the COVID-19 pandemic to avoid disruptions of medical care.34 Rural patients are more likely to undergo fecal immunochemical testing when compared to urban patients in this data. Beyond clinical care, the VHA uses processes to tackle social determinants of health such as housing, food security, and transportation, promoting equal access to health care, and promoting cultural competency among HCPs.35-37

The results suggest that solutions to CRC disparities between rural and urban areas need to consider known barriers to rural health care, including transportation, diminished rural health care workforce, and other social determinants of health.9,10,27,38 VHA makes considerable efforts to provide equitable care to all enrolled veterans, including specific programs for rural veterans, including ongoing outreach.39 This study demonstrated lack of disparity in CRC-specific mortality in veterans receiving VHA care, highlighting the importance of these efforts.

Strengths and Limitations

This study used the VHA cohort to compare patient characteristics and mortality between patients with CRC residing in rural and urban areas. The study provides nationwide perspectives on CRC across the geographical spectrum and used a longitudinal cohort with prolonged follow-up to account for comorbidities.

However, the study compared a cohort of rural and urban veterans enrolled in the VHA; hence, the results may not reflect CRC outcomes in veterans without access to VHA care. Rurality has been independently associated with decreased likelihood of meeting CRC screening guidelines among veterans and military service members.38 This study lacked sufficient information to compare CRC staging or treatment modalities among veterans. Although the data cannot identify CRC stage, the proportions of patients with metastatic CRC at diagnosis and CRC location were similar between groups. The study did not have information on their care outside of VHA setting.

This study could not ascertain whether disparities existed in CRC treatment modality since rural residence may result in referral to community-based CRC care, which did not appear in the data. To address these limitations, we used death from any cause as the primary outcome, since death is a hard outcome and is not subject to ascertainment bias. The relatively short follow-up time is another limitation, though subgroup analysis by follow-up did not show significant differences. Despite PS matching, residual unmeasured confounding may exist between urban and rural groups. The predominantly White, male VHA population with high CCI may limit the generalizability of the results.

Conclusions

Rural VHA enrollees had similar survival rates after CRC diagnosis compared to their urban counterparts in a PS-matched analysis. The VHA models of care—including mailed CRC screening tools, several socioeconomic determinants of health (housing, food security, and transportation), and promoting equal access to health care, as well as cultural competency among HCPs—HCPs—may help alleviate disparities across the rural-urban spectrum. The VHA should continue efforts to enroll veterans and provide comprehensive coordinated care in community partnerships.

References
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  3. Murphy G, Devesa SS, Cross AJ, Inskip PD, McGlynn KA, Cook MB. Sex disparities in colorectal cancer incidence by anatomic subsite, race and age. Int J Cancer. 2011;128(7):1668-75. doi:10.1002/ijc.25481
  4. Zullig LL, Smith VA, Jackson GL, et al. Colorectal cancer statistics from the Veterans Affairs central cancer registry. Clin Colorectal Cancer. 2016;15(4):e199-e204. doi:10.1016/j.clcc.2016.04.005
  5. Lin JS, Perdue LA, Henrikson NB, Bean SI, Blasi PR. Screening for Colorectal Cancer: An Evidence Update for the US Preventive Services Task Force. 2021. U.S. Preventive Services Task Force Evidence Syntheses, formerly Systematic Evidence Reviews:Chapter 1. Agency for Healthcare Research and Quality (US); 2021. Accessed February 18, 2025. https://www.ncbi.nlm.nih.gov/books/NBK570917/
  6. Siegel RL, Fedewa SA, Anderson WF, et al. Colorectal cancer incidence patterns in the United States, 1974-2013. J Natl Cancer Inst. 2017;109(8). doi:10.1093/jnci/djw322
  7. Davidson KW, Barry MJ, Mangione CM, et al. Screening for colorectal cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325(19):1965-1977. doi:10.1001/jama.2021.6238
  8. Hines R, Markossian T, Johnson A, Dong F, Bayakly R. Geographic residency status and census tract socioeconomic status as determinants of colorectal cancer outcomes. Am J Public Health. 2014;104(3):e63-e71. doi:10.2105/AJPH.2013.301572
  9. Cauwels J. The many barriers to high-quality rural health care. 2022;(9):1-32. NEJM Catal Innov Care Deliv. Accessed April 24, 2025. https://catalyst.nejm.org/doi/pdf/10.1056/CAT.22.0254
  10. Gong G, Phillips SG, Hudson C, Curti D, Philips BU. Higher US rural mortality rates linked to socioeconomic status, physician shortages, and lack of health insurance. Health Aff (Millwood);38(12):2003-2010. doi:10.1377/hlthaff.2019.00722
  11. Aboagye JK, Kaiser HE, Hayanga AJ. Rural-urban differences in access to specialist providers of colorectal cancer care in the United States: a physician workforce issue. JAMA Surg. 2014;149(6):537-543. doi:10.1001/jamasurg.2013.5062
  12. Lyckholm LJ, Hackney MH, Smith TJ. Ethics of rural health care. Crit Rev Oncol Hematol. 2001;40(2):131-138. doi:10.1016/s1040-8428(01)00139-1
  13. Krieger N, Williams DR, Moss NE. Measuring social class in US public health research: concepts, methodologies, and guidelines. Annu Rev Public Health. 1997;18:341-378. doi:10.1146/annurev.publhealth.18.1.341
  14. Singh GK, Jemal A. Socioeconomic and racial/ethnic disparities in cancer mortality, incidence, and survival in the United States, 1950-2014: over six decades of changing patterns and widening inequalities. J Environ Public Health. 2017;2017:2819372. doi:10.1155/2017/2819372
  15. Adams SA, Zahnd WE, Ranganathan R, et al. Rural and racial disparities in colorectal cancer incidence and mortality in South Carolina, 1996 - 2016. J Rural Health. 2022;38(1):34-39. doi:10.1111/jrh.12580
  16. Rogers CR, Blackburn BE, Huntington M, et al. Rural- urban disparities in colorectal cancer survival and risk among men in Utah: a statewide population-based study. Cancer Causes Control. 2020;31(3):241-253. doi:10.1007/s10552-020-01268-2
  17. US Department of Veterans Affairs. VA Informatics and Computing Infrastructure (VINCI), VA HSR RES 13-457. https://vincicentral.vinci.med.va.gov [Source not verified]
  18. US Department of Veterans Affairs Information Resource Center. VIReC Research User Guide: PSSG Geocoded Enrollee Files, 2015 Edition. US Department of Veterans Affairs, Health Services Research & Development Service, Information Resource Center; May. 2016. [source not verified]
  19. Goldsmith HF, Puskin DS, Stiles DJ. Improving the operational definition of “rural areas” for federal programs. US Department of Health and Human Services; 1993. Accessed February 27, 2025. https://www.ruralhealthinfo.org/pdf/improving-the-operational-definition-of-rural-areas.pdf
  20. Adams MA, Kerr EA, Dominitz JA, et al. Development and validation of a new ICD-10-based screening colonoscopy overuse measure in a large integrated healthcare system: a retrospective observational study. BMJ Qual Saf. 2023;32(7):414-424. doi:10.1136/bmjqs-2021-014236
  21. Schneeweiss S, Wang PS, Avorn J, Glynn RJ. Improved comorbidity adjustment for predicting mortality in Medicare populations. Health Serv Res. 2003;38(4):1103-1120. doi:10.1111/1475-6773.00165
  22. Becker S, Ichino A. Estimation of average treatment effects based on propensity scores. The Stata Journal. 2002;2(4):358-377.
  23. Leuven E, Sianesi B. PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Statistical software components. Revised February 1, 2018. Accessed February 27, 2025. https://ideas.repec.org/c/boc/bocode/s432001.html.
  24. US Cancer Statistics Working Group. US cancer statistics data visualizations tool. Centers for Disease Control and Prevention. June 2024. Accessed February 27, 2025. https://www.cdc.gov/cancer/dataviz
  25. Cao J, Zhang S. Multiple Comparison Procedures. JAMA. 2014;312(5):543-544. doi:10.1001/jama.2014.9440
  26. Gopalani SV, Janitz AE, Martinez SA, et al. Trends in cancer incidence among American Indians and Alaska Natives and Non-Hispanic Whites in the United States, 1999-2015. Epidemiology. 2020;31(2):205-213. doi:10.1097/EDE.0000000000001140
  27. Zahnd WE, Murphy C, Knoll M, et al. The intersection of rural residence and minority race/ethnicity in cancer disparities in the United States. Int J Environ Res Public Health. 2021;18(4). doi:10.3390/ijerph18041384
  28. Blake KD, Moss JL, Gaysynsky A, Srinivasan S, Croyle RT. Making the case for investment in rural cancer control: an analysis of rural cancer incidence, mortality, and funding trends. Cancer Epidemiol Biomarkers Prev. 2017;26(7):992-997. doi:10.1158/1055-9965.EPI-17-0092
  29. Singh GK, Williams SD, Siahpush M, Mulhollen A. Socioeconomic, rural-urban, and racial inequalities in US cancer mortality: part i-all cancers and lung cancer and part iicolorectal, prostate, breast, and cervical cancers. J Cancer Epidemiol. 2011;2011:107497. doi:10.1155/2011/107497
  30. Jackson GL, Melton LD, Abbott DH, et al. Quality of nonmetastatic colorectal cancer care in the Department of Veterans Affairs. J Clin Oncol. 2010;28(19):3176-3181. doi:10.1200/JCO.2009.26.7948
  31. Yoon J, Phibbs CS, Ong MK, et al. Outcomes of veterans treated in Veterans Affairs hospitals vs non-Veterans Affairs hospitals. JAMA Netw Open. 2023;6(12):e2345898. doi:10.1001/jamanetworkopen.2023.45898
  32. Malin JL, Schneider EC, Epstein AM, Adams J, Emanuel EJ, Kahn KL. Results of the National Initiative for Cancer Care Quality: how can we improve the quality of cancer care in the United States? J Clin Oncol. 2006;24(4):626-634. doi:10.1200/JCO.2005.03.3365
  33. Levin B, Lieberman DA, McFarland B, et al. Screening and surveillance for the early detection of colorectal cancer and adenomatous polyps, 2008: a joint guideline from the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology. Gastroenterology. 2008;134(5):1570-1595. doi:10.1053/j.gastro.2008.02.002
  34. Deeds SA, Moore CB, Gunnink EJ, et al. Implementation of a mailed faecal immunochemical test programme for colorectal cancer screening among Veterans. BMJ Open Qual. 2022;11(4). doi:10.1136/bmjoq-2022-001927
  35. Yehia BR, Greenstone CL, Hosenfeld CB, Matthews KL, Zephyrin LC. The role of VA community care in addressing health and health care disparities. Med Care. 2017;55(Suppl 9 suppl 2):S4-S5. doi:10.1097/MLR.0000000000000768
  36. Wright BN, MacDermid Wadsworth S, Wellnitz A, Eicher- Miller HA. Reaching rural veterans: a new mechanism to connect rural, low-income US Veterans with resources and improve food security. J Public Health (Oxf). 2019;41(4):714-723. doi:10.1093/pubmed/fdy203
  37. Nelson RE, Byrne TH, Suo Y, et al. Association of temporary financial assistance with housing stability among US veterans in the supportive services for veteran families program. JAMA Netw Open. 2021;4(2):e2037047. doi:10.1001/jamanetworkopen.2020.37047
  38. McDaniel JT, Albright D, Lee HY, et al. Rural–urban disparities in colorectal cancer screening among military service members and Veterans. J Mil Veteran Fam Health. 2019;5(1):40-48. doi:10.3138/jmvfh.2018-0013
  39. US Department of Veterans Affairs, Office of Rural Health. The rural veteran outreach toolkit. Updated February 12, 2025. Accessed February 18, 2025. https://www.ruralhealth.va.gov/partners/toolkit.asp
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Minh Anh Le, MDa; Po-Hong Liu, MDb; Amar Mandalia, MDc; Sergio Romero, MDd; Ishak A. Mansi, MDa,c; Moheb Boktor, MDb

Author affiliations: 
aUniversity of Central Florida, Orlando 
bUniversity of Texas Southwestern Medical Center, Dallas 
cOrlando Veterans Affairs Medical Center, Florida 
dNorth Florida/South Georgia Veterans Health System, Gainesville

Author disclosures: The authors report no actual or potential conflicts of interest with regard to this article.

Correspondence: Ishak Mansi ([email protected])

Fed Pract. 2025;42(suppl 2). Published online May 8. doi:10.12788/fp.0560

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Minh Anh Le, MDa; Po-Hong Liu, MDb; Amar Mandalia, MDc; Sergio Romero, MDd; Ishak A. Mansi, MDa,c; Moheb Boktor, MDb

Author affiliations: 
aUniversity of Central Florida, Orlando 
bUniversity of Texas Southwestern Medical Center, Dallas 
cOrlando Veterans Affairs Medical Center, Florida 
dNorth Florida/South Georgia Veterans Health System, Gainesville

Author disclosures: The authors report no actual or potential conflicts of interest with regard to this article.

Correspondence: Ishak Mansi ([email protected])

Fed Pract. 2025;42(suppl 2). Published online May 8. doi:10.12788/fp.0560

Author and Disclosure Information

Minh Anh Le, MDa; Po-Hong Liu, MDb; Amar Mandalia, MDc; Sergio Romero, MDd; Ishak A. Mansi, MDa,c; Moheb Boktor, MDb

Author affiliations: 
aUniversity of Central Florida, Orlando 
bUniversity of Texas Southwestern Medical Center, Dallas 
cOrlando Veterans Affairs Medical Center, Florida 
dNorth Florida/South Georgia Veterans Health System, Gainesville

Author disclosures: The authors report no actual or potential conflicts of interest with regard to this article.

Correspondence: Ishak Mansi ([email protected])

Fed Pract. 2025;42(suppl 2). Published online May 8. doi:10.12788/fp.0560

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Colorectal cancer (CRC) is the second-leading cause of cancer-related deaths in the United States, with an estimated 52,550 deaths in 2023.1 However, the disease burden varies among different segments of the population.2 While both CRC incidence and mortality have been decreasing due to screening and advances in treatment, there are disparities in incidence and mortality across the sociodemographic spectrum including race, ethnicity, education, and income.1-4 While CRC incidence is decreasing for older adults, it is increasing among those aged < 55 years.5 The incidence of CRC in adults aged 40 to 54 years has increased by 0.5% to 1.3% annually since the mid-1990s.6 The US Preventive Services Task Force now recommends starting CRC screening at age 45 years for asymptomatic adults with average risk.7

Disparities also exist across geographical boundaries and living environment. Rural Americans faces additional challenges in health and lifestyle that can affect CRC outcomes. Compared to their urban counterparts, rural residents are more likely to be older, have lower levels of education, higher levels of poverty, lack health insurance, and less access to health care practitioners (HCPs).8-10 Geographic proximity, defined as travel time or physical distance to a health facility, has been recognized as a predictor of inferior outcomes.11 These aspects of rural living may pose challenges for accessing care for CRC screening and treatment.11-13 National and local studies have shown disparities in CRC screening rates, incidence, and mortality between rural and urban populations.14-16

It is unclear whether rural/urban disparities persist under the Veterans Health Administration (VHA) health care delivery model. This study examined differences in baseline characteristics and mortality between rural and urban veterans newly diagnosed with CRC. We also focused on a subpopulation aged ≤ 45 years.

Methods

This study extracted national data from the US Department of Veterans Affairs (VA) Corporate Data Warehouse (CDW) hosted in the VA Informatics and Computing Infrastructure (VINCI) environment. VINCI is an initiative to improve access to VA data and facilitate the analysis of these data while ensuring veterans’ privacy and data security.17 CDW is the VHA business intelligence information repository, which extracts data from clinical and nonclinical sources following prescribed and validated protocols. Data extracted included demographics, diagnosis, and procedure codes for both inpatient and outpatient encounters, vital signs, and vital status. This study used data previously extracted from a national cohort of veterans that encompassed all patients who received a group of commonly prescribed medications, such as statins, proton pump inhibitors, histamine-2 blockers, acetaminophen-containing products, and hydrocortisone-containing skin applications. This cohort encompassed 8,648,754 veterans, from whom 2,460,727 had encounters during fiscal years (FY) 2016 to 2021 (study period). The cohort was used to ensure that subjects were VHA patients, allowing them to adequately capture their clinical profiles.

Patients were identified as rural or urban based on their residence address at the date of their first diagnosis of CRC. The Geospatial Service Support Center (GSSC) aggregates and updates veterans’ residence address records for all enrolled veterans from the National Change of Address database. The data contain 1 record per enrollee. GSSC Geocoded Enrollee File contains enrollee addresses and their rurality indicators, categorized as urban, rural, or highly rural.18 Rurality is defined by the Rural Urban Commuting Area (RUCA) categories developed by the Department of Agriculture and the Health Resources and Services Administration of the US Department of Health and Human Services.19 Urban areas had RUCA codes of 1.0 to 1.1, and highly rural areas had RUCA scores of 10.0. All other areas were classified as rural. Since the proportion of veterans from highly rural areas was small, we included residents from highly rural areas in the rural residents’ group.

Inclusion and Exclusion Criteria

All veterans newly diagnosed with CRC from FY 2016 to 2021 were included. We used the ninth and tenth clinical modification revisions of the International Classification of Diseases (ICD-9-CM and ICD-10-CM) to define CRC diagnosis (Supplemental materials).4,20 To ensure that patients were newly diagnosed with CRC, this study excluded patients with a previous ICD-9-CM code for CRC diagnosis since FY 2003.

Comorbidities were identified using diagnosis and procedure codes from inpatient and outpatient encounters, which were used to calculate the Charlson Comorbidity Index (CCI) at the time of CRC diagnosis using the weighted method described by Schneeweiss et al.21 We defined CRC high-risk conditions and CRC screening tests, including flexible sigmoidoscopy and stool tests, as described in previous studies (Supplemental materials).20

The main outcome was total mortality. The date of death was extracted from the VHA Death Ascertainment File, which contains mortality data from the Master Person Index file in CDW and the Social Security Administration Death Master File. We used the date of death from any cause, as cause of death was not available.

A propensity score (PS) was created to match rural (including highly rural) and urban residents at a ratio of 1:1. Using a standard procedure described in prior publications, multivariable logistic regression used all baseline characteristics to estimate the PS and perform nearest-number matching without replacement.22,23 A caliper of 0.01 maximized the matched cohort size and achieved balance (Supplemental materials). We then examined the balance of baseline characteristics between PS-matched groups.

Analyses

Cox proportional hazards regression analysis estimated the hazard ratio (HR) of death in rural residents compared to urban residents in the PS-matched cohort. The outcome event was the date of death during the study’s follow-up period (defined as period from first CRC diagnosis to death or study end), with censoring at the study’s end date (September 30, 2021). The proportional hazards assumption was assessed by inspecting the Kaplan-Meier curves. Multiple analyses examined the HR of total mortality in the PS-matched cohort, stratified by sex, race, and ethnicity. We also examined the HR of total mortality stratified by duration of follow-up.

Another PS-matching analysis among veterans aged ≤ 45 years was performed using the same techniques described earlier in this article. We performed a Cox proportional hazards regression analysis to compare mortality in PS-matched urban and rural veterans aged ≤ 45 years. The HR of death in all veterans aged ≤ 45 years (before PS-matching) was estimated using Cox proportional hazard regression analysis, adjusting for PS.

Dichotomous variables were compared using X2 tests and continuous variables were compared using t tests. Baseline characteristics with missing values were converted into categorical variables and the proportion of subjects with missing values was equalized between treatment groups after PS-matching. For subgroup analysis, we examined the HR of total mortality in each subgroup using separate Cox proportional hazards regression models similar to the primary analysis but adjusted for PS. Due to multiple comparisons in the subgroup analysis, the findings should be considered exploratory. Statistical tests were 2-tailed, and significance was defined as P < .05. Data management and statistical analyses were conducted from June 2022 to January 2023 using STATA, Version 17. The VA Orlando Healthcare System Institutional Review Board approved the study and waived requirements for informed consent because only deidentified data were used.

Results

After excluding 49 patients (Supplemental materials, available at doi:10.12788/fp.0560), we identified 30,219 veterans with newly diagnosed CRC between FY 2016 to 2021 (Table 1). Of these, 19,422 (64.3%) resided in urban areas and 10,797 (35.7%) resided in rural areas (Table 2). The mean (SD) duration from the first CRC diagnosis to death or study end was 832 (640) days, and the median (IQR) was 723 (246–1330) days. Overall, incident CRC diagnoses were numerically highest in FY 2016 and lowest in FY 2020 (Figure 1). Patients with CRC in rural areas vs urban areas were significantly older (mean, 71.2 years vs 70.8 years, respectively; P < .001), more likely to be male (96.7% vs 95.7%, respectively; P < .001), more likely to be White (83.6% vs 67.8%, respectively; P < .001) and more likely to be non-Hispanic (92.2% vs 87.5%, respectively; P < .001). In terms of general health, rural veterans with CRC were more likely to be overweight or obese (81.5% rural vs 78.5% urban; P < .001) but had fewer mean comorbidities as measured by CCI (5.66 rural vs 5.90 urban; P < .001). A higher proportion of rural veterans with CRC had received stool-based (fecal occult blood test or fecal immunochemical test) CRC screening tests (61.6% rural vs 57.2% urban; P < .001). Fewer rural patients presented with systemic symptoms or signs within 1 year of CRC diagnosis (54.4% rural vs 57.5% urban, P < .001). Among urban patients with CRC, 6959 (35.8%) deaths were observed, compared with 3766 (34.9%) among rural patients (P = .10).

0525FED-AVAHO-CRC_T10525FED-AVAHO-CRC_T20525FED-AVAHO-CRC_F1

There were 21,568 PS-matched veterans: 10,784 in each group. In the PS-matched cohort, baseline characteristics were similar between veterans in urban and rural communities, including age, sex, race/ethnicity, body mass index, and comorbidities. Among rural patients with CRC, 3763 deaths (34.9%) were observed compared with 3702 (34.3%) among urban veterans. There was no significant difference in the HR of mortality between rural and urban CRC residents (HR, 1.01; 95% CI, 0.97-1.06; P = .53) (Figure 2).

0525FED-AVAHO-CRC_F20525FED-AVAHO-CRC_F30525FED-AVAHO-CRC_F4

Among veterans aged ≤ 45 years, 551 were diagnosed with CRC (391 urban and 160 rural). We PS-matched 142 pairs of urban and rural veterans without residual differences in baseline characteristics (eAppendix 1). There was no significant difference in the HR of mortality between rural and urban veterans aged ≤ 45 years (HR, 0.97; 95% CI, 0.57-1.63; P = .90) (Figure 2). Similarly, no difference in mortality was observed adjusting for PS between all rural and urban veterans aged ≤ 45 years (HR, 1.03; 95% CI, 0.67-1.59; P = .88).

0525FED-AVAHO-CRC_eApp1

There was no difference in total mortality between rural and urban veterans in any subgroup except for American Indian or Alaska Native veterans (HR, 2.41; 95% CI, 1.29-4.50; P = .006) (eAppendix 2).

0525FED-AVAHO-CRC_eApp2

Discussion

This study examined characteristics of patients with CRC between urban and rural areas among veterans who were VHA patients. Similar to other studies, rural veterans with CRC were older, more likely to be White, and were obese, but exhibited fewer comorbidities (lower CCI and lower incidence of congestive heart failure, dementia, hemiplegia, kidney diseases, liver diseases and AIDS, but higher incidence of chronic obstructive lung disease).8,16 The incidence of CRC in this study population was lowest in FY 2020, which was reported by the Centers for Disease Control and Prevention and is attributed to COVID-19 pandemic disruption of health services.24 The overall mortality in this study was similar to rates reported in other studies from the VA Central Cancer Registry.4 In the PS-matched cohort, where baseline characteristics were similar between urban and rural patients with CRC, we found no disparities in CRC-specific mortality between veterans in rural and urban areas. Additionally, when analysis was restricted to veterans aged ≤ 45 years, the results remained consistent.

Subgroup analyses showed no significant difference in mortality between rural and urban areas by sex, race or ethnicity, except rural American Indian or Alaska Native veterans who had double the mortality of their urban counterparts (HR, 2.41; 95% CI, 1.29-4.50; P = .006). This finding is difficult to interpret due to the small number of events and the wide CI. While with a Bonferroni correction the adjusted P value was .08, which is not statistically significant, a previous study found that although CRC incidence was lower overall in American Indian or Alaska Native populations compared to non-Hispanic White populations, CRC incidence was higher among American Indian or Alaska Native individuals in some areas such as Alaska and the Northern Plains.25,26 Studies have noted that rural American Indian/Alaska Native populations experience greater poverty, less access to broadband internet, and limited access to care, contributing to poorer cancer outcomes and lower survival.27 Thus, the finding of disparity in mortality between rural and urban American Indian or Alaska Native veterans warrants further study.

Other studies have raised concerns that CRC disproportionately affects adults in rural areas with higher mortality rates.14-16 These disparities arise from sociodemographic factors and modifiable risk factors, including physical activity, dietary patterns, access to cancer screening, and gaps in quality treatment resources.16,28 These factors operate at multiple levels: from individual, local health system, to community and policy.2,27 For example, a South Carolina study (1996–2016) found that residents in rural areas were more likely to be diagnosed with advanced CRC, possibly indicating lower rates of CRC screening in rural areas. They also had higher likelihood of death from CRC.15 However, the study did not include any clinical parameters, such as comorbidities or obesity. A statewide, population-based study in Utah showed that rural men experienced a lower CRC survival in their unadjusted analysis.16 However, the study was small, with only 3948 urban and 712 rural residents. Additionally, there was no difference in total mortality in the whole cohort (HR, 0.96; 95% CI, 0.86-1.07) or in CRC-specific death (HR, 0.93; 95% CI, 0.81-1.08). A nationwide study also showed that CRC mortality rates were 8% higher in nonmetropolitan or rural areas than in the most urbanized areas containing large metropolitan counties.29 However, this study did not include descriptions of clinical confounders, such as comorbidities, making it difficult to ascertain whether the difference in CRC mortality was due to rurality or differences in baseline risk characteristics.

In this study, the lack of CRC-specific mortality disparities may be attributed to the structures and practices of VHA health care. Recent studies have noted that mortality of several chronic medical conditions treated at the VHA was lower than at non-VHA hospitals.30,31 One study that measured the quality of nonmetastatic CRC care based on National Comprehensive Cancer Network guidelines showed that > 72% of VHA patients received guideline-concordant care for each diagnostic and therapeutic measure, except for follow-up colonoscopy timing, which appear to be similar or superior to that of the private sector.30,32,33 Some of the VA initiative for CRC screening may bypass the urban-rurality divide such as the mailed fecal immunochemical test program for CRC. This program was implemented at the onset of the COVID-19 pandemic to avoid disruptions of medical care.34 Rural patients are more likely to undergo fecal immunochemical testing when compared to urban patients in this data. Beyond clinical care, the VHA uses processes to tackle social determinants of health such as housing, food security, and transportation, promoting equal access to health care, and promoting cultural competency among HCPs.35-37

The results suggest that solutions to CRC disparities between rural and urban areas need to consider known barriers to rural health care, including transportation, diminished rural health care workforce, and other social determinants of health.9,10,27,38 VHA makes considerable efforts to provide equitable care to all enrolled veterans, including specific programs for rural veterans, including ongoing outreach.39 This study demonstrated lack of disparity in CRC-specific mortality in veterans receiving VHA care, highlighting the importance of these efforts.

Strengths and Limitations

This study used the VHA cohort to compare patient characteristics and mortality between patients with CRC residing in rural and urban areas. The study provides nationwide perspectives on CRC across the geographical spectrum and used a longitudinal cohort with prolonged follow-up to account for comorbidities.

However, the study compared a cohort of rural and urban veterans enrolled in the VHA; hence, the results may not reflect CRC outcomes in veterans without access to VHA care. Rurality has been independently associated with decreased likelihood of meeting CRC screening guidelines among veterans and military service members.38 This study lacked sufficient information to compare CRC staging or treatment modalities among veterans. Although the data cannot identify CRC stage, the proportions of patients with metastatic CRC at diagnosis and CRC location were similar between groups. The study did not have information on their care outside of VHA setting.

This study could not ascertain whether disparities existed in CRC treatment modality since rural residence may result in referral to community-based CRC care, which did not appear in the data. To address these limitations, we used death from any cause as the primary outcome, since death is a hard outcome and is not subject to ascertainment bias. The relatively short follow-up time is another limitation, though subgroup analysis by follow-up did not show significant differences. Despite PS matching, residual unmeasured confounding may exist between urban and rural groups. The predominantly White, male VHA population with high CCI may limit the generalizability of the results.

Conclusions

Rural VHA enrollees had similar survival rates after CRC diagnosis compared to their urban counterparts in a PS-matched analysis. The VHA models of care—including mailed CRC screening tools, several socioeconomic determinants of health (housing, food security, and transportation), and promoting equal access to health care, as well as cultural competency among HCPs—HCPs—may help alleviate disparities across the rural-urban spectrum. The VHA should continue efforts to enroll veterans and provide comprehensive coordinated care in community partnerships.

Colorectal cancer (CRC) is the second-leading cause of cancer-related deaths in the United States, with an estimated 52,550 deaths in 2023.1 However, the disease burden varies among different segments of the population.2 While both CRC incidence and mortality have been decreasing due to screening and advances in treatment, there are disparities in incidence and mortality across the sociodemographic spectrum including race, ethnicity, education, and income.1-4 While CRC incidence is decreasing for older adults, it is increasing among those aged < 55 years.5 The incidence of CRC in adults aged 40 to 54 years has increased by 0.5% to 1.3% annually since the mid-1990s.6 The US Preventive Services Task Force now recommends starting CRC screening at age 45 years for asymptomatic adults with average risk.7

Disparities also exist across geographical boundaries and living environment. Rural Americans faces additional challenges in health and lifestyle that can affect CRC outcomes. Compared to their urban counterparts, rural residents are more likely to be older, have lower levels of education, higher levels of poverty, lack health insurance, and less access to health care practitioners (HCPs).8-10 Geographic proximity, defined as travel time or physical distance to a health facility, has been recognized as a predictor of inferior outcomes.11 These aspects of rural living may pose challenges for accessing care for CRC screening and treatment.11-13 National and local studies have shown disparities in CRC screening rates, incidence, and mortality between rural and urban populations.14-16

It is unclear whether rural/urban disparities persist under the Veterans Health Administration (VHA) health care delivery model. This study examined differences in baseline characteristics and mortality between rural and urban veterans newly diagnosed with CRC. We also focused on a subpopulation aged ≤ 45 years.

Methods

This study extracted national data from the US Department of Veterans Affairs (VA) Corporate Data Warehouse (CDW) hosted in the VA Informatics and Computing Infrastructure (VINCI) environment. VINCI is an initiative to improve access to VA data and facilitate the analysis of these data while ensuring veterans’ privacy and data security.17 CDW is the VHA business intelligence information repository, which extracts data from clinical and nonclinical sources following prescribed and validated protocols. Data extracted included demographics, diagnosis, and procedure codes for both inpatient and outpatient encounters, vital signs, and vital status. This study used data previously extracted from a national cohort of veterans that encompassed all patients who received a group of commonly prescribed medications, such as statins, proton pump inhibitors, histamine-2 blockers, acetaminophen-containing products, and hydrocortisone-containing skin applications. This cohort encompassed 8,648,754 veterans, from whom 2,460,727 had encounters during fiscal years (FY) 2016 to 2021 (study period). The cohort was used to ensure that subjects were VHA patients, allowing them to adequately capture their clinical profiles.

Patients were identified as rural or urban based on their residence address at the date of their first diagnosis of CRC. The Geospatial Service Support Center (GSSC) aggregates and updates veterans’ residence address records for all enrolled veterans from the National Change of Address database. The data contain 1 record per enrollee. GSSC Geocoded Enrollee File contains enrollee addresses and their rurality indicators, categorized as urban, rural, or highly rural.18 Rurality is defined by the Rural Urban Commuting Area (RUCA) categories developed by the Department of Agriculture and the Health Resources and Services Administration of the US Department of Health and Human Services.19 Urban areas had RUCA codes of 1.0 to 1.1, and highly rural areas had RUCA scores of 10.0. All other areas were classified as rural. Since the proportion of veterans from highly rural areas was small, we included residents from highly rural areas in the rural residents’ group.

Inclusion and Exclusion Criteria

All veterans newly diagnosed with CRC from FY 2016 to 2021 were included. We used the ninth and tenth clinical modification revisions of the International Classification of Diseases (ICD-9-CM and ICD-10-CM) to define CRC diagnosis (Supplemental materials).4,20 To ensure that patients were newly diagnosed with CRC, this study excluded patients with a previous ICD-9-CM code for CRC diagnosis since FY 2003.

Comorbidities were identified using diagnosis and procedure codes from inpatient and outpatient encounters, which were used to calculate the Charlson Comorbidity Index (CCI) at the time of CRC diagnosis using the weighted method described by Schneeweiss et al.21 We defined CRC high-risk conditions and CRC screening tests, including flexible sigmoidoscopy and stool tests, as described in previous studies (Supplemental materials).20

The main outcome was total mortality. The date of death was extracted from the VHA Death Ascertainment File, which contains mortality data from the Master Person Index file in CDW and the Social Security Administration Death Master File. We used the date of death from any cause, as cause of death was not available.

A propensity score (PS) was created to match rural (including highly rural) and urban residents at a ratio of 1:1. Using a standard procedure described in prior publications, multivariable logistic regression used all baseline characteristics to estimate the PS and perform nearest-number matching without replacement.22,23 A caliper of 0.01 maximized the matched cohort size and achieved balance (Supplemental materials). We then examined the balance of baseline characteristics between PS-matched groups.

Analyses

Cox proportional hazards regression analysis estimated the hazard ratio (HR) of death in rural residents compared to urban residents in the PS-matched cohort. The outcome event was the date of death during the study’s follow-up period (defined as period from first CRC diagnosis to death or study end), with censoring at the study’s end date (September 30, 2021). The proportional hazards assumption was assessed by inspecting the Kaplan-Meier curves. Multiple analyses examined the HR of total mortality in the PS-matched cohort, stratified by sex, race, and ethnicity. We also examined the HR of total mortality stratified by duration of follow-up.

Another PS-matching analysis among veterans aged ≤ 45 years was performed using the same techniques described earlier in this article. We performed a Cox proportional hazards regression analysis to compare mortality in PS-matched urban and rural veterans aged ≤ 45 years. The HR of death in all veterans aged ≤ 45 years (before PS-matching) was estimated using Cox proportional hazard regression analysis, adjusting for PS.

Dichotomous variables were compared using X2 tests and continuous variables were compared using t tests. Baseline characteristics with missing values were converted into categorical variables and the proportion of subjects with missing values was equalized between treatment groups after PS-matching. For subgroup analysis, we examined the HR of total mortality in each subgroup using separate Cox proportional hazards regression models similar to the primary analysis but adjusted for PS. Due to multiple comparisons in the subgroup analysis, the findings should be considered exploratory. Statistical tests were 2-tailed, and significance was defined as P < .05. Data management and statistical analyses were conducted from June 2022 to January 2023 using STATA, Version 17. The VA Orlando Healthcare System Institutional Review Board approved the study and waived requirements for informed consent because only deidentified data were used.

Results

After excluding 49 patients (Supplemental materials, available at doi:10.12788/fp.0560), we identified 30,219 veterans with newly diagnosed CRC between FY 2016 to 2021 (Table 1). Of these, 19,422 (64.3%) resided in urban areas and 10,797 (35.7%) resided in rural areas (Table 2). The mean (SD) duration from the first CRC diagnosis to death or study end was 832 (640) days, and the median (IQR) was 723 (246–1330) days. Overall, incident CRC diagnoses were numerically highest in FY 2016 and lowest in FY 2020 (Figure 1). Patients with CRC in rural areas vs urban areas were significantly older (mean, 71.2 years vs 70.8 years, respectively; P < .001), more likely to be male (96.7% vs 95.7%, respectively; P < .001), more likely to be White (83.6% vs 67.8%, respectively; P < .001) and more likely to be non-Hispanic (92.2% vs 87.5%, respectively; P < .001). In terms of general health, rural veterans with CRC were more likely to be overweight or obese (81.5% rural vs 78.5% urban; P < .001) but had fewer mean comorbidities as measured by CCI (5.66 rural vs 5.90 urban; P < .001). A higher proportion of rural veterans with CRC had received stool-based (fecal occult blood test or fecal immunochemical test) CRC screening tests (61.6% rural vs 57.2% urban; P < .001). Fewer rural patients presented with systemic symptoms or signs within 1 year of CRC diagnosis (54.4% rural vs 57.5% urban, P < .001). Among urban patients with CRC, 6959 (35.8%) deaths were observed, compared with 3766 (34.9%) among rural patients (P = .10).

0525FED-AVAHO-CRC_T10525FED-AVAHO-CRC_T20525FED-AVAHO-CRC_F1

There were 21,568 PS-matched veterans: 10,784 in each group. In the PS-matched cohort, baseline characteristics were similar between veterans in urban and rural communities, including age, sex, race/ethnicity, body mass index, and comorbidities. Among rural patients with CRC, 3763 deaths (34.9%) were observed compared with 3702 (34.3%) among urban veterans. There was no significant difference in the HR of mortality between rural and urban CRC residents (HR, 1.01; 95% CI, 0.97-1.06; P = .53) (Figure 2).

0525FED-AVAHO-CRC_F20525FED-AVAHO-CRC_F30525FED-AVAHO-CRC_F4

Among veterans aged ≤ 45 years, 551 were diagnosed with CRC (391 urban and 160 rural). We PS-matched 142 pairs of urban and rural veterans without residual differences in baseline characteristics (eAppendix 1). There was no significant difference in the HR of mortality between rural and urban veterans aged ≤ 45 years (HR, 0.97; 95% CI, 0.57-1.63; P = .90) (Figure 2). Similarly, no difference in mortality was observed adjusting for PS between all rural and urban veterans aged ≤ 45 years (HR, 1.03; 95% CI, 0.67-1.59; P = .88).

0525FED-AVAHO-CRC_eApp1

There was no difference in total mortality between rural and urban veterans in any subgroup except for American Indian or Alaska Native veterans (HR, 2.41; 95% CI, 1.29-4.50; P = .006) (eAppendix 2).

0525FED-AVAHO-CRC_eApp2

Discussion

This study examined characteristics of patients with CRC between urban and rural areas among veterans who were VHA patients. Similar to other studies, rural veterans with CRC were older, more likely to be White, and were obese, but exhibited fewer comorbidities (lower CCI and lower incidence of congestive heart failure, dementia, hemiplegia, kidney diseases, liver diseases and AIDS, but higher incidence of chronic obstructive lung disease).8,16 The incidence of CRC in this study population was lowest in FY 2020, which was reported by the Centers for Disease Control and Prevention and is attributed to COVID-19 pandemic disruption of health services.24 The overall mortality in this study was similar to rates reported in other studies from the VA Central Cancer Registry.4 In the PS-matched cohort, where baseline characteristics were similar between urban and rural patients with CRC, we found no disparities in CRC-specific mortality between veterans in rural and urban areas. Additionally, when analysis was restricted to veterans aged ≤ 45 years, the results remained consistent.

Subgroup analyses showed no significant difference in mortality between rural and urban areas by sex, race or ethnicity, except rural American Indian or Alaska Native veterans who had double the mortality of their urban counterparts (HR, 2.41; 95% CI, 1.29-4.50; P = .006). This finding is difficult to interpret due to the small number of events and the wide CI. While with a Bonferroni correction the adjusted P value was .08, which is not statistically significant, a previous study found that although CRC incidence was lower overall in American Indian or Alaska Native populations compared to non-Hispanic White populations, CRC incidence was higher among American Indian or Alaska Native individuals in some areas such as Alaska and the Northern Plains.25,26 Studies have noted that rural American Indian/Alaska Native populations experience greater poverty, less access to broadband internet, and limited access to care, contributing to poorer cancer outcomes and lower survival.27 Thus, the finding of disparity in mortality between rural and urban American Indian or Alaska Native veterans warrants further study.

Other studies have raised concerns that CRC disproportionately affects adults in rural areas with higher mortality rates.14-16 These disparities arise from sociodemographic factors and modifiable risk factors, including physical activity, dietary patterns, access to cancer screening, and gaps in quality treatment resources.16,28 These factors operate at multiple levels: from individual, local health system, to community and policy.2,27 For example, a South Carolina study (1996–2016) found that residents in rural areas were more likely to be diagnosed with advanced CRC, possibly indicating lower rates of CRC screening in rural areas. They also had higher likelihood of death from CRC.15 However, the study did not include any clinical parameters, such as comorbidities or obesity. A statewide, population-based study in Utah showed that rural men experienced a lower CRC survival in their unadjusted analysis.16 However, the study was small, with only 3948 urban and 712 rural residents. Additionally, there was no difference in total mortality in the whole cohort (HR, 0.96; 95% CI, 0.86-1.07) or in CRC-specific death (HR, 0.93; 95% CI, 0.81-1.08). A nationwide study also showed that CRC mortality rates were 8% higher in nonmetropolitan or rural areas than in the most urbanized areas containing large metropolitan counties.29 However, this study did not include descriptions of clinical confounders, such as comorbidities, making it difficult to ascertain whether the difference in CRC mortality was due to rurality or differences in baseline risk characteristics.

In this study, the lack of CRC-specific mortality disparities may be attributed to the structures and practices of VHA health care. Recent studies have noted that mortality of several chronic medical conditions treated at the VHA was lower than at non-VHA hospitals.30,31 One study that measured the quality of nonmetastatic CRC care based on National Comprehensive Cancer Network guidelines showed that > 72% of VHA patients received guideline-concordant care for each diagnostic and therapeutic measure, except for follow-up colonoscopy timing, which appear to be similar or superior to that of the private sector.30,32,33 Some of the VA initiative for CRC screening may bypass the urban-rurality divide such as the mailed fecal immunochemical test program for CRC. This program was implemented at the onset of the COVID-19 pandemic to avoid disruptions of medical care.34 Rural patients are more likely to undergo fecal immunochemical testing when compared to urban patients in this data. Beyond clinical care, the VHA uses processes to tackle social determinants of health such as housing, food security, and transportation, promoting equal access to health care, and promoting cultural competency among HCPs.35-37

The results suggest that solutions to CRC disparities between rural and urban areas need to consider known barriers to rural health care, including transportation, diminished rural health care workforce, and other social determinants of health.9,10,27,38 VHA makes considerable efforts to provide equitable care to all enrolled veterans, including specific programs for rural veterans, including ongoing outreach.39 This study demonstrated lack of disparity in CRC-specific mortality in veterans receiving VHA care, highlighting the importance of these efforts.

Strengths and Limitations

This study used the VHA cohort to compare patient characteristics and mortality between patients with CRC residing in rural and urban areas. The study provides nationwide perspectives on CRC across the geographical spectrum and used a longitudinal cohort with prolonged follow-up to account for comorbidities.

However, the study compared a cohort of rural and urban veterans enrolled in the VHA; hence, the results may not reflect CRC outcomes in veterans without access to VHA care. Rurality has been independently associated with decreased likelihood of meeting CRC screening guidelines among veterans and military service members.38 This study lacked sufficient information to compare CRC staging or treatment modalities among veterans. Although the data cannot identify CRC stage, the proportions of patients with metastatic CRC at diagnosis and CRC location were similar between groups. The study did not have information on their care outside of VHA setting.

This study could not ascertain whether disparities existed in CRC treatment modality since rural residence may result in referral to community-based CRC care, which did not appear in the data. To address these limitations, we used death from any cause as the primary outcome, since death is a hard outcome and is not subject to ascertainment bias. The relatively short follow-up time is another limitation, though subgroup analysis by follow-up did not show significant differences. Despite PS matching, residual unmeasured confounding may exist between urban and rural groups. The predominantly White, male VHA population with high CCI may limit the generalizability of the results.

Conclusions

Rural VHA enrollees had similar survival rates after CRC diagnosis compared to their urban counterparts in a PS-matched analysis. The VHA models of care—including mailed CRC screening tools, several socioeconomic determinants of health (housing, food security, and transportation), and promoting equal access to health care, as well as cultural competency among HCPs—HCPs—may help alleviate disparities across the rural-urban spectrum. The VHA should continue efforts to enroll veterans and provide comprehensive coordinated care in community partnerships.

References
  1. Siegel RL, Wagle NS, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2023. CA Cancer J Clin. 2023;73(3):233-254. doi:10.3322/caac.21772
  2. Carethers JM, Doubeni CA. Causes of socioeconomic disparities in colorectal cancer and intervention framework and strategies. Gastroenterology. 2020;158(2):354-367. doi:10.1053/j.gastro.2019.10.029
  3. Murphy G, Devesa SS, Cross AJ, Inskip PD, McGlynn KA, Cook MB. Sex disparities in colorectal cancer incidence by anatomic subsite, race and age. Int J Cancer. 2011;128(7):1668-75. doi:10.1002/ijc.25481
  4. Zullig LL, Smith VA, Jackson GL, et al. Colorectal cancer statistics from the Veterans Affairs central cancer registry. Clin Colorectal Cancer. 2016;15(4):e199-e204. doi:10.1016/j.clcc.2016.04.005
  5. Lin JS, Perdue LA, Henrikson NB, Bean SI, Blasi PR. Screening for Colorectal Cancer: An Evidence Update for the US Preventive Services Task Force. 2021. U.S. Preventive Services Task Force Evidence Syntheses, formerly Systematic Evidence Reviews:Chapter 1. Agency for Healthcare Research and Quality (US); 2021. Accessed February 18, 2025. https://www.ncbi.nlm.nih.gov/books/NBK570917/
  6. Siegel RL, Fedewa SA, Anderson WF, et al. Colorectal cancer incidence patterns in the United States, 1974-2013. J Natl Cancer Inst. 2017;109(8). doi:10.1093/jnci/djw322
  7. Davidson KW, Barry MJ, Mangione CM, et al. Screening for colorectal cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325(19):1965-1977. doi:10.1001/jama.2021.6238
  8. Hines R, Markossian T, Johnson A, Dong F, Bayakly R. Geographic residency status and census tract socioeconomic status as determinants of colorectal cancer outcomes. Am J Public Health. 2014;104(3):e63-e71. doi:10.2105/AJPH.2013.301572
  9. Cauwels J. The many barriers to high-quality rural health care. 2022;(9):1-32. NEJM Catal Innov Care Deliv. Accessed April 24, 2025. https://catalyst.nejm.org/doi/pdf/10.1056/CAT.22.0254
  10. Gong G, Phillips SG, Hudson C, Curti D, Philips BU. Higher US rural mortality rates linked to socioeconomic status, physician shortages, and lack of health insurance. Health Aff (Millwood);38(12):2003-2010. doi:10.1377/hlthaff.2019.00722
  11. Aboagye JK, Kaiser HE, Hayanga AJ. Rural-urban differences in access to specialist providers of colorectal cancer care in the United States: a physician workforce issue. JAMA Surg. 2014;149(6):537-543. doi:10.1001/jamasurg.2013.5062
  12. Lyckholm LJ, Hackney MH, Smith TJ. Ethics of rural health care. Crit Rev Oncol Hematol. 2001;40(2):131-138. doi:10.1016/s1040-8428(01)00139-1
  13. Krieger N, Williams DR, Moss NE. Measuring social class in US public health research: concepts, methodologies, and guidelines. Annu Rev Public Health. 1997;18:341-378. doi:10.1146/annurev.publhealth.18.1.341
  14. Singh GK, Jemal A. Socioeconomic and racial/ethnic disparities in cancer mortality, incidence, and survival in the United States, 1950-2014: over six decades of changing patterns and widening inequalities. J Environ Public Health. 2017;2017:2819372. doi:10.1155/2017/2819372
  15. Adams SA, Zahnd WE, Ranganathan R, et al. Rural and racial disparities in colorectal cancer incidence and mortality in South Carolina, 1996 - 2016. J Rural Health. 2022;38(1):34-39. doi:10.1111/jrh.12580
  16. Rogers CR, Blackburn BE, Huntington M, et al. Rural- urban disparities in colorectal cancer survival and risk among men in Utah: a statewide population-based study. Cancer Causes Control. 2020;31(3):241-253. doi:10.1007/s10552-020-01268-2
  17. US Department of Veterans Affairs. VA Informatics and Computing Infrastructure (VINCI), VA HSR RES 13-457. https://vincicentral.vinci.med.va.gov [Source not verified]
  18. US Department of Veterans Affairs Information Resource Center. VIReC Research User Guide: PSSG Geocoded Enrollee Files, 2015 Edition. US Department of Veterans Affairs, Health Services Research & Development Service, Information Resource Center; May. 2016. [source not verified]
  19. Goldsmith HF, Puskin DS, Stiles DJ. Improving the operational definition of “rural areas” for federal programs. US Department of Health and Human Services; 1993. Accessed February 27, 2025. https://www.ruralhealthinfo.org/pdf/improving-the-operational-definition-of-rural-areas.pdf
  20. Adams MA, Kerr EA, Dominitz JA, et al. Development and validation of a new ICD-10-based screening colonoscopy overuse measure in a large integrated healthcare system: a retrospective observational study. BMJ Qual Saf. 2023;32(7):414-424. doi:10.1136/bmjqs-2021-014236
  21. Schneeweiss S, Wang PS, Avorn J, Glynn RJ. Improved comorbidity adjustment for predicting mortality in Medicare populations. Health Serv Res. 2003;38(4):1103-1120. doi:10.1111/1475-6773.00165
  22. Becker S, Ichino A. Estimation of average treatment effects based on propensity scores. The Stata Journal. 2002;2(4):358-377.
  23. Leuven E, Sianesi B. PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Statistical software components. Revised February 1, 2018. Accessed February 27, 2025. https://ideas.repec.org/c/boc/bocode/s432001.html.
  24. US Cancer Statistics Working Group. US cancer statistics data visualizations tool. Centers for Disease Control and Prevention. June 2024. Accessed February 27, 2025. https://www.cdc.gov/cancer/dataviz
  25. Cao J, Zhang S. Multiple Comparison Procedures. JAMA. 2014;312(5):543-544. doi:10.1001/jama.2014.9440
  26. Gopalani SV, Janitz AE, Martinez SA, et al. Trends in cancer incidence among American Indians and Alaska Natives and Non-Hispanic Whites in the United States, 1999-2015. Epidemiology. 2020;31(2):205-213. doi:10.1097/EDE.0000000000001140
  27. Zahnd WE, Murphy C, Knoll M, et al. The intersection of rural residence and minority race/ethnicity in cancer disparities in the United States. Int J Environ Res Public Health. 2021;18(4). doi:10.3390/ijerph18041384
  28. Blake KD, Moss JL, Gaysynsky A, Srinivasan S, Croyle RT. Making the case for investment in rural cancer control: an analysis of rural cancer incidence, mortality, and funding trends. Cancer Epidemiol Biomarkers Prev. 2017;26(7):992-997. doi:10.1158/1055-9965.EPI-17-0092
  29. Singh GK, Williams SD, Siahpush M, Mulhollen A. Socioeconomic, rural-urban, and racial inequalities in US cancer mortality: part i-all cancers and lung cancer and part iicolorectal, prostate, breast, and cervical cancers. J Cancer Epidemiol. 2011;2011:107497. doi:10.1155/2011/107497
  30. Jackson GL, Melton LD, Abbott DH, et al. Quality of nonmetastatic colorectal cancer care in the Department of Veterans Affairs. J Clin Oncol. 2010;28(19):3176-3181. doi:10.1200/JCO.2009.26.7948
  31. Yoon J, Phibbs CS, Ong MK, et al. Outcomes of veterans treated in Veterans Affairs hospitals vs non-Veterans Affairs hospitals. JAMA Netw Open. 2023;6(12):e2345898. doi:10.1001/jamanetworkopen.2023.45898
  32. Malin JL, Schneider EC, Epstein AM, Adams J, Emanuel EJ, Kahn KL. Results of the National Initiative for Cancer Care Quality: how can we improve the quality of cancer care in the United States? J Clin Oncol. 2006;24(4):626-634. doi:10.1200/JCO.2005.03.3365
  33. Levin B, Lieberman DA, McFarland B, et al. Screening and surveillance for the early detection of colorectal cancer and adenomatous polyps, 2008: a joint guideline from the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology. Gastroenterology. 2008;134(5):1570-1595. doi:10.1053/j.gastro.2008.02.002
  34. Deeds SA, Moore CB, Gunnink EJ, et al. Implementation of a mailed faecal immunochemical test programme for colorectal cancer screening among Veterans. BMJ Open Qual. 2022;11(4). doi:10.1136/bmjoq-2022-001927
  35. Yehia BR, Greenstone CL, Hosenfeld CB, Matthews KL, Zephyrin LC. The role of VA community care in addressing health and health care disparities. Med Care. 2017;55(Suppl 9 suppl 2):S4-S5. doi:10.1097/MLR.0000000000000768
  36. Wright BN, MacDermid Wadsworth S, Wellnitz A, Eicher- Miller HA. Reaching rural veterans: a new mechanism to connect rural, low-income US Veterans with resources and improve food security. J Public Health (Oxf). 2019;41(4):714-723. doi:10.1093/pubmed/fdy203
  37. Nelson RE, Byrne TH, Suo Y, et al. Association of temporary financial assistance with housing stability among US veterans in the supportive services for veteran families program. JAMA Netw Open. 2021;4(2):e2037047. doi:10.1001/jamanetworkopen.2020.37047
  38. McDaniel JT, Albright D, Lee HY, et al. Rural–urban disparities in colorectal cancer screening among military service members and Veterans. J Mil Veteran Fam Health. 2019;5(1):40-48. doi:10.3138/jmvfh.2018-0013
  39. US Department of Veterans Affairs, Office of Rural Health. The rural veteran outreach toolkit. Updated February 12, 2025. Accessed February 18, 2025. https://www.ruralhealth.va.gov/partners/toolkit.asp
References
  1. Siegel RL, Wagle NS, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2023. CA Cancer J Clin. 2023;73(3):233-254. doi:10.3322/caac.21772
  2. Carethers JM, Doubeni CA. Causes of socioeconomic disparities in colorectal cancer and intervention framework and strategies. Gastroenterology. 2020;158(2):354-367. doi:10.1053/j.gastro.2019.10.029
  3. Murphy G, Devesa SS, Cross AJ, Inskip PD, McGlynn KA, Cook MB. Sex disparities in colorectal cancer incidence by anatomic subsite, race and age. Int J Cancer. 2011;128(7):1668-75. doi:10.1002/ijc.25481
  4. Zullig LL, Smith VA, Jackson GL, et al. Colorectal cancer statistics from the Veterans Affairs central cancer registry. Clin Colorectal Cancer. 2016;15(4):e199-e204. doi:10.1016/j.clcc.2016.04.005
  5. Lin JS, Perdue LA, Henrikson NB, Bean SI, Blasi PR. Screening for Colorectal Cancer: An Evidence Update for the US Preventive Services Task Force. 2021. U.S. Preventive Services Task Force Evidence Syntheses, formerly Systematic Evidence Reviews:Chapter 1. Agency for Healthcare Research and Quality (US); 2021. Accessed February 18, 2025. https://www.ncbi.nlm.nih.gov/books/NBK570917/
  6. Siegel RL, Fedewa SA, Anderson WF, et al. Colorectal cancer incidence patterns in the United States, 1974-2013. J Natl Cancer Inst. 2017;109(8). doi:10.1093/jnci/djw322
  7. Davidson KW, Barry MJ, Mangione CM, et al. Screening for colorectal cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325(19):1965-1977. doi:10.1001/jama.2021.6238
  8. Hines R, Markossian T, Johnson A, Dong F, Bayakly R. Geographic residency status and census tract socioeconomic status as determinants of colorectal cancer outcomes. Am J Public Health. 2014;104(3):e63-e71. doi:10.2105/AJPH.2013.301572
  9. Cauwels J. The many barriers to high-quality rural health care. 2022;(9):1-32. NEJM Catal Innov Care Deliv. Accessed April 24, 2025. https://catalyst.nejm.org/doi/pdf/10.1056/CAT.22.0254
  10. Gong G, Phillips SG, Hudson C, Curti D, Philips BU. Higher US rural mortality rates linked to socioeconomic status, physician shortages, and lack of health insurance. Health Aff (Millwood);38(12):2003-2010. doi:10.1377/hlthaff.2019.00722
  11. Aboagye JK, Kaiser HE, Hayanga AJ. Rural-urban differences in access to specialist providers of colorectal cancer care in the United States: a physician workforce issue. JAMA Surg. 2014;149(6):537-543. doi:10.1001/jamasurg.2013.5062
  12. Lyckholm LJ, Hackney MH, Smith TJ. Ethics of rural health care. Crit Rev Oncol Hematol. 2001;40(2):131-138. doi:10.1016/s1040-8428(01)00139-1
  13. Krieger N, Williams DR, Moss NE. Measuring social class in US public health research: concepts, methodologies, and guidelines. Annu Rev Public Health. 1997;18:341-378. doi:10.1146/annurev.publhealth.18.1.341
  14. Singh GK, Jemal A. Socioeconomic and racial/ethnic disparities in cancer mortality, incidence, and survival in the United States, 1950-2014: over six decades of changing patterns and widening inequalities. J Environ Public Health. 2017;2017:2819372. doi:10.1155/2017/2819372
  15. Adams SA, Zahnd WE, Ranganathan R, et al. Rural and racial disparities in colorectal cancer incidence and mortality in South Carolina, 1996 - 2016. J Rural Health. 2022;38(1):34-39. doi:10.1111/jrh.12580
  16. Rogers CR, Blackburn BE, Huntington M, et al. Rural- urban disparities in colorectal cancer survival and risk among men in Utah: a statewide population-based study. Cancer Causes Control. 2020;31(3):241-253. doi:10.1007/s10552-020-01268-2
  17. US Department of Veterans Affairs. VA Informatics and Computing Infrastructure (VINCI), VA HSR RES 13-457. https://vincicentral.vinci.med.va.gov [Source not verified]
  18. US Department of Veterans Affairs Information Resource Center. VIReC Research User Guide: PSSG Geocoded Enrollee Files, 2015 Edition. US Department of Veterans Affairs, Health Services Research & Development Service, Information Resource Center; May. 2016. [source not verified]
  19. Goldsmith HF, Puskin DS, Stiles DJ. Improving the operational definition of “rural areas” for federal programs. US Department of Health and Human Services; 1993. Accessed February 27, 2025. https://www.ruralhealthinfo.org/pdf/improving-the-operational-definition-of-rural-areas.pdf
  20. Adams MA, Kerr EA, Dominitz JA, et al. Development and validation of a new ICD-10-based screening colonoscopy overuse measure in a large integrated healthcare system: a retrospective observational study. BMJ Qual Saf. 2023;32(7):414-424. doi:10.1136/bmjqs-2021-014236
  21. Schneeweiss S, Wang PS, Avorn J, Glynn RJ. Improved comorbidity adjustment for predicting mortality in Medicare populations. Health Serv Res. 2003;38(4):1103-1120. doi:10.1111/1475-6773.00165
  22. Becker S, Ichino A. Estimation of average treatment effects based on propensity scores. The Stata Journal. 2002;2(4):358-377.
  23. Leuven E, Sianesi B. PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Statistical software components. Revised February 1, 2018. Accessed February 27, 2025. https://ideas.repec.org/c/boc/bocode/s432001.html.
  24. US Cancer Statistics Working Group. US cancer statistics data visualizations tool. Centers for Disease Control and Prevention. June 2024. Accessed February 27, 2025. https://www.cdc.gov/cancer/dataviz
  25. Cao J, Zhang S. Multiple Comparison Procedures. JAMA. 2014;312(5):543-544. doi:10.1001/jama.2014.9440
  26. Gopalani SV, Janitz AE, Martinez SA, et al. Trends in cancer incidence among American Indians and Alaska Natives and Non-Hispanic Whites in the United States, 1999-2015. Epidemiology. 2020;31(2):205-213. doi:10.1097/EDE.0000000000001140
  27. Zahnd WE, Murphy C, Knoll M, et al. The intersection of rural residence and minority race/ethnicity in cancer disparities in the United States. Int J Environ Res Public Health. 2021;18(4). doi:10.3390/ijerph18041384
  28. Blake KD, Moss JL, Gaysynsky A, Srinivasan S, Croyle RT. Making the case for investment in rural cancer control: an analysis of rural cancer incidence, mortality, and funding trends. Cancer Epidemiol Biomarkers Prev. 2017;26(7):992-997. doi:10.1158/1055-9965.EPI-17-0092
  29. Singh GK, Williams SD, Siahpush M, Mulhollen A. Socioeconomic, rural-urban, and racial inequalities in US cancer mortality: part i-all cancers and lung cancer and part iicolorectal, prostate, breast, and cervical cancers. J Cancer Epidemiol. 2011;2011:107497. doi:10.1155/2011/107497
  30. Jackson GL, Melton LD, Abbott DH, et al. Quality of nonmetastatic colorectal cancer care in the Department of Veterans Affairs. J Clin Oncol. 2010;28(19):3176-3181. doi:10.1200/JCO.2009.26.7948
  31. Yoon J, Phibbs CS, Ong MK, et al. Outcomes of veterans treated in Veterans Affairs hospitals vs non-Veterans Affairs hospitals. JAMA Netw Open. 2023;6(12):e2345898. doi:10.1001/jamanetworkopen.2023.45898
  32. Malin JL, Schneider EC, Epstein AM, Adams J, Emanuel EJ, Kahn KL. Results of the National Initiative for Cancer Care Quality: how can we improve the quality of cancer care in the United States? J Clin Oncol. 2006;24(4):626-634. doi:10.1200/JCO.2005.03.3365
  33. Levin B, Lieberman DA, McFarland B, et al. Screening and surveillance for the early detection of colorectal cancer and adenomatous polyps, 2008: a joint guideline from the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology. Gastroenterology. 2008;134(5):1570-1595. doi:10.1053/j.gastro.2008.02.002
  34. Deeds SA, Moore CB, Gunnink EJ, et al. Implementation of a mailed faecal immunochemical test programme for colorectal cancer screening among Veterans. BMJ Open Qual. 2022;11(4). doi:10.1136/bmjoq-2022-001927
  35. Yehia BR, Greenstone CL, Hosenfeld CB, Matthews KL, Zephyrin LC. The role of VA community care in addressing health and health care disparities. Med Care. 2017;55(Suppl 9 suppl 2):S4-S5. doi:10.1097/MLR.0000000000000768
  36. Wright BN, MacDermid Wadsworth S, Wellnitz A, Eicher- Miller HA. Reaching rural veterans: a new mechanism to connect rural, low-income US Veterans with resources and improve food security. J Public Health (Oxf). 2019;41(4):714-723. doi:10.1093/pubmed/fdy203
  37. Nelson RE, Byrne TH, Suo Y, et al. Association of temporary financial assistance with housing stability among US veterans in the supportive services for veteran families program. JAMA Netw Open. 2021;4(2):e2037047. doi:10.1001/jamanetworkopen.2020.37047
  38. McDaniel JT, Albright D, Lee HY, et al. Rural–urban disparities in colorectal cancer screening among military service members and Veterans. J Mil Veteran Fam Health. 2019;5(1):40-48. doi:10.3138/jmvfh.2018-0013
  39. US Department of Veterans Affairs, Office of Rural Health. The rural veteran outreach toolkit. Updated February 12, 2025. Accessed February 18, 2025. https://www.ruralhealth.va.gov/partners/toolkit.asp
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Continuous Glucose Monitoring vs Fingerstick Monitoring for Hemoglobin A1c Control in Veterans

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Continuous Glucose Monitoring vs Fingerstick Monitoring for Hemoglobin A1c Control in Veterans

In the United States, 1 in 4 veterans lives with type 2 diabetes mellitus (T2DM), double the rate of the general population.1 Medications are important for the treatment of T2DM and preventing complications that may develop if not properly managed. Common classes of medications for diabetes include biguanides, sodiumglucose cotransporter-2 (SGLT-2) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, dipeptidyl peptidase-4 inhibitors, thiazolidinediones, sulfonylureas, and insulin. The selection of treatment depends on patient-specific factors including hemoglobin A1c (HbA1c) goal, potential effects on weight, risk of hypoglycemia, and comorbidities such as atherosclerotic cardiovascular disease, heart failure, or chronic kidney disease.2

HbA1c level reflects the mean blood glucose over the previous 3 months and serves as an indication of diabetes control. In patients with diabetes, it is recommended that HbA1c is checked ≥ 2 times annually for those meeting treatment goals, or more often if the patient needs to adjust medications to reach their HbA1c goal. The goal HbA1c level for most adults with diabetes is < 7%.3 This target can be adjusted based on age, comorbidities, or other patient factors. It is generally recommended that frequent glucose monitoring is not needed for patients with T2DM who are only taking oral agents and/or noninsulin injectables. However, for those on insulin regimens, it is advised to monitor glucose closely, with even more frequent testing for those with an intensive insulin regimen.3

Most patients with diabetes use fingerstick testing to self-monitor their blood glucose. However, continuous glucose monitors (CGMs) are becoming widely available and offer a solution to those who do not have the ability to check their glucose multiple times a day and throughout the night. The American Diabetes Association recommends that the frequency and timing of blood glucose monitoring, or the consideration of CGM use, should be based on the specific needs and goals of each patient.3 Guidelines also encourage those on intensive insulin regimens to check glucose levels when fasting, before and after meals, prior to exercise, and when hypoglycemia or hyperglycemia is suspected. Frequent testing can become a burden for patients, whereas once a CGM sensor is placed, it can be worn for 10 to 14 days. CGMs are also capable of transmitting glucose readings every 1 to 15 minutes to a receiver or mobile phone, allowing for further adaptability to a patient’s lifestyle.3

CGMs work by measuring the interstitial glucose with a small filament sensor and have demonstrated accuracy when compared to blood glucose readings. The ability of a CGM to accurately reflect HbA1c levels is a potential benefit, reducing the need for frequent testing to determine whether patients have achieved glycemic control.4 Another benefit of a CGM is the ease of sharing data; patient accounts can be linked with a health care site, allowing clinicians to access glucose data even if the patient is not able to be seen in clinic. This allows health care practitioners (HCPs) to more efficiently tailor medications and optimize regimens based on patient-specific data that was not available by fingerstick testing alone.

Vigersky and colleagues provided one of the few studies on the long-term effects of CGM in patients managing T2DM through diet and exercise alone, oral medications, or basal insulin and found significant improvement in HbA1c after only 3 months of CGM use.5

An important aspect of CGM use is the ability to alert the patient to low blood glucose readings, which can be dangerous for those unaware of hypoglycemia. Many studies have investigated the association between CGM use and acute metabolic events, demonstrating the potential for CGMs to prevent these emergencies. Karter and colleagues found a reduction in emergency department visits and hospitalizations for hypoglycemia associated with the use of CGMs in patients with type 1 DM (T1DM) and T2DM.6

There have been few studies on the use of CGM in veterans. Langford and colleagues found a reduction of HbA1c among veterans with T2DM using CGMs. However, > 50% of the patients in the study were not receiving insulin therapy, which currently is a US Department of Veterans Affairs (VA) CGM criteria for use.7 While current studies provide evidence that supports improvement in HbA1c levels with the use of CGMs, data are lacking for veterans with T2DM taking insulin. There is also minimal research that indicates which patients should be offered a CGM. The objective of this study was to evaluate glycemic control in veterans with T2DM on insulin using a CGM who were previously monitoring blood glucose with fingerstick testing. Secondary endpoints were explored to identify subgroups that may benefit from a CGM and other potential advantages of CGMs.

Methods

This was a retrospective study of veterans who transitioned from fingerstick testing to CGM for glucose monitoring. Each veteran served as their own control to limit confounding variables when comparing HbA1c levels. Veterans with an active or suspended CGM order were identified by reviewing outpatient prescription data. All data collection and analysis were done within the Veterans Affairs Sioux Falls Health Care System.

The primary objective of this study was to assess glycemic control from the use of a CGM by evaluating the change in HbA1c after transitioning to a CGM compared to the change in HbA1c with standard fingerstick monitoring. Three HbA1c values were collected for each veteran: before starting CGM, at initiation, and following CGM initiation (Figure 1). CGM start date was the date the CGM prescription order was placed. The pre-CGM HbA1c level was ≥ 1 year prior to the CGM start date or the HbA1c closest to 1 year. The start CGM HbA1c level was within 3 months before or 1 month after the CGM start date. The post-CGM HbA1c level was the most recent time of data collection and at least 6 months after CGM initiation. The change in HbA1c from fingerstick glucose monitoring was the difference between the pre-CGM and start CGM values. The change in HbA1c from use of a CGM was the difference between start CGM and post-CGM values, which were compared to determine HbA1c reduction from CGM use.

Abbreviations: CGM, continuous glucose monitor; HbA1c, hemoglobin A1c.

This study also explored secondary outcomes including changes in HbA1c by prescriber type, differences in HbA1c reduction based on age, and changes in diabetes medications, including total daily insulin doses. For secondary outcomes, diabetes medication information and the total daily dose of insulin were gathered at the start of CGM use and at the time of data collection. The most recent CGM order prescribed was also collected.

Veterans were included if they were aged ≥ 18 years, had an active order for a CGM, T2DM diagnosis, an insulin prescription, and previously used test strips for glucose monitoring. Patients with T1DM, those who accessed CGMs or care in the community, and patients without HbA1c values pre-CGM, were excluded.

Statistical Analysis

The primary endpoint of change in HbA1c level before and after CGM use was compared using a paired t test. A 0.5% change in HbA1c was considered clinically significant, as suggested in other studies.8,9 P < .05 was considered statistically significant. Analysis for continuous baseline characteristics, including age and total daily insulin, were reported as mean values. Nominal characteristics including sex, race, diabetes medications, and prescriber type are reported as percentages.

Results

A total of 402 veterans were identified with an active CGM at the time of initial data collection in January 2024 and 175 met inclusion criteria. Sixty patients were excluded due to diabetes managed through a community HCP, 38 had T1DM, and 129 lacked HbA1c within all specified time periods. The 175 veterans were randomized, and 150 were selected to perform a chart review for data collection. The mean age was 70 years, most were male and identified as White (Table 1). The majority of patients were managed by endocrinology (53.3%), followed by primary care (24.0%), and pharmacy (22.7%) (Table 2). The mean baseline HbA1c was 8.6%.

The difference in HbA1c before and after use of CGM was -0.97% (P = .0001). Prior to use of a CGM the change in HbA1c was minimal, with an increase of 0.003% with the use of selfmonitoring glucose. After use of a CGM, HbA1c decreased by 0.971%. This reduction in HbA1c would also be considered clinically significant as the change was > 0.5%. The mean pre-, at start, and post-CGM HbA1c levels were 8.6%, 8.6%, and 7.6%, respectively (Figure 2). Pharmacy prescribers had a 0.7% reduction in HbA1c post-CGM, the least of all prescribers. While most age groups saw a reduction in HbA1c, those aged ≥ 80 years had an increase of 0.18% (Table 3). There was an overall mean reduction in insulin of 22 units, which was similar between all prescribers.

Abbreviation: CGM, continuous glucose monitor.

Discussion

The primary endpoint of difference in change of HbA1c before and after CGM use was found to be statistically and clinically significant, with a nearly 1% reduction in HbA1c, which was similar to the reduction found by Vigersky and colleagues. 5 Across all prescribers, post-CGM HbA1c levels were similar; however, patients with CGM prescribed by pharmacists had the smallest change in HbA1c. VA pharmacists primarily assess veterans taking insulin who have HbA1c levels that are below the goal with the aim of decreasing insulin to reduce the risk of hypoglycemia, which could result in increased HbA1c levels. This may also explain the observed increase in post-CGM HbA1c levels in patients aged ≥ 80 years. Patients under the care of pharmacists also had baseline mean HbA1c levels that were lower than primary care and endocrinology prescribers and were closer to their HbA1c goal at baseline, which likely was reflected in the smaller reduction in post-CGM HbA1c level.

While there was a decrease in HbA1c levels with CGM use, there were also changes to medications during this timeframe that also may have impacted HbA1c levels. The most common diabetes medications started during CGM use were GLP-1 agonists and SGLT2-inhibitors. Additionally, there was a reduction in the total daily dose of insulin in the study population. These results demonstrate the potential benefits of CGMs for prescribers who take advantage of the CGM glucose data available to assist with medication adjustments. Another consideration for differences in changes of HbA1c among prescriber types is the opportunity for more frequent follow- up visits with pharmacy or endocrinology compared with primary care. If veterans are followed more closely, it may be associated with improved HbA1c control. Further research investigating changes in HbA1c levels based on followup frequency may be useful.

Strengths and Limitations

The crossover design was a strength of this study. This design reduced confounding variables by having veterans serve as their own controls. In addition, the collection of multiple secondary outcomes adds to the knowledge base for future studies. This study focused on a unique population of veterans with T2DM who were taking insulin, an area that previously had very little data available to determine the benefits of CGM use.

Although the use of a CGM showed statistical significance in lowering HbA1c, many veterans were started on new diabetes medication during the period of CGM use, which also likely contributed to the reduction in HbA1c and may have confounded the results. The study was limited by its small population size due to time constraints of chart reviews and the limited generalizability of results outside of the VA system. The majority of patients were from a single site, male and identified as White, which may not be reflective of other VA and community health care systems. It was also noted that the time from the initiation of CGM use to the most recent HbA1c level varied from 6 months to several years. Additionally, veterans managed by community-based HCPs with complex diabetes cases were excluded.

Conclusions

This study demonstrated a clinically and statistically significant reduction in HbA1c with the use of a CGM compared to fingerstick monitoring in veterans with T2DM who were being treated with insulin. The change in post-CGM HbA1c levels across prescribers was similar. In the subgroup analysis of change in HbA1c among age groups, there was a lower HbA1c reduction in individuals aged ≥ 80 years. The results from this study support the idea that CGM use may be beneficial for patients who require a reduction in HbA1c by allowing more precise adjustments to medications and optimization of therapy, as well as the potential to reduce insulin requirements, which is especially valuable in the older adult veteran population.

References
  1. US Department of Veterans Affairs. VA supports veterans who have type 2 diabetes. VA News. Accessed September 30, 2024. https://news.va.gov/107579/va-supports-veterans-who-have-type-2-diabetes/
  2. ElSayed NA, Aleppo G, Aroda VR, et al. 9. Pharmacologic approaches to glycemic treatment: standards of care in diabetes-2023. Diabetes Care. 2023;46(Suppl 1):S140- S157. doi:10.2337/dc23-S009
  3. ElSayed NA, Aleppo G, Aroda VR, et al. 6. Glycemic targets: standards of care in diabetes-2023. Diabetes Care. 2023;46(Suppl 1):S97-S110. doi:10.2337/dc23-S006
  4. Miller E, Gavin JR, Kruger DF, Brunton SA. Continuous glucose monitoring: optimizing diabetes care: executive summary. Clin Diabetes. 2022;40(4):394-398. doi:10.2337/cd22-0043
  5. Vigersky RA, Fonda SJ, Chellappa M, Walker MS, Ehrhardt NM. Short- and long-term effects of real-time continuous glucose monitoring in patients with type 2 diabetes. Diabetes Care. 2012;35(1):32-38. doi:10.2337/dc11-1438
  6. Karter AJ, Parker MM, Moffet HH, Gilliam LK, Dlott R. Association of real-time continuous glucose monitoring with glycemic control and acute metabolic events among patients with insulin-treated diabetes. JAMA. 2021;325(22):2273-2284. doi:10.1001/JAMA.2021.6530
  7. Langford SN, Lane M, Karounos D. Continuous blood glucose monitoring outcomes in veterans with type 2 diabetes. Fed Pract. 2021;38(Suppl 4):S14-S17. doi:10.12788/fp.0189
  8. Radin MS. Pitfalls in hemoglobin A1c measurement: when results may be misleading. J Gen Intern Med. 2014;29(2):388-394. doi:10.1007/s11606-013-2595-x.
  9. Little RR, Rohlfing CL, Sacks DB; National Glycohemoglobin Standardization Program (NGSP) steering committee. Status of hemoglobin A1c measurement and goals for improvement: from chaos to order for improving diabetes care. Clin Chem. 2011;57(2):205-214. doi:10.1373/clinchem.2010.148841
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Kelsey Floerchinger, PharmDa; Kelley Oehlke, PharmD, BCACPa; Scott Bebensee, PharmD, BCPSa; Austin Hansen, PharmDa; Kelsey Oye, PharmD, BCACP, CDCESa

Correspondence: Kelsey Floerchinger ([email protected])

Author affiliations: aVeterans Affairs Sioux Falls Health Care System, South Dakota

Author disclosures: The authors report no actual or potential conflict of interest with regard to this article.

Disclaimer: The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the official position or policy of the Defense Health Agency, US Department of Defense, the US Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

Fed Pract. 2024;41(suppl 5). Published online November 15. doi:10.12788/fp.0525

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Kelsey Floerchinger, PharmDa; Kelley Oehlke, PharmD, BCACPa; Scott Bebensee, PharmD, BCPSa; Austin Hansen, PharmDa; Kelsey Oye, PharmD, BCACP, CDCESa

Correspondence: Kelsey Floerchinger ([email protected])

Author affiliations: aVeterans Affairs Sioux Falls Health Care System, South Dakota

Author disclosures: The authors report no actual or potential conflict of interest with regard to this article.

Disclaimer: The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the official position or policy of the Defense Health Agency, US Department of Defense, the US Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

Fed Pract. 2024;41(suppl 5). Published online November 15. doi:10.12788/fp.0525

Author and Disclosure Information

Kelsey Floerchinger, PharmDa; Kelley Oehlke, PharmD, BCACPa; Scott Bebensee, PharmD, BCPSa; Austin Hansen, PharmDa; Kelsey Oye, PharmD, BCACP, CDCESa

Correspondence: Kelsey Floerchinger ([email protected])

Author affiliations: aVeterans Affairs Sioux Falls Health Care System, South Dakota

Author disclosures: The authors report no actual or potential conflict of interest with regard to this article.

Disclaimer: The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the official position or policy of the Defense Health Agency, US Department of Defense, the US Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

Fed Pract. 2024;41(suppl 5). Published online November 15. doi:10.12788/fp.0525

Article PDF
Article PDF

In the United States, 1 in 4 veterans lives with type 2 diabetes mellitus (T2DM), double the rate of the general population.1 Medications are important for the treatment of T2DM and preventing complications that may develop if not properly managed. Common classes of medications for diabetes include biguanides, sodiumglucose cotransporter-2 (SGLT-2) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, dipeptidyl peptidase-4 inhibitors, thiazolidinediones, sulfonylureas, and insulin. The selection of treatment depends on patient-specific factors including hemoglobin A1c (HbA1c) goal, potential effects on weight, risk of hypoglycemia, and comorbidities such as atherosclerotic cardiovascular disease, heart failure, or chronic kidney disease.2

HbA1c level reflects the mean blood glucose over the previous 3 months and serves as an indication of diabetes control. In patients with diabetes, it is recommended that HbA1c is checked ≥ 2 times annually for those meeting treatment goals, or more often if the patient needs to adjust medications to reach their HbA1c goal. The goal HbA1c level for most adults with diabetes is < 7%.3 This target can be adjusted based on age, comorbidities, or other patient factors. It is generally recommended that frequent glucose monitoring is not needed for patients with T2DM who are only taking oral agents and/or noninsulin injectables. However, for those on insulin regimens, it is advised to monitor glucose closely, with even more frequent testing for those with an intensive insulin regimen.3

Most patients with diabetes use fingerstick testing to self-monitor their blood glucose. However, continuous glucose monitors (CGMs) are becoming widely available and offer a solution to those who do not have the ability to check their glucose multiple times a day and throughout the night. The American Diabetes Association recommends that the frequency and timing of blood glucose monitoring, or the consideration of CGM use, should be based on the specific needs and goals of each patient.3 Guidelines also encourage those on intensive insulin regimens to check glucose levels when fasting, before and after meals, prior to exercise, and when hypoglycemia or hyperglycemia is suspected. Frequent testing can become a burden for patients, whereas once a CGM sensor is placed, it can be worn for 10 to 14 days. CGMs are also capable of transmitting glucose readings every 1 to 15 minutes to a receiver or mobile phone, allowing for further adaptability to a patient’s lifestyle.3

CGMs work by measuring the interstitial glucose with a small filament sensor and have demonstrated accuracy when compared to blood glucose readings. The ability of a CGM to accurately reflect HbA1c levels is a potential benefit, reducing the need for frequent testing to determine whether patients have achieved glycemic control.4 Another benefit of a CGM is the ease of sharing data; patient accounts can be linked with a health care site, allowing clinicians to access glucose data even if the patient is not able to be seen in clinic. This allows health care practitioners (HCPs) to more efficiently tailor medications and optimize regimens based on patient-specific data that was not available by fingerstick testing alone.

Vigersky and colleagues provided one of the few studies on the long-term effects of CGM in patients managing T2DM through diet and exercise alone, oral medications, or basal insulin and found significant improvement in HbA1c after only 3 months of CGM use.5

An important aspect of CGM use is the ability to alert the patient to low blood glucose readings, which can be dangerous for those unaware of hypoglycemia. Many studies have investigated the association between CGM use and acute metabolic events, demonstrating the potential for CGMs to prevent these emergencies. Karter and colleagues found a reduction in emergency department visits and hospitalizations for hypoglycemia associated with the use of CGMs in patients with type 1 DM (T1DM) and T2DM.6

There have been few studies on the use of CGM in veterans. Langford and colleagues found a reduction of HbA1c among veterans with T2DM using CGMs. However, > 50% of the patients in the study were not receiving insulin therapy, which currently is a US Department of Veterans Affairs (VA) CGM criteria for use.7 While current studies provide evidence that supports improvement in HbA1c levels with the use of CGMs, data are lacking for veterans with T2DM taking insulin. There is also minimal research that indicates which patients should be offered a CGM. The objective of this study was to evaluate glycemic control in veterans with T2DM on insulin using a CGM who were previously monitoring blood glucose with fingerstick testing. Secondary endpoints were explored to identify subgroups that may benefit from a CGM and other potential advantages of CGMs.

Methods

This was a retrospective study of veterans who transitioned from fingerstick testing to CGM for glucose monitoring. Each veteran served as their own control to limit confounding variables when comparing HbA1c levels. Veterans with an active or suspended CGM order were identified by reviewing outpatient prescription data. All data collection and analysis were done within the Veterans Affairs Sioux Falls Health Care System.

The primary objective of this study was to assess glycemic control from the use of a CGM by evaluating the change in HbA1c after transitioning to a CGM compared to the change in HbA1c with standard fingerstick monitoring. Three HbA1c values were collected for each veteran: before starting CGM, at initiation, and following CGM initiation (Figure 1). CGM start date was the date the CGM prescription order was placed. The pre-CGM HbA1c level was ≥ 1 year prior to the CGM start date or the HbA1c closest to 1 year. The start CGM HbA1c level was within 3 months before or 1 month after the CGM start date. The post-CGM HbA1c level was the most recent time of data collection and at least 6 months after CGM initiation. The change in HbA1c from fingerstick glucose monitoring was the difference between the pre-CGM and start CGM values. The change in HbA1c from use of a CGM was the difference between start CGM and post-CGM values, which were compared to determine HbA1c reduction from CGM use.

Abbreviations: CGM, continuous glucose monitor; HbA1c, hemoglobin A1c.

This study also explored secondary outcomes including changes in HbA1c by prescriber type, differences in HbA1c reduction based on age, and changes in diabetes medications, including total daily insulin doses. For secondary outcomes, diabetes medication information and the total daily dose of insulin were gathered at the start of CGM use and at the time of data collection. The most recent CGM order prescribed was also collected.

Veterans were included if they were aged ≥ 18 years, had an active order for a CGM, T2DM diagnosis, an insulin prescription, and previously used test strips for glucose monitoring. Patients with T1DM, those who accessed CGMs or care in the community, and patients without HbA1c values pre-CGM, were excluded.

Statistical Analysis

The primary endpoint of change in HbA1c level before and after CGM use was compared using a paired t test. A 0.5% change in HbA1c was considered clinically significant, as suggested in other studies.8,9 P < .05 was considered statistically significant. Analysis for continuous baseline characteristics, including age and total daily insulin, were reported as mean values. Nominal characteristics including sex, race, diabetes medications, and prescriber type are reported as percentages.

Results

A total of 402 veterans were identified with an active CGM at the time of initial data collection in January 2024 and 175 met inclusion criteria. Sixty patients were excluded due to diabetes managed through a community HCP, 38 had T1DM, and 129 lacked HbA1c within all specified time periods. The 175 veterans were randomized, and 150 were selected to perform a chart review for data collection. The mean age was 70 years, most were male and identified as White (Table 1). The majority of patients were managed by endocrinology (53.3%), followed by primary care (24.0%), and pharmacy (22.7%) (Table 2). The mean baseline HbA1c was 8.6%.

The difference in HbA1c before and after use of CGM was -0.97% (P = .0001). Prior to use of a CGM the change in HbA1c was minimal, with an increase of 0.003% with the use of selfmonitoring glucose. After use of a CGM, HbA1c decreased by 0.971%. This reduction in HbA1c would also be considered clinically significant as the change was > 0.5%. The mean pre-, at start, and post-CGM HbA1c levels were 8.6%, 8.6%, and 7.6%, respectively (Figure 2). Pharmacy prescribers had a 0.7% reduction in HbA1c post-CGM, the least of all prescribers. While most age groups saw a reduction in HbA1c, those aged ≥ 80 years had an increase of 0.18% (Table 3). There was an overall mean reduction in insulin of 22 units, which was similar between all prescribers.

Abbreviation: CGM, continuous glucose monitor.

Discussion

The primary endpoint of difference in change of HbA1c before and after CGM use was found to be statistically and clinically significant, with a nearly 1% reduction in HbA1c, which was similar to the reduction found by Vigersky and colleagues. 5 Across all prescribers, post-CGM HbA1c levels were similar; however, patients with CGM prescribed by pharmacists had the smallest change in HbA1c. VA pharmacists primarily assess veterans taking insulin who have HbA1c levels that are below the goal with the aim of decreasing insulin to reduce the risk of hypoglycemia, which could result in increased HbA1c levels. This may also explain the observed increase in post-CGM HbA1c levels in patients aged ≥ 80 years. Patients under the care of pharmacists also had baseline mean HbA1c levels that were lower than primary care and endocrinology prescribers and were closer to their HbA1c goal at baseline, which likely was reflected in the smaller reduction in post-CGM HbA1c level.

While there was a decrease in HbA1c levels with CGM use, there were also changes to medications during this timeframe that also may have impacted HbA1c levels. The most common diabetes medications started during CGM use were GLP-1 agonists and SGLT2-inhibitors. Additionally, there was a reduction in the total daily dose of insulin in the study population. These results demonstrate the potential benefits of CGMs for prescribers who take advantage of the CGM glucose data available to assist with medication adjustments. Another consideration for differences in changes of HbA1c among prescriber types is the opportunity for more frequent follow- up visits with pharmacy or endocrinology compared with primary care. If veterans are followed more closely, it may be associated with improved HbA1c control. Further research investigating changes in HbA1c levels based on followup frequency may be useful.

Strengths and Limitations

The crossover design was a strength of this study. This design reduced confounding variables by having veterans serve as their own controls. In addition, the collection of multiple secondary outcomes adds to the knowledge base for future studies. This study focused on a unique population of veterans with T2DM who were taking insulin, an area that previously had very little data available to determine the benefits of CGM use.

Although the use of a CGM showed statistical significance in lowering HbA1c, many veterans were started on new diabetes medication during the period of CGM use, which also likely contributed to the reduction in HbA1c and may have confounded the results. The study was limited by its small population size due to time constraints of chart reviews and the limited generalizability of results outside of the VA system. The majority of patients were from a single site, male and identified as White, which may not be reflective of other VA and community health care systems. It was also noted that the time from the initiation of CGM use to the most recent HbA1c level varied from 6 months to several years. Additionally, veterans managed by community-based HCPs with complex diabetes cases were excluded.

Conclusions

This study demonstrated a clinically and statistically significant reduction in HbA1c with the use of a CGM compared to fingerstick monitoring in veterans with T2DM who were being treated with insulin. The change in post-CGM HbA1c levels across prescribers was similar. In the subgroup analysis of change in HbA1c among age groups, there was a lower HbA1c reduction in individuals aged ≥ 80 years. The results from this study support the idea that CGM use may be beneficial for patients who require a reduction in HbA1c by allowing more precise adjustments to medications and optimization of therapy, as well as the potential to reduce insulin requirements, which is especially valuable in the older adult veteran population.

In the United States, 1 in 4 veterans lives with type 2 diabetes mellitus (T2DM), double the rate of the general population.1 Medications are important for the treatment of T2DM and preventing complications that may develop if not properly managed. Common classes of medications for diabetes include biguanides, sodiumglucose cotransporter-2 (SGLT-2) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, dipeptidyl peptidase-4 inhibitors, thiazolidinediones, sulfonylureas, and insulin. The selection of treatment depends on patient-specific factors including hemoglobin A1c (HbA1c) goal, potential effects on weight, risk of hypoglycemia, and comorbidities such as atherosclerotic cardiovascular disease, heart failure, or chronic kidney disease.2

HbA1c level reflects the mean blood glucose over the previous 3 months and serves as an indication of diabetes control. In patients with diabetes, it is recommended that HbA1c is checked ≥ 2 times annually for those meeting treatment goals, or more often if the patient needs to adjust medications to reach their HbA1c goal. The goal HbA1c level for most adults with diabetes is < 7%.3 This target can be adjusted based on age, comorbidities, or other patient factors. It is generally recommended that frequent glucose monitoring is not needed for patients with T2DM who are only taking oral agents and/or noninsulin injectables. However, for those on insulin regimens, it is advised to monitor glucose closely, with even more frequent testing for those with an intensive insulin regimen.3

Most patients with diabetes use fingerstick testing to self-monitor their blood glucose. However, continuous glucose monitors (CGMs) are becoming widely available and offer a solution to those who do not have the ability to check their glucose multiple times a day and throughout the night. The American Diabetes Association recommends that the frequency and timing of blood glucose monitoring, or the consideration of CGM use, should be based on the specific needs and goals of each patient.3 Guidelines also encourage those on intensive insulin regimens to check glucose levels when fasting, before and after meals, prior to exercise, and when hypoglycemia or hyperglycemia is suspected. Frequent testing can become a burden for patients, whereas once a CGM sensor is placed, it can be worn for 10 to 14 days. CGMs are also capable of transmitting glucose readings every 1 to 15 minutes to a receiver or mobile phone, allowing for further adaptability to a patient’s lifestyle.3

CGMs work by measuring the interstitial glucose with a small filament sensor and have demonstrated accuracy when compared to blood glucose readings. The ability of a CGM to accurately reflect HbA1c levels is a potential benefit, reducing the need for frequent testing to determine whether patients have achieved glycemic control.4 Another benefit of a CGM is the ease of sharing data; patient accounts can be linked with a health care site, allowing clinicians to access glucose data even if the patient is not able to be seen in clinic. This allows health care practitioners (HCPs) to more efficiently tailor medications and optimize regimens based on patient-specific data that was not available by fingerstick testing alone.

Vigersky and colleagues provided one of the few studies on the long-term effects of CGM in patients managing T2DM through diet and exercise alone, oral medications, or basal insulin and found significant improvement in HbA1c after only 3 months of CGM use.5

An important aspect of CGM use is the ability to alert the patient to low blood glucose readings, which can be dangerous for those unaware of hypoglycemia. Many studies have investigated the association between CGM use and acute metabolic events, demonstrating the potential for CGMs to prevent these emergencies. Karter and colleagues found a reduction in emergency department visits and hospitalizations for hypoglycemia associated with the use of CGMs in patients with type 1 DM (T1DM) and T2DM.6

There have been few studies on the use of CGM in veterans. Langford and colleagues found a reduction of HbA1c among veterans with T2DM using CGMs. However, > 50% of the patients in the study were not receiving insulin therapy, which currently is a US Department of Veterans Affairs (VA) CGM criteria for use.7 While current studies provide evidence that supports improvement in HbA1c levels with the use of CGMs, data are lacking for veterans with T2DM taking insulin. There is also minimal research that indicates which patients should be offered a CGM. The objective of this study was to evaluate glycemic control in veterans with T2DM on insulin using a CGM who were previously monitoring blood glucose with fingerstick testing. Secondary endpoints were explored to identify subgroups that may benefit from a CGM and other potential advantages of CGMs.

Methods

This was a retrospective study of veterans who transitioned from fingerstick testing to CGM for glucose monitoring. Each veteran served as their own control to limit confounding variables when comparing HbA1c levels. Veterans with an active or suspended CGM order were identified by reviewing outpatient prescription data. All data collection and analysis were done within the Veterans Affairs Sioux Falls Health Care System.

The primary objective of this study was to assess glycemic control from the use of a CGM by evaluating the change in HbA1c after transitioning to a CGM compared to the change in HbA1c with standard fingerstick monitoring. Three HbA1c values were collected for each veteran: before starting CGM, at initiation, and following CGM initiation (Figure 1). CGM start date was the date the CGM prescription order was placed. The pre-CGM HbA1c level was ≥ 1 year prior to the CGM start date or the HbA1c closest to 1 year. The start CGM HbA1c level was within 3 months before or 1 month after the CGM start date. The post-CGM HbA1c level was the most recent time of data collection and at least 6 months after CGM initiation. The change in HbA1c from fingerstick glucose monitoring was the difference between the pre-CGM and start CGM values. The change in HbA1c from use of a CGM was the difference between start CGM and post-CGM values, which were compared to determine HbA1c reduction from CGM use.

Abbreviations: CGM, continuous glucose monitor; HbA1c, hemoglobin A1c.

This study also explored secondary outcomes including changes in HbA1c by prescriber type, differences in HbA1c reduction based on age, and changes in diabetes medications, including total daily insulin doses. For secondary outcomes, diabetes medication information and the total daily dose of insulin were gathered at the start of CGM use and at the time of data collection. The most recent CGM order prescribed was also collected.

Veterans were included if they were aged ≥ 18 years, had an active order for a CGM, T2DM diagnosis, an insulin prescription, and previously used test strips for glucose monitoring. Patients with T1DM, those who accessed CGMs or care in the community, and patients without HbA1c values pre-CGM, were excluded.

Statistical Analysis

The primary endpoint of change in HbA1c level before and after CGM use was compared using a paired t test. A 0.5% change in HbA1c was considered clinically significant, as suggested in other studies.8,9 P < .05 was considered statistically significant. Analysis for continuous baseline characteristics, including age and total daily insulin, were reported as mean values. Nominal characteristics including sex, race, diabetes medications, and prescriber type are reported as percentages.

Results

A total of 402 veterans were identified with an active CGM at the time of initial data collection in January 2024 and 175 met inclusion criteria. Sixty patients were excluded due to diabetes managed through a community HCP, 38 had T1DM, and 129 lacked HbA1c within all specified time periods. The 175 veterans were randomized, and 150 were selected to perform a chart review for data collection. The mean age was 70 years, most were male and identified as White (Table 1). The majority of patients were managed by endocrinology (53.3%), followed by primary care (24.0%), and pharmacy (22.7%) (Table 2). The mean baseline HbA1c was 8.6%.

The difference in HbA1c before and after use of CGM was -0.97% (P = .0001). Prior to use of a CGM the change in HbA1c was minimal, with an increase of 0.003% with the use of selfmonitoring glucose. After use of a CGM, HbA1c decreased by 0.971%. This reduction in HbA1c would also be considered clinically significant as the change was > 0.5%. The mean pre-, at start, and post-CGM HbA1c levels were 8.6%, 8.6%, and 7.6%, respectively (Figure 2). Pharmacy prescribers had a 0.7% reduction in HbA1c post-CGM, the least of all prescribers. While most age groups saw a reduction in HbA1c, those aged ≥ 80 years had an increase of 0.18% (Table 3). There was an overall mean reduction in insulin of 22 units, which was similar between all prescribers.

Abbreviation: CGM, continuous glucose monitor.

Discussion

The primary endpoint of difference in change of HbA1c before and after CGM use was found to be statistically and clinically significant, with a nearly 1% reduction in HbA1c, which was similar to the reduction found by Vigersky and colleagues. 5 Across all prescribers, post-CGM HbA1c levels were similar; however, patients with CGM prescribed by pharmacists had the smallest change in HbA1c. VA pharmacists primarily assess veterans taking insulin who have HbA1c levels that are below the goal with the aim of decreasing insulin to reduce the risk of hypoglycemia, which could result in increased HbA1c levels. This may also explain the observed increase in post-CGM HbA1c levels in patients aged ≥ 80 years. Patients under the care of pharmacists also had baseline mean HbA1c levels that were lower than primary care and endocrinology prescribers and were closer to their HbA1c goal at baseline, which likely was reflected in the smaller reduction in post-CGM HbA1c level.

While there was a decrease in HbA1c levels with CGM use, there were also changes to medications during this timeframe that also may have impacted HbA1c levels. The most common diabetes medications started during CGM use were GLP-1 agonists and SGLT2-inhibitors. Additionally, there was a reduction in the total daily dose of insulin in the study population. These results demonstrate the potential benefits of CGMs for prescribers who take advantage of the CGM glucose data available to assist with medication adjustments. Another consideration for differences in changes of HbA1c among prescriber types is the opportunity for more frequent follow- up visits with pharmacy or endocrinology compared with primary care. If veterans are followed more closely, it may be associated with improved HbA1c control. Further research investigating changes in HbA1c levels based on followup frequency may be useful.

Strengths and Limitations

The crossover design was a strength of this study. This design reduced confounding variables by having veterans serve as their own controls. In addition, the collection of multiple secondary outcomes adds to the knowledge base for future studies. This study focused on a unique population of veterans with T2DM who were taking insulin, an area that previously had very little data available to determine the benefits of CGM use.

Although the use of a CGM showed statistical significance in lowering HbA1c, many veterans were started on new diabetes medication during the period of CGM use, which also likely contributed to the reduction in HbA1c and may have confounded the results. The study was limited by its small population size due to time constraints of chart reviews and the limited generalizability of results outside of the VA system. The majority of patients were from a single site, male and identified as White, which may not be reflective of other VA and community health care systems. It was also noted that the time from the initiation of CGM use to the most recent HbA1c level varied from 6 months to several years. Additionally, veterans managed by community-based HCPs with complex diabetes cases were excluded.

Conclusions

This study demonstrated a clinically and statistically significant reduction in HbA1c with the use of a CGM compared to fingerstick monitoring in veterans with T2DM who were being treated with insulin. The change in post-CGM HbA1c levels across prescribers was similar. In the subgroup analysis of change in HbA1c among age groups, there was a lower HbA1c reduction in individuals aged ≥ 80 years. The results from this study support the idea that CGM use may be beneficial for patients who require a reduction in HbA1c by allowing more precise adjustments to medications and optimization of therapy, as well as the potential to reduce insulin requirements, which is especially valuable in the older adult veteran population.

References
  1. US Department of Veterans Affairs. VA supports veterans who have type 2 diabetes. VA News. Accessed September 30, 2024. https://news.va.gov/107579/va-supports-veterans-who-have-type-2-diabetes/
  2. ElSayed NA, Aleppo G, Aroda VR, et al. 9. Pharmacologic approaches to glycemic treatment: standards of care in diabetes-2023. Diabetes Care. 2023;46(Suppl 1):S140- S157. doi:10.2337/dc23-S009
  3. ElSayed NA, Aleppo G, Aroda VR, et al. 6. Glycemic targets: standards of care in diabetes-2023. Diabetes Care. 2023;46(Suppl 1):S97-S110. doi:10.2337/dc23-S006
  4. Miller E, Gavin JR, Kruger DF, Brunton SA. Continuous glucose monitoring: optimizing diabetes care: executive summary. Clin Diabetes. 2022;40(4):394-398. doi:10.2337/cd22-0043
  5. Vigersky RA, Fonda SJ, Chellappa M, Walker MS, Ehrhardt NM. Short- and long-term effects of real-time continuous glucose monitoring in patients with type 2 diabetes. Diabetes Care. 2012;35(1):32-38. doi:10.2337/dc11-1438
  6. Karter AJ, Parker MM, Moffet HH, Gilliam LK, Dlott R. Association of real-time continuous glucose monitoring with glycemic control and acute metabolic events among patients with insulin-treated diabetes. JAMA. 2021;325(22):2273-2284. doi:10.1001/JAMA.2021.6530
  7. Langford SN, Lane M, Karounos D. Continuous blood glucose monitoring outcomes in veterans with type 2 diabetes. Fed Pract. 2021;38(Suppl 4):S14-S17. doi:10.12788/fp.0189
  8. Radin MS. Pitfalls in hemoglobin A1c measurement: when results may be misleading. J Gen Intern Med. 2014;29(2):388-394. doi:10.1007/s11606-013-2595-x.
  9. Little RR, Rohlfing CL, Sacks DB; National Glycohemoglobin Standardization Program (NGSP) steering committee. Status of hemoglobin A1c measurement and goals for improvement: from chaos to order for improving diabetes care. Clin Chem. 2011;57(2):205-214. doi:10.1373/clinchem.2010.148841
References
  1. US Department of Veterans Affairs. VA supports veterans who have type 2 diabetes. VA News. Accessed September 30, 2024. https://news.va.gov/107579/va-supports-veterans-who-have-type-2-diabetes/
  2. ElSayed NA, Aleppo G, Aroda VR, et al. 9. Pharmacologic approaches to glycemic treatment: standards of care in diabetes-2023. Diabetes Care. 2023;46(Suppl 1):S140- S157. doi:10.2337/dc23-S009
  3. ElSayed NA, Aleppo G, Aroda VR, et al. 6. Glycemic targets: standards of care in diabetes-2023. Diabetes Care. 2023;46(Suppl 1):S97-S110. doi:10.2337/dc23-S006
  4. Miller E, Gavin JR, Kruger DF, Brunton SA. Continuous glucose monitoring: optimizing diabetes care: executive summary. Clin Diabetes. 2022;40(4):394-398. doi:10.2337/cd22-0043
  5. Vigersky RA, Fonda SJ, Chellappa M, Walker MS, Ehrhardt NM. Short- and long-term effects of real-time continuous glucose monitoring in patients with type 2 diabetes. Diabetes Care. 2012;35(1):32-38. doi:10.2337/dc11-1438
  6. Karter AJ, Parker MM, Moffet HH, Gilliam LK, Dlott R. Association of real-time continuous glucose monitoring with glycemic control and acute metabolic events among patients with insulin-treated diabetes. JAMA. 2021;325(22):2273-2284. doi:10.1001/JAMA.2021.6530
  7. Langford SN, Lane M, Karounos D. Continuous blood glucose monitoring outcomes in veterans with type 2 diabetes. Fed Pract. 2021;38(Suppl 4):S14-S17. doi:10.12788/fp.0189
  8. Radin MS. Pitfalls in hemoglobin A1c measurement: when results may be misleading. J Gen Intern Med. 2014;29(2):388-394. doi:10.1007/s11606-013-2595-x.
  9. Little RR, Rohlfing CL, Sacks DB; National Glycohemoglobin Standardization Program (NGSP) steering committee. Status of hemoglobin A1c measurement and goals for improvement: from chaos to order for improving diabetes care. Clin Chem. 2011;57(2):205-214. doi:10.1373/clinchem.2010.148841
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VA Cancer Clinical Trials as a Strategy for Increasing Accrual of Racial and Ethnic Underrepresented Groups

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Background

Cancer clinical trials (CCTs) are central to improving cancer care. However, generalizability of findings from CCTs is difficult due to the lack of diversity in most United States CCTs. Clinical trial accrual of underrepresented groups, is low throughout the United States and is approximately 4-5% in most CCTs. Reasons for low accrual in this population are multifactorial. Despite numerous factors related to accruing racial and ethnic underrepresented groups, many institutions have sought to address these barriers. We conducted a scoping review to identify evidence-based approaches to increase participation in cancer treatment clinical trials.

Methods

We reviewed the Salisbury VA Medical Center Oncology clinical trial database from October 2019 to June 2024. The participants in these clinical trials required consent. These clinical trials included treatment interventional as well as non-treatment interventional. Fifteen studies were included and over 260 Veterans participated.

Results

Key themes emerged that included a focus on patient education, cultural competency, and building capacity in the clinics to care for the Veteran population at three separate sites in the Salisbury VA system. The Black Veteran accrual rate of 29% was achieved. This accrual rate is representative of our VA catchment population of 33% for Black Veterans, and is five times the national average.

Conclusions

The research team’s success in enrolling Black Veterans in clinical trials is attributed to several factors. The demographic composition of Veterans served by the Salisbury, Charlotte, and Kernersville VA provided a diverse population that included a 33% Black group. The type of clinical trials focused on patients who were most impacted by the disease. The VA did afford less barriers to access to health care.

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Background

Cancer clinical trials (CCTs) are central to improving cancer care. However, generalizability of findings from CCTs is difficult due to the lack of diversity in most United States CCTs. Clinical trial accrual of underrepresented groups, is low throughout the United States and is approximately 4-5% in most CCTs. Reasons for low accrual in this population are multifactorial. Despite numerous factors related to accruing racial and ethnic underrepresented groups, many institutions have sought to address these barriers. We conducted a scoping review to identify evidence-based approaches to increase participation in cancer treatment clinical trials.

Methods

We reviewed the Salisbury VA Medical Center Oncology clinical trial database from October 2019 to June 2024. The participants in these clinical trials required consent. These clinical trials included treatment interventional as well as non-treatment interventional. Fifteen studies were included and over 260 Veterans participated.

Results

Key themes emerged that included a focus on patient education, cultural competency, and building capacity in the clinics to care for the Veteran population at three separate sites in the Salisbury VA system. The Black Veteran accrual rate of 29% was achieved. This accrual rate is representative of our VA catchment population of 33% for Black Veterans, and is five times the national average.

Conclusions

The research team’s success in enrolling Black Veterans in clinical trials is attributed to several factors. The demographic composition of Veterans served by the Salisbury, Charlotte, and Kernersville VA provided a diverse population that included a 33% Black group. The type of clinical trials focused on patients who were most impacted by the disease. The VA did afford less barriers to access to health care.

Background

Cancer clinical trials (CCTs) are central to improving cancer care. However, generalizability of findings from CCTs is difficult due to the lack of diversity in most United States CCTs. Clinical trial accrual of underrepresented groups, is low throughout the United States and is approximately 4-5% in most CCTs. Reasons for low accrual in this population are multifactorial. Despite numerous factors related to accruing racial and ethnic underrepresented groups, many institutions have sought to address these barriers. We conducted a scoping review to identify evidence-based approaches to increase participation in cancer treatment clinical trials.

Methods

We reviewed the Salisbury VA Medical Center Oncology clinical trial database from October 2019 to June 2024. The participants in these clinical trials required consent. These clinical trials included treatment interventional as well as non-treatment interventional. Fifteen studies were included and over 260 Veterans participated.

Results

Key themes emerged that included a focus on patient education, cultural competency, and building capacity in the clinics to care for the Veteran population at three separate sites in the Salisbury VA system. The Black Veteran accrual rate of 29% was achieved. This accrual rate is representative of our VA catchment population of 33% for Black Veterans, and is five times the national average.

Conclusions

The research team’s success in enrolling Black Veterans in clinical trials is attributed to several factors. The demographic composition of Veterans served by the Salisbury, Charlotte, and Kernersville VA provided a diverse population that included a 33% Black group. The type of clinical trials focused on patients who were most impacted by the disease. The VA did afford less barriers to access to health care.

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Cost Analysis of Dermatology Residency Applications From 2021 to 2024 Using the Texas Seeking Transparency in Application to Residency Database

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Cost Analysis of Dermatology Residency Applications From 2021 to 2024 Using the Texas Seeking Transparency in Application to Residency Database

To the Editor:

Residency applicants, especially in competitive specialties such as dermatology, face major financial barriers due to the high costs of applications, interviews, and away rotations.1 While several studies have examined application costs of other specialties, few have analyzed expenses associated with dermatology applications.1,2 There are no data examining costs following the start of the COVID-19 pandemic in 2020; thus, our study evaluated dermatology application cost trends from 2021 to 2024 and compared them to other specialties to identify strategies to reduce the financial burden on applicants.

Self-reported total application costs, application fees, interview expenses, and away rotation costs from 2021 to 2024 were collected from the Texas Seeking Transparency in Application to Residency (STAR) database powered by the UT Southwestern Medical Center (Dallas, Texas).3 The mean total application expenses per year were compared among specialties, and an analysis of variance was used to determine if the differences were statistically significant.

The number of applicants who recorded information in the Texas STAR database was 110 in 2021, 163 in 2022, 136 in 2023, and 129 in 2024.3 The total dermatology application expenses increased from $2805 in 2021 to $6231 in 2024; interview costs increased from $404 in 2021 to $911 in 2024; and away rotation costs increased from $850 in 2021 to $3812 in 2024 (all P<.05)(Table). There was no significant change in application fees during the study period ($2176 in 2021 to $2125 in 2024 [P=.58]). Dermatology had the fourth highest average total cost over the study period compared to all other specialties, increasing from $2250 in 2021 to $5250 in 2024, following orthopedic surgery ($2250 in 2021 to $6750 in 2024), plastic surgery ($2250 in 2021 to $9750 in 2024), and neurosurgery ($1750 in 2021 to $11,250 in 2024).

CT116006216-Table

Our study found that dermatology residency application costs have increased significantly from 2021 to 2024, primarily driven by rising interview and away rotation expenses (both P<.05). This trend places dermatology among the most expensive fields to apply to for residency. A cross-sectional survey of dermatology residency program directors identified away rotations as one of the top 5 selection criteria, underscoring their importance in the matching process.4 In addition, a cross-sectional analysis of 345 dermatology residents found that 26.2% matched at institutions where they had mentors, including those they connected with through away rotations.5,6 Overall, the high cost of away rotations partially may reflect the competitive nature of the specialty, as building connections at programs may enhance the chances of matching. These costs also can vary based on geography, as rotating in high-cost urban centers can be more expensive than in rural areas; however, rural rotations may be less common due to limited program availability and applicant preferences. For example, nearly 50% of 2024 Electronic Residency Application Service applicants indicated a preference for urban settings, while fewer than 5% selected rural settings.7 Additionally, the high costs associated with applying to residency programs and completing away rotations can disproportionately impact students from rural backgrounds and underrepresented minorities, who may have fewer financial resources.

In our study, the lower application-related expenses in 2021 (during the pandemic) compared to those of 2024 (postpandemic) likely stem from the Association of American Medical Colleges’ recommendation to conduct virtual interviews during the pandemic.8 In 2024, some dermatology programs returned to in-person interviews, with some applicants consequently incurring higher costs related to travel, lodging, and other associated expenses.8 A cost-analysis study of 4153 dermatology applicants from 2016 to 2021 found that the average application costs were $1759 per applicant during the pandemic, when virtual interviews replaced in-person ones, whereas costs were $8476 per applicant during periods with in-person interviews and no COVID-19 restrictions.2 However, we did not observe a significant change in application fees over our study period, likely because the pandemic did not affect application numbers. A cross-sectional analysis of dermatology applicants during the pandemic similarly reported reductions in application-related expenses during the period when interviews were conducted virtually,9 supporting the trend observed in our study. Overall, our findings taken together with other studies highlight the pandemic’s role in reducing expenses and underscore the potential for exploring additional cost-saving measures.

Implementing strategies to reduce these financial burdens—including virtual interviews, increasing student funding for away rotations, and limiting the number of applications individual students can submit—could help alleviate socioeconomic disparities. The new signaling system for residency programs aims to reduce the number of applications submitted, as applicants typically receive interviews only from the limited number of programs they signal, reducing overall application costs. However, our data from the Texas STAR database suggest that application numbers remained relatively stable from 2021 to 2024, indicating that, despite signaling, many applicants still may apply broadly in hopes of improving their chances in an increasingly competitive field. Although a definitive solution to reducing the financial burden on dermatology applicants remains elusive, these strategies can raise awareness and encourage important dialogues.

Limitations of our study include the voluntary nature of the Texas STAR survey, leading to potential voluntary response bias, as well as the small sample size. Students who choose to submit cost data may differ systematically from those who do not; for example, students who match may be more likely to report their outcomes, while those who do not match may be less likely to participate, potentially introducing selection bias. In addition, general awareness of the Texas STAR survey may vary across institutions and among students, further limiting the number of students who participate. Additionally, 2021 was the only presignaling year included, making it difficult to assess longer-term trends. Despite these limitations, the Texas STAR database remains a valuable resource for analyzing general residency application expenses and trends, as it offers comprehensive data from more than 100 medical schools and includes many variables.3

In conclusion, our study found that total dermatology residency application costs have increased significantly from 2021 to 2024 (all P<.05), making dermatology among the most expensive specialties for applying. This study sets the foundation for future survey-based research for applicants and program directors on strategies to alleviate financial burdens.

References
  1. Mansouri B, Walker GD, Mitchell J, et al. The cost of applying to dermatology residency: 2014 data estimates. J Am Acad Dermatol. 2016;74:754-756. doi:10.1016/j.jaad.2015.10.049
  2. Gorgy M, Shah S, Arbuiso S, et al. Comparison of cost changes due to the COVID-19 pandemic for dermatology residency applications in the USA. Clin Exp Dermatol. 2022;47:600-602. doi:10.1111/ced.15001<.li>
  3. UT Southwestern. Texas STAR. 2024. Accessed November 5, 2025. https://www.utsouthwestern.edu/education/medical-school/about-the-school/student-affairs/texas-star.html
  4. Baldwin K, Weidner Z, Ahn J, et al. Are away rotations critical for a successful match in orthopaedic surgery? Clin Orthop Relat Res. 2009;467:3340-3345. doi:10.1007/s11999-009-0920-9
  5. Yeh C, Desai AD, Wilson BN, et al. Cross-sectional analysis of scholarly work and mentor relationships in matched dermatology residency applicants. J Am Acad Dermatol. 2022;86:1437-1439. doi:10.1016/j.jaad.2021.06.861
  6. Gorouhi F, Alikhan A, Rezaei A, et al. Dermatology residency selection criteria with an emphasis on program characteristics: a national program director survey. Dermatol Res Pract. 2014;2014:692760. doi:10.1155/2014/692760
  7. Association of American Medical Colleges. Decoding geographic and setting preferences in residency selection. January 18, 2024. Accessed October 27, 2025. https://www.aamc.org/services/eras-institutions/geographic-preferences
  8. Association of American Medical Colleges. Virtual interviews: tips for program directors. Updated May 14, 2020. https://med.stanford.edu/content/dam/sm/gme/program_portal/pd/pd_meet/2019-2020/8-6-20-Virtual_Interview_Tips_for_Program_Directors_05142020.pdf
  9. Williams GE, Zimmerman JM, Wiggins CJ, et al. The indelible marks on dermatology: impacts of COVID-19 on dermatology residency match using the Texas STAR database. Clin Dermatol. 2023;41:215-218. doi:10.1016/j.clindermatol.2022.12.001
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Author and Disclosure Information

Naeha Pathak (ORCID: 0000-0002-9870-0704) is from the Icahn School of Medicine at Mount Sinai, New York, New York. Dr. Lipner (ORCID: 0000-0001-5913-9304) is from the Israel Englander Department of Dermatology, Weill Cornell Medicine, New York.

The authors have no relevant financial disclosures to report.

Correspondence: Shari R. Lipner, MD, PhD, 1305 York Ave, 9th Floor, New York, NY 10021 ([email protected]).

Cutis. 2025 December;116(6):216-217. doi:10.12788/cutis.1303

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Naeha Pathak (ORCID: 0000-0002-9870-0704) is from the Icahn School of Medicine at Mount Sinai, New York, New York. Dr. Lipner (ORCID: 0000-0001-5913-9304) is from the Israel Englander Department of Dermatology, Weill Cornell Medicine, New York.

The authors have no relevant financial disclosures to report.

Correspondence: Shari R. Lipner, MD, PhD, 1305 York Ave, 9th Floor, New York, NY 10021 ([email protected]).

Cutis. 2025 December;116(6):216-217. doi:10.12788/cutis.1303

Author and Disclosure Information

Naeha Pathak (ORCID: 0000-0002-9870-0704) is from the Icahn School of Medicine at Mount Sinai, New York, New York. Dr. Lipner (ORCID: 0000-0001-5913-9304) is from the Israel Englander Department of Dermatology, Weill Cornell Medicine, New York.

The authors have no relevant financial disclosures to report.

Correspondence: Shari R. Lipner, MD, PhD, 1305 York Ave, 9th Floor, New York, NY 10021 ([email protected]).

Cutis. 2025 December;116(6):216-217. doi:10.12788/cutis.1303

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Article PDF

To the Editor:

Residency applicants, especially in competitive specialties such as dermatology, face major financial barriers due to the high costs of applications, interviews, and away rotations.1 While several studies have examined application costs of other specialties, few have analyzed expenses associated with dermatology applications.1,2 There are no data examining costs following the start of the COVID-19 pandemic in 2020; thus, our study evaluated dermatology application cost trends from 2021 to 2024 and compared them to other specialties to identify strategies to reduce the financial burden on applicants.

Self-reported total application costs, application fees, interview expenses, and away rotation costs from 2021 to 2024 were collected from the Texas Seeking Transparency in Application to Residency (STAR) database powered by the UT Southwestern Medical Center (Dallas, Texas).3 The mean total application expenses per year were compared among specialties, and an analysis of variance was used to determine if the differences were statistically significant.

The number of applicants who recorded information in the Texas STAR database was 110 in 2021, 163 in 2022, 136 in 2023, and 129 in 2024.3 The total dermatology application expenses increased from $2805 in 2021 to $6231 in 2024; interview costs increased from $404 in 2021 to $911 in 2024; and away rotation costs increased from $850 in 2021 to $3812 in 2024 (all P<.05)(Table). There was no significant change in application fees during the study period ($2176 in 2021 to $2125 in 2024 [P=.58]). Dermatology had the fourth highest average total cost over the study period compared to all other specialties, increasing from $2250 in 2021 to $5250 in 2024, following orthopedic surgery ($2250 in 2021 to $6750 in 2024), plastic surgery ($2250 in 2021 to $9750 in 2024), and neurosurgery ($1750 in 2021 to $11,250 in 2024).

CT116006216-Table

Our study found that dermatology residency application costs have increased significantly from 2021 to 2024, primarily driven by rising interview and away rotation expenses (both P<.05). This trend places dermatology among the most expensive fields to apply to for residency. A cross-sectional survey of dermatology residency program directors identified away rotations as one of the top 5 selection criteria, underscoring their importance in the matching process.4 In addition, a cross-sectional analysis of 345 dermatology residents found that 26.2% matched at institutions where they had mentors, including those they connected with through away rotations.5,6 Overall, the high cost of away rotations partially may reflect the competitive nature of the specialty, as building connections at programs may enhance the chances of matching. These costs also can vary based on geography, as rotating in high-cost urban centers can be more expensive than in rural areas; however, rural rotations may be less common due to limited program availability and applicant preferences. For example, nearly 50% of 2024 Electronic Residency Application Service applicants indicated a preference for urban settings, while fewer than 5% selected rural settings.7 Additionally, the high costs associated with applying to residency programs and completing away rotations can disproportionately impact students from rural backgrounds and underrepresented minorities, who may have fewer financial resources.

In our study, the lower application-related expenses in 2021 (during the pandemic) compared to those of 2024 (postpandemic) likely stem from the Association of American Medical Colleges’ recommendation to conduct virtual interviews during the pandemic.8 In 2024, some dermatology programs returned to in-person interviews, with some applicants consequently incurring higher costs related to travel, lodging, and other associated expenses.8 A cost-analysis study of 4153 dermatology applicants from 2016 to 2021 found that the average application costs were $1759 per applicant during the pandemic, when virtual interviews replaced in-person ones, whereas costs were $8476 per applicant during periods with in-person interviews and no COVID-19 restrictions.2 However, we did not observe a significant change in application fees over our study period, likely because the pandemic did not affect application numbers. A cross-sectional analysis of dermatology applicants during the pandemic similarly reported reductions in application-related expenses during the period when interviews were conducted virtually,9 supporting the trend observed in our study. Overall, our findings taken together with other studies highlight the pandemic’s role in reducing expenses and underscore the potential for exploring additional cost-saving measures.

Implementing strategies to reduce these financial burdens—including virtual interviews, increasing student funding for away rotations, and limiting the number of applications individual students can submit—could help alleviate socioeconomic disparities. The new signaling system for residency programs aims to reduce the number of applications submitted, as applicants typically receive interviews only from the limited number of programs they signal, reducing overall application costs. However, our data from the Texas STAR database suggest that application numbers remained relatively stable from 2021 to 2024, indicating that, despite signaling, many applicants still may apply broadly in hopes of improving their chances in an increasingly competitive field. Although a definitive solution to reducing the financial burden on dermatology applicants remains elusive, these strategies can raise awareness and encourage important dialogues.

Limitations of our study include the voluntary nature of the Texas STAR survey, leading to potential voluntary response bias, as well as the small sample size. Students who choose to submit cost data may differ systematically from those who do not; for example, students who match may be more likely to report their outcomes, while those who do not match may be less likely to participate, potentially introducing selection bias. In addition, general awareness of the Texas STAR survey may vary across institutions and among students, further limiting the number of students who participate. Additionally, 2021 was the only presignaling year included, making it difficult to assess longer-term trends. Despite these limitations, the Texas STAR database remains a valuable resource for analyzing general residency application expenses and trends, as it offers comprehensive data from more than 100 medical schools and includes many variables.3

In conclusion, our study found that total dermatology residency application costs have increased significantly from 2021 to 2024 (all P<.05), making dermatology among the most expensive specialties for applying. This study sets the foundation for future survey-based research for applicants and program directors on strategies to alleviate financial burdens.

To the Editor:

Residency applicants, especially in competitive specialties such as dermatology, face major financial barriers due to the high costs of applications, interviews, and away rotations.1 While several studies have examined application costs of other specialties, few have analyzed expenses associated with dermatology applications.1,2 There are no data examining costs following the start of the COVID-19 pandemic in 2020; thus, our study evaluated dermatology application cost trends from 2021 to 2024 and compared them to other specialties to identify strategies to reduce the financial burden on applicants.

Self-reported total application costs, application fees, interview expenses, and away rotation costs from 2021 to 2024 were collected from the Texas Seeking Transparency in Application to Residency (STAR) database powered by the UT Southwestern Medical Center (Dallas, Texas).3 The mean total application expenses per year were compared among specialties, and an analysis of variance was used to determine if the differences were statistically significant.

The number of applicants who recorded information in the Texas STAR database was 110 in 2021, 163 in 2022, 136 in 2023, and 129 in 2024.3 The total dermatology application expenses increased from $2805 in 2021 to $6231 in 2024; interview costs increased from $404 in 2021 to $911 in 2024; and away rotation costs increased from $850 in 2021 to $3812 in 2024 (all P<.05)(Table). There was no significant change in application fees during the study period ($2176 in 2021 to $2125 in 2024 [P=.58]). Dermatology had the fourth highest average total cost over the study period compared to all other specialties, increasing from $2250 in 2021 to $5250 in 2024, following orthopedic surgery ($2250 in 2021 to $6750 in 2024), plastic surgery ($2250 in 2021 to $9750 in 2024), and neurosurgery ($1750 in 2021 to $11,250 in 2024).

CT116006216-Table

Our study found that dermatology residency application costs have increased significantly from 2021 to 2024, primarily driven by rising interview and away rotation expenses (both P<.05). This trend places dermatology among the most expensive fields to apply to for residency. A cross-sectional survey of dermatology residency program directors identified away rotations as one of the top 5 selection criteria, underscoring their importance in the matching process.4 In addition, a cross-sectional analysis of 345 dermatology residents found that 26.2% matched at institutions where they had mentors, including those they connected with through away rotations.5,6 Overall, the high cost of away rotations partially may reflect the competitive nature of the specialty, as building connections at programs may enhance the chances of matching. These costs also can vary based on geography, as rotating in high-cost urban centers can be more expensive than in rural areas; however, rural rotations may be less common due to limited program availability and applicant preferences. For example, nearly 50% of 2024 Electronic Residency Application Service applicants indicated a preference for urban settings, while fewer than 5% selected rural settings.7 Additionally, the high costs associated with applying to residency programs and completing away rotations can disproportionately impact students from rural backgrounds and underrepresented minorities, who may have fewer financial resources.

In our study, the lower application-related expenses in 2021 (during the pandemic) compared to those of 2024 (postpandemic) likely stem from the Association of American Medical Colleges’ recommendation to conduct virtual interviews during the pandemic.8 In 2024, some dermatology programs returned to in-person interviews, with some applicants consequently incurring higher costs related to travel, lodging, and other associated expenses.8 A cost-analysis study of 4153 dermatology applicants from 2016 to 2021 found that the average application costs were $1759 per applicant during the pandemic, when virtual interviews replaced in-person ones, whereas costs were $8476 per applicant during periods with in-person interviews and no COVID-19 restrictions.2 However, we did not observe a significant change in application fees over our study period, likely because the pandemic did not affect application numbers. A cross-sectional analysis of dermatology applicants during the pandemic similarly reported reductions in application-related expenses during the period when interviews were conducted virtually,9 supporting the trend observed in our study. Overall, our findings taken together with other studies highlight the pandemic’s role in reducing expenses and underscore the potential for exploring additional cost-saving measures.

Implementing strategies to reduce these financial burdens—including virtual interviews, increasing student funding for away rotations, and limiting the number of applications individual students can submit—could help alleviate socioeconomic disparities. The new signaling system for residency programs aims to reduce the number of applications submitted, as applicants typically receive interviews only from the limited number of programs they signal, reducing overall application costs. However, our data from the Texas STAR database suggest that application numbers remained relatively stable from 2021 to 2024, indicating that, despite signaling, many applicants still may apply broadly in hopes of improving their chances in an increasingly competitive field. Although a definitive solution to reducing the financial burden on dermatology applicants remains elusive, these strategies can raise awareness and encourage important dialogues.

Limitations of our study include the voluntary nature of the Texas STAR survey, leading to potential voluntary response bias, as well as the small sample size. Students who choose to submit cost data may differ systematically from those who do not; for example, students who match may be more likely to report their outcomes, while those who do not match may be less likely to participate, potentially introducing selection bias. In addition, general awareness of the Texas STAR survey may vary across institutions and among students, further limiting the number of students who participate. Additionally, 2021 was the only presignaling year included, making it difficult to assess longer-term trends. Despite these limitations, the Texas STAR database remains a valuable resource for analyzing general residency application expenses and trends, as it offers comprehensive data from more than 100 medical schools and includes many variables.3

In conclusion, our study found that total dermatology residency application costs have increased significantly from 2021 to 2024 (all P<.05), making dermatology among the most expensive specialties for applying. This study sets the foundation for future survey-based research for applicants and program directors on strategies to alleviate financial burdens.

References
  1. Mansouri B, Walker GD, Mitchell J, et al. The cost of applying to dermatology residency: 2014 data estimates. J Am Acad Dermatol. 2016;74:754-756. doi:10.1016/j.jaad.2015.10.049
  2. Gorgy M, Shah S, Arbuiso S, et al. Comparison of cost changes due to the COVID-19 pandemic for dermatology residency applications in the USA. Clin Exp Dermatol. 2022;47:600-602. doi:10.1111/ced.15001<.li>
  3. UT Southwestern. Texas STAR. 2024. Accessed November 5, 2025. https://www.utsouthwestern.edu/education/medical-school/about-the-school/student-affairs/texas-star.html
  4. Baldwin K, Weidner Z, Ahn J, et al. Are away rotations critical for a successful match in orthopaedic surgery? Clin Orthop Relat Res. 2009;467:3340-3345. doi:10.1007/s11999-009-0920-9
  5. Yeh C, Desai AD, Wilson BN, et al. Cross-sectional analysis of scholarly work and mentor relationships in matched dermatology residency applicants. J Am Acad Dermatol. 2022;86:1437-1439. doi:10.1016/j.jaad.2021.06.861
  6. Gorouhi F, Alikhan A, Rezaei A, et al. Dermatology residency selection criteria with an emphasis on program characteristics: a national program director survey. Dermatol Res Pract. 2014;2014:692760. doi:10.1155/2014/692760
  7. Association of American Medical Colleges. Decoding geographic and setting preferences in residency selection. January 18, 2024. Accessed October 27, 2025. https://www.aamc.org/services/eras-institutions/geographic-preferences
  8. Association of American Medical Colleges. Virtual interviews: tips for program directors. Updated May 14, 2020. https://med.stanford.edu/content/dam/sm/gme/program_portal/pd/pd_meet/2019-2020/8-6-20-Virtual_Interview_Tips_for_Program_Directors_05142020.pdf
  9. Williams GE, Zimmerman JM, Wiggins CJ, et al. The indelible marks on dermatology: impacts of COVID-19 on dermatology residency match using the Texas STAR database. Clin Dermatol. 2023;41:215-218. doi:10.1016/j.clindermatol.2022.12.001
References
  1. Mansouri B, Walker GD, Mitchell J, et al. The cost of applying to dermatology residency: 2014 data estimates. J Am Acad Dermatol. 2016;74:754-756. doi:10.1016/j.jaad.2015.10.049
  2. Gorgy M, Shah S, Arbuiso S, et al. Comparison of cost changes due to the COVID-19 pandemic for dermatology residency applications in the USA. Clin Exp Dermatol. 2022;47:600-602. doi:10.1111/ced.15001<.li>
  3. UT Southwestern. Texas STAR. 2024. Accessed November 5, 2025. https://www.utsouthwestern.edu/education/medical-school/about-the-school/student-affairs/texas-star.html
  4. Baldwin K, Weidner Z, Ahn J, et al. Are away rotations critical for a successful match in orthopaedic surgery? Clin Orthop Relat Res. 2009;467:3340-3345. doi:10.1007/s11999-009-0920-9
  5. Yeh C, Desai AD, Wilson BN, et al. Cross-sectional analysis of scholarly work and mentor relationships in matched dermatology residency applicants. J Am Acad Dermatol. 2022;86:1437-1439. doi:10.1016/j.jaad.2021.06.861
  6. Gorouhi F, Alikhan A, Rezaei A, et al. Dermatology residency selection criteria with an emphasis on program characteristics: a national program director survey. Dermatol Res Pract. 2014;2014:692760. doi:10.1155/2014/692760
  7. Association of American Medical Colleges. Decoding geographic and setting preferences in residency selection. January 18, 2024. Accessed October 27, 2025. https://www.aamc.org/services/eras-institutions/geographic-preferences
  8. Association of American Medical Colleges. Virtual interviews: tips for program directors. Updated May 14, 2020. https://med.stanford.edu/content/dam/sm/gme/program_portal/pd/pd_meet/2019-2020/8-6-20-Virtual_Interview_Tips_for_Program_Directors_05142020.pdf
  9. Williams GE, Zimmerman JM, Wiggins CJ, et al. The indelible marks on dermatology: impacts of COVID-19 on dermatology residency match using the Texas STAR database. Clin Dermatol. 2023;41:215-218. doi:10.1016/j.clindermatol.2022.12.001
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Cost Analysis of Dermatology Residency Applications From 2021 to 2024 Using the Texas Seeking Transparency in Application to Residency Database

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PRACTICE POINTS

  • Dermatology application costs increased from 2021 to 2024, largely due to expenses related to away rotations and, in some cases, a return to in-person interviews.
  • Away rotations play a critical role in the dermatology match; however, they also contribute substantially to financial burden.
  • The cost-saving impact of virtual interviews during the COVID-19 pandemic highlights a meaningful opportunity for future cost reduction.
  • Further interventions are needed to meaningfully reduce financial burden and promote equity.
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Staff Perspectives on the VISN 20 Tele-Neuropsychology Program

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Staff Perspectives on the VISN 20 Tele-Neuropsychology Program

There are 2.7 million (48%) rural veterans enrolled in the Veterans Health Administration (VHA).1 Many VHA-enrolled rural veterans are aged ≥ 65 years (54%), a medically complex population that requires more extensive health care.1 These veterans may live far from US Department of Veterans Affairs (VA) medical centers (VAMCs) and often receive most of their care at rural community-based outpatient clinics (CBOCs). In addition to face-to-face (F2F) services provided at these clinics, many patient care needs may be met using telehealth technology, which can connect veterans at CBOCs with remote health care practitioners (HCPs).

This technology is used across medical specialties throughout the VA and has expanded into neuropsychology services to improve access amid the shortage of rural neuropsychologists. Prior research suggests that access to neuropsychology services improves the functional outcomes of people with diverse medical conditions, including dementia, brain injury, and epilepsy, and reduces emergency department visits, hospitalization duration, and health care costs.2-6 Given that veterans unable to access neuropsychology services may be at risk for poorer outcomes, identifying ways to improve access is a priority. Tele-neuropsychology (teleNP) has been used to expand access for rural veterans in need of these services.7,8 

TeleNP is the application of audiovisual technologies to enable remote clinical encounters for neuropsychological assessments.9 TeleNP has been shown to be generally equivalent to F2F care, without significant differences compared with in-person visits.10-13 TeleNP was increasingly implemented following the COVID-19 pandemic and remains an enduring and expanding feature of neuropsychology care delivery.8,14-18 TeleNP services can increase access to care, especially for rural veterans and those with limited transportation. 

Research in non-VA samples suggests a high level of clinician satisfaction with teleNP.16 In VA samples, research has found high levels of patient satisfaction with teleNP both within Veterans Integrated Services Network (VISN) 20 and in a VA health care system outside VISN 20.7,19 Investigating staff perceptions of these services and their utility compared with non-VA F2F visits is pertinent to the overall feasibility and effectiveness of teleNP. 

TELE-NEUROPSYCHOLOGY PROGRAM 

A clinical resource hub (CRH) is a VISN-governed program that provides veteran health care when local VHA facilities have service gaps.20,21 CRH 20 serves several Pacific Northwest VISN 20 health care systems and began providing teleNP in 2015. The CRH 20 teleNP service serves older adults in rural settings with > 570 teleNP evaluations completed over a recent 12-month period (May 2023 to May 2024). In the CRH 20 teleNP program, veterans are offered services by CRH 20 neuropsychologists via telehealth to a patient’s local VAMC, larger health care clinic, CBOC, or via Veterans Video Connect to the home. 

FIGURE. Usefulness of face-to-face and tele-neuropsychology evaluations and reports (N = 18). Abbreviations: VA, US Department of Veterans Affairs.
FIGURE. Usefulness of face-to-face and tele-neuropsychology evaluations and reports (N = 18). Abbreviations: VA, US Department of Veterans Affairs.

Referral pathways to the CRH 20 teleNP program differ across sites. For VISN 20 sites that do not have any in-house neuropsychology services, referrals are initiated by HCPs from any discipline. At 2 sites with in-house neuropsychology programs, CRH 20 teleNP referrals typically are forwarded from the inhouse service whenever the veteran prefers to be seen at an outlying clinic. All sites, including the CBOCs, are equipped fully for testing, and the HCP encounters veterans in a private office via video-based telehealth technology after a telehealth technician orients them to the space. The private office minimizes environmental disruptions and uses standardized technology to ensure valid results. A limited number of evaluations are offered at home (< 5% of the evaluations) if the veteran is unable to come to a VHA facility, has access to reliable internet, and a minimally distracting home setting. 

In VISN 20, teleNP is a routine practice for delivering services to rural sites, most of which lack neuropsychologists. However, there is limited information about the extent to which the referral sources find the service useful. This quality improvement (QI) project aimed to better understand how well-established teleNP services were received by referral sources/stakeholders and how services could be improved. Prior to the advent of the CRH 20 teleNP program, staff had the option of referring for F2F evaluations in the local community (outside the VA) at some sites, an option that remains. This QI project examined staff perspectives on the usefulness of CRH 20 teleNP services compared with non-VA F2F services. We administered an anonymous, confidential survey examining these factors to VISN 20 staff within 4 VA health care systems. 

METHODS 

This QI project used a mixed quantitative and qualitative descriptive survey design to elicit feedback. The authors (3 neuropsychologists, 1 geropsychologist, and 1 research coordinator) developed the survey questions. The 13-question survey was voluntary, anonymous, and confidential, and respondents were given an opportunity to ask questions, with the first author serving as the point of contact. 

The survey ascertained information about respondents and their work setting (ie, facility type, specific work setting and location, profession, and rurality of patients). First respondents were asked whether they have referred patients to neuropsychology services in the past year. Those who had not referred patients during the past year were asked about reasons for nonreferral with an option to provide an open-ended response. Respondents who did refer were asked how they refer for neuropsychology services and about the usefulness and timeliness of both teleNP and non-VA F2F services. Respondents were asked to respond with their preference for teleNP vs non-VA F2F with an open-ended prompt. Finally, respondents were invited to share any feedback for improvement regarding teleNP services. 

A link to the survey, hosted on the VA Research Electronic Data Capture system, was emailed to facility and service line leaders at the 4 VISN 20 health care systems for distribution to the staff. All staff were included because in many of the facilities, particularly those that are highly rural with low staffing, it is not uncommon for technicians, nurses, and other support staff to assist with placing consults. In particular, VISN 20 nurses often have an optimal understanding of referral pathways to care for patients and are positioned to give and receive feedback about the utility of neuropsychological evaluations. The Research and Development Committee at the Boise VA Medical Center determined this project to be QI and exempt from institutional review board oversight. The VISN 20 employee labor relations HR supervisor approved this survey, with union awareness. Responses were anonymous. 

Data were imported into Microsoft Excel and IBM SPSS Statistics for further analysis. Data were summarized using descriptive statistics, frequencies, and percentages. Nonparametric χ2 and Wilcoxon signed-rank tests were used to test for differences. An inductive approach to develop codes was used for the 3 open-ended questions. Two authors (CC, CEG) independently coded the responses and reviewed discrepancies. Final code applications were based on consensus. 

RESULTS 

The survey was deployed for 1 month between February 7, 2024, and June 15, 2024, at each of the 4 health care systems. Thirty-three staff members responded; of these, 1 person did not respond to an item on whether they referred for neuropsychology services. Eighteen of 33 respondents reported referring patients to teleNP or F2F neuropsychology services in the past year. Fourteen of the 33 respondents stated they did not refer; of these, 2 were unfamiliar with the teleNP service and 12 provided other reasons (eg, new to VA, not in their professional scope to order consults, did not have patients needing services). 

The analysis focused on the 18 respondents who referred for neuropsychology services. Thirteen were within health care system A, and 5 were within health care system B (which had no nearby non-VA contracted neuropsychology services) and none were in the other 2 health care systems. Ten of 18 respondents (56%) stated they practiced primarily in a rural setting. Five respondents worked in a CBOC, 12 in a main VA facility, 9 in a primary care setting, 8 in a mental health setting, and 3 in other settings (eg, domiciliary). Participants could select > 1 setting. The 18 respondents who referred to neuropsychology services included 7 psychologists, 1 nurse, 2 social workers, 1 social services assistant, 4 nurse practitioners, 2 physicians, and 1 unknown HCP. 

When asked to categorize the usefulness of services, more respondents characterized teleNP as very much so (1 on a 5-point scale) than F2F referrals (Figure). The mean (SD) of 1.5 (0.8) for teleNP usefulness fell between very much so and mostly and 1 respondent indicated not applicable. Similarly, the mean (SD) for non-VA F2F usefulness was 1.7 (0.9); 9 respondents rated this item as not applicable. A Wilcoxon signed-rank test of related samples indicated no significant differences between the pairs of ratings (Z = 1.50; P = .41). 

Respondents with rural patients were more likely to refer them to teleNP services compared with respondents with nonrural patients (χ2 = 5.7; P = .02). However, ratings of teleNP usefulness did not significantly differ for those serving rural vs with nonrural patients (χ2 = 1.4; P = .49). Mean (SD) rating of teleNP usefulness was 1.3 (0.7) for the 9 rural subgroup respondents (between very much so and mostly) vs 1.8 (0.9) for the 8 nonrural subgroup respondents (between very much so and mostly). The mean (SD) rating for non-VA F2F usefulness was 1.8 (1.0) for the 4 rural subgroup respondents and 1.6 (0.8) for the 5 nonrural subgroup, between very much so and mostly for both groups. 

Most respondents had no preference between teleNP or F2F. Notably, the responses underlying this group were multifaceted and corresponded to multiple codes (ie, access, preference for in-person services, technology, space and logistics, and service boundaries and requirements). According to 1 respondent, “the logistics of scheduling/room availability, technological challenges, and client behavioral issues that are likely to occur could possibly be more easily addressed via in-person sessions for some clients and providers.” 

Six of 18 respondents preferred teleNP, citing timeliness, ease of access, and evaluation quality. One respondent noted that the “majority of my veterans live in extremely remote areas” and may need to take a plane for their visit. The 3 respondents who preferred in-person neuropsychology services cited veterans’ preference for in-person services. 

Open-Ended Feedback 

Thirteen respondents offered feedback on what is working well with teleNP services. Reasons mentioned were related to the service (ie, timeliness, access, quality) and the neuropsychologist (ie, communication and HCP skills). One respondent described the service and neuropsychologists positively, stating that they were “responsive, notes are readily available, clear assessments and recommendations, being available by [Microsoft] Teams/email.” 

Ten respondents provided suggestions for improvement. Suggestions focused on expanding services, such as to “all veterans with cognitive/memory concerns that desire testing,” individuals with attention-deficit/hyperactivity disorder and co-occurring mental health concerns, and those in residential programs. Suggestions included hiring psychology technicians or more staff and providing education at local clinics. 

DISCUSSION 

This QI project examines VA staff perspectives on the usefulness of CRH 20 teleNP services and non-VA F2F services. While the small sample size limits generalizability, this preliminary study suggests that VA teleNP evaluations were similar to those conducted F2F in non-VA settings. While ratings of teleNP usefulness did not differ significantly for those serving rural vs nonrural veterans, respondents serving rural patients were more likely to refer patients to teleNP, suggesting that teleNP may increase access in rural settings, consistent with other studies.7,8,13 This article also presents qualitative suggestions for improving teleNP delivery within the VHA. This is the first known initiative to report on VHA staff satisfaction with a teleNP service and expands the limited literature to date on satisfaction with teleNP services. The findings provide initial support for continued use and, potentially, expansion of teleNP services within this CRH remote hub-and-spoke model. 

Limitations 

A significant limitation of the current work is the small sample size of survey respondents. In particular, while teleNP turnaround time was perceived as faster than non-VA F2F care, only 8 respondents reported on timeliness of F2F evaluation results, which renders it difficult to draw conclusions. Interestingly, not all respondents reported referring to neuropsychology services within the previous year; the most common reasons reflect the perception that referral to neuropsychology was outside of that staff member’s role or not clinically indicated. 

One additional possible explanation for the absence of reporting on utility of teleNP specifically is that respondents did not track whether their patient was seen by teleNP or F2F services, based on how the referral process varies at each health care system. For example, in health care system C, a large number of referrals are forwarded to the service by local VA F2F neuropsychologists. This may speak to the seamlessness of the teleNP process, such that local staff and/or referring HCPs are unaware of the modality over which neuropsychology is being conducted. It is plausible that the reason behind this smaller response rate in health care systems B and C relates to how neuropsychology consults are processed at these local VAMCs. We suspect that in these settings, the HCPs referring for neuropsychological evaluations (eg, primary care, mental health) may be unaware that their referrals are being triaged to neuropsychologists in a different program (CRH 20 teleNP). Therefore, they would not necessarily know that they used teleNP and didn’t complete the survey. 

The referral process for these 2 sites contrasts with the process for other VISN 20 sites where there is no local neuropsychology program triaging. In these settings, referrals from local HCPs come directly to teleNP; thus, it is more likely that these HCPs are aware of teleNP services. There were only 2 physicians who completed the survey, which may relate to their workload and a workflow where other staff have been increasingly requested to order the consults for the physician. This type of workflow results in an increase in the number of VHA staff involved in patient care. Ratings of usefulness were highest in health care system B, which does not have neuropsychology services at the facility or in the community; this may relate to elevated teleNP satisfaction ratings. 

Further work may help identify which aspects of a teleNP service make it more useful than F2F care for this population or determine whether there were HCPor setting-specific factors that influenced the ratings (ie, preference for VA care or comparison of favorability ratings for the HCPs who conduct teleNP and F2F within the same system). The latter comparisons could not be drawn in the current systems due to the absence of HCPs who provide both teleNP and F2F modalities within VISN 20. Another consideration for future work would be to use a previously published/validated survey measure and piloting of questions with a naive sample before implementation. 

CONCLUSIONS 

This analysis provides initial support for feasibility and acceptability of teleNP as an alternative to traditional in-person neuropsychological evaluations. The small number of survey respondents may reflect the multiple pathways through which consults are forwarded to CRH 20, which includes both direct HCP referrals and forwarded consults from local neuropsychology services. CRH 20 has completed > 570 teleNP evaluations within 1 year, suggesting that lack of awareness may not be hindering veteran access to the service. Replication with a larger sample that is more broadly representative of key stakeholders in veteran care, identification of populations that would benefit most from teleNP services, and dissemination studies of the expansion of teleNP services are all important directions for future work. The robustness and longevity of the VISN 20 teleNP program, coupled with the preliminary positive findings from this project, demonstrate support for further assessment of the potential impact of telehealth on neuropsychological care within the VHA and show that barriers associated with access to health care services in remote settings may be mitigated through teleNP service delivery.

References
  1. US Department of Veterans Affairs, Office of Rural Health. Rural veterans. Updated March 10, 2025. Accessed July 7, 2025. https://www.ruralhealth.va.gov/aboutus/ruralvets.asp
  2. Braun M, Tupper D, Kaufmann P, et al. Neuropsychological assessment: a valuable tool in the diagnosis and management of neurological, neurodevelopmental, medical, and psychiatric disorders. Cogn Behav Neurol. 2011;24(3):107-114. doi:10.1097/wnn.0b013e3182351289
  3. Donders J. The incremental value of neuropsychological assessment: a critical review. Clin Neuropsychol. 2020;34(1):56-87. doi:10.1080/13854046.2019.1575471
  4. Williams MW, Rapport LJ, Hanks RA, et al. Incremental value of neuropsychological evaluations to computed tomography in predicting long-term outcomes after traumatic brain injury. Clin Neuropsychol. 2013;27(3):356-375. doi:10.1080/13854046.2013.765507
  5. Sieg E, Mai Q, Mosti C, Brook M. The utility of neuropsychological consultation in identifying medical inpatients with suspected cognitive impairment at risk for greater hospital utilization. Clin Neuropsychol. 2019;33(1):75-89. doi:10.1080/13854046.2018.1465124
  6. Vankirk KM, Horner MD, Turner TH, et al. CE hospital service utilization is reduced following neuropsychological evaluation in a sample of U.S. veterans. Clin Neuropsychol. 2013;27(5):750-761. doi:10.1080/13854046.2013.783122
  7. Appleman ER, O’Connor MK, Boucher SJ, et al. Teleneuropsychology clinic development and patient satisfaction. Clin Neuropsychol. 2021;35(4):819-837. doi:10.1080/13854046.2020.1871515
  8. Stelmokas J, Ratcliffe LN, Lengu K, et al. Evaluation of teleneuropsychology services in veterans during COVID-19. Psychol Serv. 2024;21(1):65-72. doi:10.1037/ser0000810
  9. Bilder R Postal KS, Barisa M, et al. Inter Organizational Practice Committee recommendations/guidance for teleneuropsychology in response to the COVID-19 pandemic. Arch Clin Neuropsychol. 2020;35(6):647-659. doi:10.1093/arclin/acaa046
  10. Brearly TW, Shura RD, Martindale SL, et al. Neuropsychological test administration by videoconference: a systematic review and meta-analysis. Neuropsychol Rev. 2017;27(2):174-186. doi:10.1007/s11065-017-9349-1
  11. Brown AD, Kelso W, Eratne D, et al. Investigating equivalence of in-person and telehealth-based neuropsychological assessment performance for individuals being investigated for younger onset dementia. Arch Clin Neuropsychol. 2024;39(5):594-607. doi:10.1093/arclin/acad108
  12. Chapman JE, Ponsford J, Bagot KL, et al. The use of videoconferencing in clinical neuropsychology practice: a mixed methods evaluation of neuropsychologists’ experiences and views. Aust Psychol. 2020;55(6):618-633. doi:10.1111/ap.12471
  13. Marra DE, Hamlet KM, Bauer RM, et al. Validity of teleneuropsychology for older adults in response to COVID-19: a systematic and critical review. Clin Neuropsychol. 2020;34:1411-1452. doi:10.1080/13854046.2020.1769192
  14. Hammers DB, Stolwyk R, Harder L, et al. A survey of international clinical teleneuropsychology service provision prior to COVID-19. Clin Neuropsychol. 2020;34(7-8):1267- 1283. doi:10.1080/13854046.2020.1810323
  15. Marra DE, Hoelzle JB, Davis JJ, et al. Initial changes in neuropsychologists’ clinical practice during the COVID-19 pandemic: a survey study. Clin Neuropsychol. 2020;34(7- 8):1251-1266. doi:10.1080/13854046.2020.1800098
  16. Parsons MW, Gardner MM, Sherman, JC et al. Feasibility and acceptance of direct-to-home teleneuropsychology services during the COVID-19 pandemic. J Int Neuropsychol Soc. 2022;28(2):210-215. doi:10.1017/s1355617721000436
  17. Rochette AD, Rahman-Filipiak A, Spencer RJ, et al. Teleneuropsychology practice survey during COVID-19 within the United States. Appl Neuropsychol Adult. 2022;29(6):1312- 1322. doi:10.1080/23279095.2021.1872576
  18. Messler AC, Hargrave DD, Trittschuh EH, et al. National survey of telehealth neuropsychology practices: current attitudes, practices, and relevance of tele-neuropsychology three years after the onset of COVID-19. Clin Neuropsychol. 2023;39:1017-1036. doi:10.1080/13854046.2023.2192422
  19. Rautman L, Sordahl JA. Veteran satisfaction with tele-neuropsychology services. Clin Neuropsychol. 2018;32:1453949. doi:10.1080/13854046.2018.1453949
  20. US Department of Veterans Affairs. Patient care services: clinical resource hubs. Updated March 20, 2024. Accessed August 4, 2025. https://www.patientcare .va.gov/primarycare/CRH.asp  
  21. Burnett K, Stockdale SE, Yoon J, et al. The Clinical Resource Hub initiative: first-year implementation of the Veterans Health Administration regional telehealth contingency staffing program. Ambul Care Manage. 2023;46(3):228-239. doi:10.1097/JAC.0000000000000468
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Fed Pract. 2025;42(11):e0652. Published online November 20. doi:10.12788/fp.0652

Author affiliations 

aBoise Veterans Affairs Medical Center, Idaho 
bMontana Veterans Affairs Health Care System, Fort Harrison 
cVeterans Affairs Palo Alto Health Care System, California 
dStanford University, Palo Alto, California 

Author disclosures 

The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies. 

Ethics and consent 

The Boise Veterans Affairs Medical Center Research and Development Committee determined this project to be quality improvement and exempt from institutional review board review. 

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Correspondence: Ana Messler ([email protected]

Fed Pract. 2025;42(11):e0652. Published online November 20. doi:10.12788/fp.0652

Author affiliations 

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bMontana Veterans Affairs Health Care System, Fort Harrison 
cVeterans Affairs Palo Alto Health Care System, California 
dStanford University, Palo Alto, California 

Author disclosures 

The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies. 

Ethics and consent 

The Boise Veterans Affairs Medical Center Research and Development Committee determined this project to be quality improvement and exempt from institutional review board review. 

Author and Disclosure Information

Correspondence: Ana Messler ([email protected]

Fed Pract. 2025;42(11):e0652. Published online November 20. doi:10.12788/fp.0652

Author affiliations 

aBoise Veterans Affairs Medical Center, Idaho 
bMontana Veterans Affairs Health Care System, Fort Harrison 
cVeterans Affairs Palo Alto Health Care System, California 
dStanford University, Palo Alto, California 

Author disclosures 

The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies. 

Ethics and consent 

The Boise Veterans Affairs Medical Center Research and Development Committee determined this project to be quality improvement and exempt from institutional review board review. 

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There are 2.7 million (48%) rural veterans enrolled in the Veterans Health Administration (VHA).1 Many VHA-enrolled rural veterans are aged ≥ 65 years (54%), a medically complex population that requires more extensive health care.1 These veterans may live far from US Department of Veterans Affairs (VA) medical centers (VAMCs) and often receive most of their care at rural community-based outpatient clinics (CBOCs). In addition to face-to-face (F2F) services provided at these clinics, many patient care needs may be met using telehealth technology, which can connect veterans at CBOCs with remote health care practitioners (HCPs).

This technology is used across medical specialties throughout the VA and has expanded into neuropsychology services to improve access amid the shortage of rural neuropsychologists. Prior research suggests that access to neuropsychology services improves the functional outcomes of people with diverse medical conditions, including dementia, brain injury, and epilepsy, and reduces emergency department visits, hospitalization duration, and health care costs.2-6 Given that veterans unable to access neuropsychology services may be at risk for poorer outcomes, identifying ways to improve access is a priority. Tele-neuropsychology (teleNP) has been used to expand access for rural veterans in need of these services.7,8 

TeleNP is the application of audiovisual technologies to enable remote clinical encounters for neuropsychological assessments.9 TeleNP has been shown to be generally equivalent to F2F care, without significant differences compared with in-person visits.10-13 TeleNP was increasingly implemented following the COVID-19 pandemic and remains an enduring and expanding feature of neuropsychology care delivery.8,14-18 TeleNP services can increase access to care, especially for rural veterans and those with limited transportation. 

Research in non-VA samples suggests a high level of clinician satisfaction with teleNP.16 In VA samples, research has found high levels of patient satisfaction with teleNP both within Veterans Integrated Services Network (VISN) 20 and in a VA health care system outside VISN 20.7,19 Investigating staff perceptions of these services and their utility compared with non-VA F2F visits is pertinent to the overall feasibility and effectiveness of teleNP. 

TELE-NEUROPSYCHOLOGY PROGRAM 

A clinical resource hub (CRH) is a VISN-governed program that provides veteran health care when local VHA facilities have service gaps.20,21 CRH 20 serves several Pacific Northwest VISN 20 health care systems and began providing teleNP in 2015. The CRH 20 teleNP service serves older adults in rural settings with > 570 teleNP evaluations completed over a recent 12-month period (May 2023 to May 2024). In the CRH 20 teleNP program, veterans are offered services by CRH 20 neuropsychologists via telehealth to a patient’s local VAMC, larger health care clinic, CBOC, or via Veterans Video Connect to the home. 

FIGURE. Usefulness of face-to-face and tele-neuropsychology evaluations and reports (N = 18). Abbreviations: VA, US Department of Veterans Affairs.
FIGURE. Usefulness of face-to-face and tele-neuropsychology evaluations and reports (N = 18). Abbreviations: VA, US Department of Veterans Affairs.

Referral pathways to the CRH 20 teleNP program differ across sites. For VISN 20 sites that do not have any in-house neuropsychology services, referrals are initiated by HCPs from any discipline. At 2 sites with in-house neuropsychology programs, CRH 20 teleNP referrals typically are forwarded from the inhouse service whenever the veteran prefers to be seen at an outlying clinic. All sites, including the CBOCs, are equipped fully for testing, and the HCP encounters veterans in a private office via video-based telehealth technology after a telehealth technician orients them to the space. The private office minimizes environmental disruptions and uses standardized technology to ensure valid results. A limited number of evaluations are offered at home (< 5% of the evaluations) if the veteran is unable to come to a VHA facility, has access to reliable internet, and a minimally distracting home setting. 

In VISN 20, teleNP is a routine practice for delivering services to rural sites, most of which lack neuropsychologists. However, there is limited information about the extent to which the referral sources find the service useful. This quality improvement (QI) project aimed to better understand how well-established teleNP services were received by referral sources/stakeholders and how services could be improved. Prior to the advent of the CRH 20 teleNP program, staff had the option of referring for F2F evaluations in the local community (outside the VA) at some sites, an option that remains. This QI project examined staff perspectives on the usefulness of CRH 20 teleNP services compared with non-VA F2F services. We administered an anonymous, confidential survey examining these factors to VISN 20 staff within 4 VA health care systems. 

METHODS 

This QI project used a mixed quantitative and qualitative descriptive survey design to elicit feedback. The authors (3 neuropsychologists, 1 geropsychologist, and 1 research coordinator) developed the survey questions. The 13-question survey was voluntary, anonymous, and confidential, and respondents were given an opportunity to ask questions, with the first author serving as the point of contact. 

The survey ascertained information about respondents and their work setting (ie, facility type, specific work setting and location, profession, and rurality of patients). First respondents were asked whether they have referred patients to neuropsychology services in the past year. Those who had not referred patients during the past year were asked about reasons for nonreferral with an option to provide an open-ended response. Respondents who did refer were asked how they refer for neuropsychology services and about the usefulness and timeliness of both teleNP and non-VA F2F services. Respondents were asked to respond with their preference for teleNP vs non-VA F2F with an open-ended prompt. Finally, respondents were invited to share any feedback for improvement regarding teleNP services. 

A link to the survey, hosted on the VA Research Electronic Data Capture system, was emailed to facility and service line leaders at the 4 VISN 20 health care systems for distribution to the staff. All staff were included because in many of the facilities, particularly those that are highly rural with low staffing, it is not uncommon for technicians, nurses, and other support staff to assist with placing consults. In particular, VISN 20 nurses often have an optimal understanding of referral pathways to care for patients and are positioned to give and receive feedback about the utility of neuropsychological evaluations. The Research and Development Committee at the Boise VA Medical Center determined this project to be QI and exempt from institutional review board oversight. The VISN 20 employee labor relations HR supervisor approved this survey, with union awareness. Responses were anonymous. 

Data were imported into Microsoft Excel and IBM SPSS Statistics for further analysis. Data were summarized using descriptive statistics, frequencies, and percentages. Nonparametric χ2 and Wilcoxon signed-rank tests were used to test for differences. An inductive approach to develop codes was used for the 3 open-ended questions. Two authors (CC, CEG) independently coded the responses and reviewed discrepancies. Final code applications were based on consensus. 

RESULTS 

The survey was deployed for 1 month between February 7, 2024, and June 15, 2024, at each of the 4 health care systems. Thirty-three staff members responded; of these, 1 person did not respond to an item on whether they referred for neuropsychology services. Eighteen of 33 respondents reported referring patients to teleNP or F2F neuropsychology services in the past year. Fourteen of the 33 respondents stated they did not refer; of these, 2 were unfamiliar with the teleNP service and 12 provided other reasons (eg, new to VA, not in their professional scope to order consults, did not have patients needing services). 

The analysis focused on the 18 respondents who referred for neuropsychology services. Thirteen were within health care system A, and 5 were within health care system B (which had no nearby non-VA contracted neuropsychology services) and none were in the other 2 health care systems. Ten of 18 respondents (56%) stated they practiced primarily in a rural setting. Five respondents worked in a CBOC, 12 in a main VA facility, 9 in a primary care setting, 8 in a mental health setting, and 3 in other settings (eg, domiciliary). Participants could select > 1 setting. The 18 respondents who referred to neuropsychology services included 7 psychologists, 1 nurse, 2 social workers, 1 social services assistant, 4 nurse practitioners, 2 physicians, and 1 unknown HCP. 

When asked to categorize the usefulness of services, more respondents characterized teleNP as very much so (1 on a 5-point scale) than F2F referrals (Figure). The mean (SD) of 1.5 (0.8) for teleNP usefulness fell between very much so and mostly and 1 respondent indicated not applicable. Similarly, the mean (SD) for non-VA F2F usefulness was 1.7 (0.9); 9 respondents rated this item as not applicable. A Wilcoxon signed-rank test of related samples indicated no significant differences between the pairs of ratings (Z = 1.50; P = .41). 

Respondents with rural patients were more likely to refer them to teleNP services compared with respondents with nonrural patients (χ2 = 5.7; P = .02). However, ratings of teleNP usefulness did not significantly differ for those serving rural vs with nonrural patients (χ2 = 1.4; P = .49). Mean (SD) rating of teleNP usefulness was 1.3 (0.7) for the 9 rural subgroup respondents (between very much so and mostly) vs 1.8 (0.9) for the 8 nonrural subgroup respondents (between very much so and mostly). The mean (SD) rating for non-VA F2F usefulness was 1.8 (1.0) for the 4 rural subgroup respondents and 1.6 (0.8) for the 5 nonrural subgroup, between very much so and mostly for both groups. 

Most respondents had no preference between teleNP or F2F. Notably, the responses underlying this group were multifaceted and corresponded to multiple codes (ie, access, preference for in-person services, technology, space and logistics, and service boundaries and requirements). According to 1 respondent, “the logistics of scheduling/room availability, technological challenges, and client behavioral issues that are likely to occur could possibly be more easily addressed via in-person sessions for some clients and providers.” 

Six of 18 respondents preferred teleNP, citing timeliness, ease of access, and evaluation quality. One respondent noted that the “majority of my veterans live in extremely remote areas” and may need to take a plane for their visit. The 3 respondents who preferred in-person neuropsychology services cited veterans’ preference for in-person services. 

Open-Ended Feedback 

Thirteen respondents offered feedback on what is working well with teleNP services. Reasons mentioned were related to the service (ie, timeliness, access, quality) and the neuropsychologist (ie, communication and HCP skills). One respondent described the service and neuropsychologists positively, stating that they were “responsive, notes are readily available, clear assessments and recommendations, being available by [Microsoft] Teams/email.” 

Ten respondents provided suggestions for improvement. Suggestions focused on expanding services, such as to “all veterans with cognitive/memory concerns that desire testing,” individuals with attention-deficit/hyperactivity disorder and co-occurring mental health concerns, and those in residential programs. Suggestions included hiring psychology technicians or more staff and providing education at local clinics. 

DISCUSSION 

This QI project examines VA staff perspectives on the usefulness of CRH 20 teleNP services and non-VA F2F services. While the small sample size limits generalizability, this preliminary study suggests that VA teleNP evaluations were similar to those conducted F2F in non-VA settings. While ratings of teleNP usefulness did not differ significantly for those serving rural vs nonrural veterans, respondents serving rural patients were more likely to refer patients to teleNP, suggesting that teleNP may increase access in rural settings, consistent with other studies.7,8,13 This article also presents qualitative suggestions for improving teleNP delivery within the VHA. This is the first known initiative to report on VHA staff satisfaction with a teleNP service and expands the limited literature to date on satisfaction with teleNP services. The findings provide initial support for continued use and, potentially, expansion of teleNP services within this CRH remote hub-and-spoke model. 

Limitations 

A significant limitation of the current work is the small sample size of survey respondents. In particular, while teleNP turnaround time was perceived as faster than non-VA F2F care, only 8 respondents reported on timeliness of F2F evaluation results, which renders it difficult to draw conclusions. Interestingly, not all respondents reported referring to neuropsychology services within the previous year; the most common reasons reflect the perception that referral to neuropsychology was outside of that staff member’s role or not clinically indicated. 

One additional possible explanation for the absence of reporting on utility of teleNP specifically is that respondents did not track whether their patient was seen by teleNP or F2F services, based on how the referral process varies at each health care system. For example, in health care system C, a large number of referrals are forwarded to the service by local VA F2F neuropsychologists. This may speak to the seamlessness of the teleNP process, such that local staff and/or referring HCPs are unaware of the modality over which neuropsychology is being conducted. It is plausible that the reason behind this smaller response rate in health care systems B and C relates to how neuropsychology consults are processed at these local VAMCs. We suspect that in these settings, the HCPs referring for neuropsychological evaluations (eg, primary care, mental health) may be unaware that their referrals are being triaged to neuropsychologists in a different program (CRH 20 teleNP). Therefore, they would not necessarily know that they used teleNP and didn’t complete the survey. 

The referral process for these 2 sites contrasts with the process for other VISN 20 sites where there is no local neuropsychology program triaging. In these settings, referrals from local HCPs come directly to teleNP; thus, it is more likely that these HCPs are aware of teleNP services. There were only 2 physicians who completed the survey, which may relate to their workload and a workflow where other staff have been increasingly requested to order the consults for the physician. This type of workflow results in an increase in the number of VHA staff involved in patient care. Ratings of usefulness were highest in health care system B, which does not have neuropsychology services at the facility or in the community; this may relate to elevated teleNP satisfaction ratings. 

Further work may help identify which aspects of a teleNP service make it more useful than F2F care for this population or determine whether there were HCPor setting-specific factors that influenced the ratings (ie, preference for VA care or comparison of favorability ratings for the HCPs who conduct teleNP and F2F within the same system). The latter comparisons could not be drawn in the current systems due to the absence of HCPs who provide both teleNP and F2F modalities within VISN 20. Another consideration for future work would be to use a previously published/validated survey measure and piloting of questions with a naive sample before implementation. 

CONCLUSIONS 

This analysis provides initial support for feasibility and acceptability of teleNP as an alternative to traditional in-person neuropsychological evaluations. The small number of survey respondents may reflect the multiple pathways through which consults are forwarded to CRH 20, which includes both direct HCP referrals and forwarded consults from local neuropsychology services. CRH 20 has completed > 570 teleNP evaluations within 1 year, suggesting that lack of awareness may not be hindering veteran access to the service. Replication with a larger sample that is more broadly representative of key stakeholders in veteran care, identification of populations that would benefit most from teleNP services, and dissemination studies of the expansion of teleNP services are all important directions for future work. The robustness and longevity of the VISN 20 teleNP program, coupled with the preliminary positive findings from this project, demonstrate support for further assessment of the potential impact of telehealth on neuropsychological care within the VHA and show that barriers associated with access to health care services in remote settings may be mitigated through teleNP service delivery.

There are 2.7 million (48%) rural veterans enrolled in the Veterans Health Administration (VHA).1 Many VHA-enrolled rural veterans are aged ≥ 65 years (54%), a medically complex population that requires more extensive health care.1 These veterans may live far from US Department of Veterans Affairs (VA) medical centers (VAMCs) and often receive most of their care at rural community-based outpatient clinics (CBOCs). In addition to face-to-face (F2F) services provided at these clinics, many patient care needs may be met using telehealth technology, which can connect veterans at CBOCs with remote health care practitioners (HCPs).

This technology is used across medical specialties throughout the VA and has expanded into neuropsychology services to improve access amid the shortage of rural neuropsychologists. Prior research suggests that access to neuropsychology services improves the functional outcomes of people with diverse medical conditions, including dementia, brain injury, and epilepsy, and reduces emergency department visits, hospitalization duration, and health care costs.2-6 Given that veterans unable to access neuropsychology services may be at risk for poorer outcomes, identifying ways to improve access is a priority. Tele-neuropsychology (teleNP) has been used to expand access for rural veterans in need of these services.7,8 

TeleNP is the application of audiovisual technologies to enable remote clinical encounters for neuropsychological assessments.9 TeleNP has been shown to be generally equivalent to F2F care, without significant differences compared with in-person visits.10-13 TeleNP was increasingly implemented following the COVID-19 pandemic and remains an enduring and expanding feature of neuropsychology care delivery.8,14-18 TeleNP services can increase access to care, especially for rural veterans and those with limited transportation. 

Research in non-VA samples suggests a high level of clinician satisfaction with teleNP.16 In VA samples, research has found high levels of patient satisfaction with teleNP both within Veterans Integrated Services Network (VISN) 20 and in a VA health care system outside VISN 20.7,19 Investigating staff perceptions of these services and their utility compared with non-VA F2F visits is pertinent to the overall feasibility and effectiveness of teleNP. 

TELE-NEUROPSYCHOLOGY PROGRAM 

A clinical resource hub (CRH) is a VISN-governed program that provides veteran health care when local VHA facilities have service gaps.20,21 CRH 20 serves several Pacific Northwest VISN 20 health care systems and began providing teleNP in 2015. The CRH 20 teleNP service serves older adults in rural settings with > 570 teleNP evaluations completed over a recent 12-month period (May 2023 to May 2024). In the CRH 20 teleNP program, veterans are offered services by CRH 20 neuropsychologists via telehealth to a patient’s local VAMC, larger health care clinic, CBOC, or via Veterans Video Connect to the home. 

FIGURE. Usefulness of face-to-face and tele-neuropsychology evaluations and reports (N = 18). Abbreviations: VA, US Department of Veterans Affairs.
FIGURE. Usefulness of face-to-face and tele-neuropsychology evaluations and reports (N = 18). Abbreviations: VA, US Department of Veterans Affairs.

Referral pathways to the CRH 20 teleNP program differ across sites. For VISN 20 sites that do not have any in-house neuropsychology services, referrals are initiated by HCPs from any discipline. At 2 sites with in-house neuropsychology programs, CRH 20 teleNP referrals typically are forwarded from the inhouse service whenever the veteran prefers to be seen at an outlying clinic. All sites, including the CBOCs, are equipped fully for testing, and the HCP encounters veterans in a private office via video-based telehealth technology after a telehealth technician orients them to the space. The private office minimizes environmental disruptions and uses standardized technology to ensure valid results. A limited number of evaluations are offered at home (< 5% of the evaluations) if the veteran is unable to come to a VHA facility, has access to reliable internet, and a minimally distracting home setting. 

In VISN 20, teleNP is a routine practice for delivering services to rural sites, most of which lack neuropsychologists. However, there is limited information about the extent to which the referral sources find the service useful. This quality improvement (QI) project aimed to better understand how well-established teleNP services were received by referral sources/stakeholders and how services could be improved. Prior to the advent of the CRH 20 teleNP program, staff had the option of referring for F2F evaluations in the local community (outside the VA) at some sites, an option that remains. This QI project examined staff perspectives on the usefulness of CRH 20 teleNP services compared with non-VA F2F services. We administered an anonymous, confidential survey examining these factors to VISN 20 staff within 4 VA health care systems. 

METHODS 

This QI project used a mixed quantitative and qualitative descriptive survey design to elicit feedback. The authors (3 neuropsychologists, 1 geropsychologist, and 1 research coordinator) developed the survey questions. The 13-question survey was voluntary, anonymous, and confidential, and respondents were given an opportunity to ask questions, with the first author serving as the point of contact. 

The survey ascertained information about respondents and their work setting (ie, facility type, specific work setting and location, profession, and rurality of patients). First respondents were asked whether they have referred patients to neuropsychology services in the past year. Those who had not referred patients during the past year were asked about reasons for nonreferral with an option to provide an open-ended response. Respondents who did refer were asked how they refer for neuropsychology services and about the usefulness and timeliness of both teleNP and non-VA F2F services. Respondents were asked to respond with their preference for teleNP vs non-VA F2F with an open-ended prompt. Finally, respondents were invited to share any feedback for improvement regarding teleNP services. 

A link to the survey, hosted on the VA Research Electronic Data Capture system, was emailed to facility and service line leaders at the 4 VISN 20 health care systems for distribution to the staff. All staff were included because in many of the facilities, particularly those that are highly rural with low staffing, it is not uncommon for technicians, nurses, and other support staff to assist with placing consults. In particular, VISN 20 nurses often have an optimal understanding of referral pathways to care for patients and are positioned to give and receive feedback about the utility of neuropsychological evaluations. The Research and Development Committee at the Boise VA Medical Center determined this project to be QI and exempt from institutional review board oversight. The VISN 20 employee labor relations HR supervisor approved this survey, with union awareness. Responses were anonymous. 

Data were imported into Microsoft Excel and IBM SPSS Statistics for further analysis. Data were summarized using descriptive statistics, frequencies, and percentages. Nonparametric χ2 and Wilcoxon signed-rank tests were used to test for differences. An inductive approach to develop codes was used for the 3 open-ended questions. Two authors (CC, CEG) independently coded the responses and reviewed discrepancies. Final code applications were based on consensus. 

RESULTS 

The survey was deployed for 1 month between February 7, 2024, and June 15, 2024, at each of the 4 health care systems. Thirty-three staff members responded; of these, 1 person did not respond to an item on whether they referred for neuropsychology services. Eighteen of 33 respondents reported referring patients to teleNP or F2F neuropsychology services in the past year. Fourteen of the 33 respondents stated they did not refer; of these, 2 were unfamiliar with the teleNP service and 12 provided other reasons (eg, new to VA, not in their professional scope to order consults, did not have patients needing services). 

The analysis focused on the 18 respondents who referred for neuropsychology services. Thirteen were within health care system A, and 5 were within health care system B (which had no nearby non-VA contracted neuropsychology services) and none were in the other 2 health care systems. Ten of 18 respondents (56%) stated they practiced primarily in a rural setting. Five respondents worked in a CBOC, 12 in a main VA facility, 9 in a primary care setting, 8 in a mental health setting, and 3 in other settings (eg, domiciliary). Participants could select > 1 setting. The 18 respondents who referred to neuropsychology services included 7 psychologists, 1 nurse, 2 social workers, 1 social services assistant, 4 nurse practitioners, 2 physicians, and 1 unknown HCP. 

When asked to categorize the usefulness of services, more respondents characterized teleNP as very much so (1 on a 5-point scale) than F2F referrals (Figure). The mean (SD) of 1.5 (0.8) for teleNP usefulness fell between very much so and mostly and 1 respondent indicated not applicable. Similarly, the mean (SD) for non-VA F2F usefulness was 1.7 (0.9); 9 respondents rated this item as not applicable. A Wilcoxon signed-rank test of related samples indicated no significant differences between the pairs of ratings (Z = 1.50; P = .41). 

Respondents with rural patients were more likely to refer them to teleNP services compared with respondents with nonrural patients (χ2 = 5.7; P = .02). However, ratings of teleNP usefulness did not significantly differ for those serving rural vs with nonrural patients (χ2 = 1.4; P = .49). Mean (SD) rating of teleNP usefulness was 1.3 (0.7) for the 9 rural subgroup respondents (between very much so and mostly) vs 1.8 (0.9) for the 8 nonrural subgroup respondents (between very much so and mostly). The mean (SD) rating for non-VA F2F usefulness was 1.8 (1.0) for the 4 rural subgroup respondents and 1.6 (0.8) for the 5 nonrural subgroup, between very much so and mostly for both groups. 

Most respondents had no preference between teleNP or F2F. Notably, the responses underlying this group were multifaceted and corresponded to multiple codes (ie, access, preference for in-person services, technology, space and logistics, and service boundaries and requirements). According to 1 respondent, “the logistics of scheduling/room availability, technological challenges, and client behavioral issues that are likely to occur could possibly be more easily addressed via in-person sessions for some clients and providers.” 

Six of 18 respondents preferred teleNP, citing timeliness, ease of access, and evaluation quality. One respondent noted that the “majority of my veterans live in extremely remote areas” and may need to take a plane for their visit. The 3 respondents who preferred in-person neuropsychology services cited veterans’ preference for in-person services. 

Open-Ended Feedback 

Thirteen respondents offered feedback on what is working well with teleNP services. Reasons mentioned were related to the service (ie, timeliness, access, quality) and the neuropsychologist (ie, communication and HCP skills). One respondent described the service and neuropsychologists positively, stating that they were “responsive, notes are readily available, clear assessments and recommendations, being available by [Microsoft] Teams/email.” 

Ten respondents provided suggestions for improvement. Suggestions focused on expanding services, such as to “all veterans with cognitive/memory concerns that desire testing,” individuals with attention-deficit/hyperactivity disorder and co-occurring mental health concerns, and those in residential programs. Suggestions included hiring psychology technicians or more staff and providing education at local clinics. 

DISCUSSION 

This QI project examines VA staff perspectives on the usefulness of CRH 20 teleNP services and non-VA F2F services. While the small sample size limits generalizability, this preliminary study suggests that VA teleNP evaluations were similar to those conducted F2F in non-VA settings. While ratings of teleNP usefulness did not differ significantly for those serving rural vs nonrural veterans, respondents serving rural patients were more likely to refer patients to teleNP, suggesting that teleNP may increase access in rural settings, consistent with other studies.7,8,13 This article also presents qualitative suggestions for improving teleNP delivery within the VHA. This is the first known initiative to report on VHA staff satisfaction with a teleNP service and expands the limited literature to date on satisfaction with teleNP services. The findings provide initial support for continued use and, potentially, expansion of teleNP services within this CRH remote hub-and-spoke model. 

Limitations 

A significant limitation of the current work is the small sample size of survey respondents. In particular, while teleNP turnaround time was perceived as faster than non-VA F2F care, only 8 respondents reported on timeliness of F2F evaluation results, which renders it difficult to draw conclusions. Interestingly, not all respondents reported referring to neuropsychology services within the previous year; the most common reasons reflect the perception that referral to neuropsychology was outside of that staff member’s role or not clinically indicated. 

One additional possible explanation for the absence of reporting on utility of teleNP specifically is that respondents did not track whether their patient was seen by teleNP or F2F services, based on how the referral process varies at each health care system. For example, in health care system C, a large number of referrals are forwarded to the service by local VA F2F neuropsychologists. This may speak to the seamlessness of the teleNP process, such that local staff and/or referring HCPs are unaware of the modality over which neuropsychology is being conducted. It is plausible that the reason behind this smaller response rate in health care systems B and C relates to how neuropsychology consults are processed at these local VAMCs. We suspect that in these settings, the HCPs referring for neuropsychological evaluations (eg, primary care, mental health) may be unaware that their referrals are being triaged to neuropsychologists in a different program (CRH 20 teleNP). Therefore, they would not necessarily know that they used teleNP and didn’t complete the survey. 

The referral process for these 2 sites contrasts with the process for other VISN 20 sites where there is no local neuropsychology program triaging. In these settings, referrals from local HCPs come directly to teleNP; thus, it is more likely that these HCPs are aware of teleNP services. There were only 2 physicians who completed the survey, which may relate to their workload and a workflow where other staff have been increasingly requested to order the consults for the physician. This type of workflow results in an increase in the number of VHA staff involved in patient care. Ratings of usefulness were highest in health care system B, which does not have neuropsychology services at the facility or in the community; this may relate to elevated teleNP satisfaction ratings. 

Further work may help identify which aspects of a teleNP service make it more useful than F2F care for this population or determine whether there were HCPor setting-specific factors that influenced the ratings (ie, preference for VA care or comparison of favorability ratings for the HCPs who conduct teleNP and F2F within the same system). The latter comparisons could not be drawn in the current systems due to the absence of HCPs who provide both teleNP and F2F modalities within VISN 20. Another consideration for future work would be to use a previously published/validated survey measure and piloting of questions with a naive sample before implementation. 

CONCLUSIONS 

This analysis provides initial support for feasibility and acceptability of teleNP as an alternative to traditional in-person neuropsychological evaluations. The small number of survey respondents may reflect the multiple pathways through which consults are forwarded to CRH 20, which includes both direct HCP referrals and forwarded consults from local neuropsychology services. CRH 20 has completed > 570 teleNP evaluations within 1 year, suggesting that lack of awareness may not be hindering veteran access to the service. Replication with a larger sample that is more broadly representative of key stakeholders in veteran care, identification of populations that would benefit most from teleNP services, and dissemination studies of the expansion of teleNP services are all important directions for future work. The robustness and longevity of the VISN 20 teleNP program, coupled with the preliminary positive findings from this project, demonstrate support for further assessment of the potential impact of telehealth on neuropsychological care within the VHA and show that barriers associated with access to health care services in remote settings may be mitigated through teleNP service delivery.

References
  1. US Department of Veterans Affairs, Office of Rural Health. Rural veterans. Updated March 10, 2025. Accessed July 7, 2025. https://www.ruralhealth.va.gov/aboutus/ruralvets.asp
  2. Braun M, Tupper D, Kaufmann P, et al. Neuropsychological assessment: a valuable tool in the diagnosis and management of neurological, neurodevelopmental, medical, and psychiatric disorders. Cogn Behav Neurol. 2011;24(3):107-114. doi:10.1097/wnn.0b013e3182351289
  3. Donders J. The incremental value of neuropsychological assessment: a critical review. Clin Neuropsychol. 2020;34(1):56-87. doi:10.1080/13854046.2019.1575471
  4. Williams MW, Rapport LJ, Hanks RA, et al. Incremental value of neuropsychological evaluations to computed tomography in predicting long-term outcomes after traumatic brain injury. Clin Neuropsychol. 2013;27(3):356-375. doi:10.1080/13854046.2013.765507
  5. Sieg E, Mai Q, Mosti C, Brook M. The utility of neuropsychological consultation in identifying medical inpatients with suspected cognitive impairment at risk for greater hospital utilization. Clin Neuropsychol. 2019;33(1):75-89. doi:10.1080/13854046.2018.1465124
  6. Vankirk KM, Horner MD, Turner TH, et al. CE hospital service utilization is reduced following neuropsychological evaluation in a sample of U.S. veterans. Clin Neuropsychol. 2013;27(5):750-761. doi:10.1080/13854046.2013.783122
  7. Appleman ER, O’Connor MK, Boucher SJ, et al. Teleneuropsychology clinic development and patient satisfaction. Clin Neuropsychol. 2021;35(4):819-837. doi:10.1080/13854046.2020.1871515
  8. Stelmokas J, Ratcliffe LN, Lengu K, et al. Evaluation of teleneuropsychology services in veterans during COVID-19. Psychol Serv. 2024;21(1):65-72. doi:10.1037/ser0000810
  9. Bilder R Postal KS, Barisa M, et al. Inter Organizational Practice Committee recommendations/guidance for teleneuropsychology in response to the COVID-19 pandemic. Arch Clin Neuropsychol. 2020;35(6):647-659. doi:10.1093/arclin/acaa046
  10. Brearly TW, Shura RD, Martindale SL, et al. Neuropsychological test administration by videoconference: a systematic review and meta-analysis. Neuropsychol Rev. 2017;27(2):174-186. doi:10.1007/s11065-017-9349-1
  11. Brown AD, Kelso W, Eratne D, et al. Investigating equivalence of in-person and telehealth-based neuropsychological assessment performance for individuals being investigated for younger onset dementia. Arch Clin Neuropsychol. 2024;39(5):594-607. doi:10.1093/arclin/acad108
  12. Chapman JE, Ponsford J, Bagot KL, et al. The use of videoconferencing in clinical neuropsychology practice: a mixed methods evaluation of neuropsychologists’ experiences and views. Aust Psychol. 2020;55(6):618-633. doi:10.1111/ap.12471
  13. Marra DE, Hamlet KM, Bauer RM, et al. Validity of teleneuropsychology for older adults in response to COVID-19: a systematic and critical review. Clin Neuropsychol. 2020;34:1411-1452. doi:10.1080/13854046.2020.1769192
  14. Hammers DB, Stolwyk R, Harder L, et al. A survey of international clinical teleneuropsychology service provision prior to COVID-19. Clin Neuropsychol. 2020;34(7-8):1267- 1283. doi:10.1080/13854046.2020.1810323
  15. Marra DE, Hoelzle JB, Davis JJ, et al. Initial changes in neuropsychologists’ clinical practice during the COVID-19 pandemic: a survey study. Clin Neuropsychol. 2020;34(7- 8):1251-1266. doi:10.1080/13854046.2020.1800098
  16. Parsons MW, Gardner MM, Sherman, JC et al. Feasibility and acceptance of direct-to-home teleneuropsychology services during the COVID-19 pandemic. J Int Neuropsychol Soc. 2022;28(2):210-215. doi:10.1017/s1355617721000436
  17. Rochette AD, Rahman-Filipiak A, Spencer RJ, et al. Teleneuropsychology practice survey during COVID-19 within the United States. Appl Neuropsychol Adult. 2022;29(6):1312- 1322. doi:10.1080/23279095.2021.1872576
  18. Messler AC, Hargrave DD, Trittschuh EH, et al. National survey of telehealth neuropsychology practices: current attitudes, practices, and relevance of tele-neuropsychology three years after the onset of COVID-19. Clin Neuropsychol. 2023;39:1017-1036. doi:10.1080/13854046.2023.2192422
  19. Rautman L, Sordahl JA. Veteran satisfaction with tele-neuropsychology services. Clin Neuropsychol. 2018;32:1453949. doi:10.1080/13854046.2018.1453949
  20. US Department of Veterans Affairs. Patient care services: clinical resource hubs. Updated March 20, 2024. Accessed August 4, 2025. https://www.patientcare .va.gov/primarycare/CRH.asp  
  21. Burnett K, Stockdale SE, Yoon J, et al. The Clinical Resource Hub initiative: first-year implementation of the Veterans Health Administration regional telehealth contingency staffing program. Ambul Care Manage. 2023;46(3):228-239. doi:10.1097/JAC.0000000000000468
References
  1. US Department of Veterans Affairs, Office of Rural Health. Rural veterans. Updated March 10, 2025. Accessed July 7, 2025. https://www.ruralhealth.va.gov/aboutus/ruralvets.asp
  2. Braun M, Tupper D, Kaufmann P, et al. Neuropsychological assessment: a valuable tool in the diagnosis and management of neurological, neurodevelopmental, medical, and psychiatric disorders. Cogn Behav Neurol. 2011;24(3):107-114. doi:10.1097/wnn.0b013e3182351289
  3. Donders J. The incremental value of neuropsychological assessment: a critical review. Clin Neuropsychol. 2020;34(1):56-87. doi:10.1080/13854046.2019.1575471
  4. Williams MW, Rapport LJ, Hanks RA, et al. Incremental value of neuropsychological evaluations to computed tomography in predicting long-term outcomes after traumatic brain injury. Clin Neuropsychol. 2013;27(3):356-375. doi:10.1080/13854046.2013.765507
  5. Sieg E, Mai Q, Mosti C, Brook M. The utility of neuropsychological consultation in identifying medical inpatients with suspected cognitive impairment at risk for greater hospital utilization. Clin Neuropsychol. 2019;33(1):75-89. doi:10.1080/13854046.2018.1465124
  6. Vankirk KM, Horner MD, Turner TH, et al. CE hospital service utilization is reduced following neuropsychological evaluation in a sample of U.S. veterans. Clin Neuropsychol. 2013;27(5):750-761. doi:10.1080/13854046.2013.783122
  7. Appleman ER, O’Connor MK, Boucher SJ, et al. Teleneuropsychology clinic development and patient satisfaction. Clin Neuropsychol. 2021;35(4):819-837. doi:10.1080/13854046.2020.1871515
  8. Stelmokas J, Ratcliffe LN, Lengu K, et al. Evaluation of teleneuropsychology services in veterans during COVID-19. Psychol Serv. 2024;21(1):65-72. doi:10.1037/ser0000810
  9. Bilder R Postal KS, Barisa M, et al. Inter Organizational Practice Committee recommendations/guidance for teleneuropsychology in response to the COVID-19 pandemic. Arch Clin Neuropsychol. 2020;35(6):647-659. doi:10.1093/arclin/acaa046
  10. Brearly TW, Shura RD, Martindale SL, et al. Neuropsychological test administration by videoconference: a systematic review and meta-analysis. Neuropsychol Rev. 2017;27(2):174-186. doi:10.1007/s11065-017-9349-1
  11. Brown AD, Kelso W, Eratne D, et al. Investigating equivalence of in-person and telehealth-based neuropsychological assessment performance for individuals being investigated for younger onset dementia. Arch Clin Neuropsychol. 2024;39(5):594-607. doi:10.1093/arclin/acad108
  12. Chapman JE, Ponsford J, Bagot KL, et al. The use of videoconferencing in clinical neuropsychology practice: a mixed methods evaluation of neuropsychologists’ experiences and views. Aust Psychol. 2020;55(6):618-633. doi:10.1111/ap.12471
  13. Marra DE, Hamlet KM, Bauer RM, et al. Validity of teleneuropsychology for older adults in response to COVID-19: a systematic and critical review. Clin Neuropsychol. 2020;34:1411-1452. doi:10.1080/13854046.2020.1769192
  14. Hammers DB, Stolwyk R, Harder L, et al. A survey of international clinical teleneuropsychology service provision prior to COVID-19. Clin Neuropsychol. 2020;34(7-8):1267- 1283. doi:10.1080/13854046.2020.1810323
  15. Marra DE, Hoelzle JB, Davis JJ, et al. Initial changes in neuropsychologists’ clinical practice during the COVID-19 pandemic: a survey study. Clin Neuropsychol. 2020;34(7- 8):1251-1266. doi:10.1080/13854046.2020.1800098
  16. Parsons MW, Gardner MM, Sherman, JC et al. Feasibility and acceptance of direct-to-home teleneuropsychology services during the COVID-19 pandemic. J Int Neuropsychol Soc. 2022;28(2):210-215. doi:10.1017/s1355617721000436
  17. Rochette AD, Rahman-Filipiak A, Spencer RJ, et al. Teleneuropsychology practice survey during COVID-19 within the United States. Appl Neuropsychol Adult. 2022;29(6):1312- 1322. doi:10.1080/23279095.2021.1872576
  18. Messler AC, Hargrave DD, Trittschuh EH, et al. National survey of telehealth neuropsychology practices: current attitudes, practices, and relevance of tele-neuropsychology three years after the onset of COVID-19. Clin Neuropsychol. 2023;39:1017-1036. doi:10.1080/13854046.2023.2192422
  19. Rautman L, Sordahl JA. Veteran satisfaction with tele-neuropsychology services. Clin Neuropsychol. 2018;32:1453949. doi:10.1080/13854046.2018.1453949
  20. US Department of Veterans Affairs. Patient care services: clinical resource hubs. Updated March 20, 2024. Accessed August 4, 2025. https://www.patientcare .va.gov/primarycare/CRH.asp  
  21. Burnett K, Stockdale SE, Yoon J, et al. The Clinical Resource Hub initiative: first-year implementation of the Veterans Health Administration regional telehealth contingency staffing program. Ambul Care Manage. 2023;46(3):228-239. doi:10.1097/JAC.0000000000000468
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The Role of Dermatologists in Developing AI Tools for Diagnosis and Classification of Skin Disease

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The Role of Dermatologists in Developing AI Tools for Diagnosis and Classification of Skin Disease

Use of artificial intelligence (AI) in dermatology has increased over the past decade, likely driven by advances in deep learning algorithms, computing hardware, and machine learning.1 Studies comparing the performance of AI algorithms to dermatologists in classifying skin disorders have shown conflicting results.2,3 In this study, we aimed to analyze AI tools used for diagnosing and classifying skin disease and evaluate the role of dermatologists in the creation of AI technology. We also investigated the number of clinical images used in datasets to train AI programs and compared tools that were created with dermatologist input to those created without dermatologist/clinician involvement.

Methods

A search of PubMed articles indexed for MEDLINE using the terms machine learning, artificial intelligence, and dermatology was conducted on September 18, 2022. Articles were included if they described full-length trials; used machine learning for diagnosis of or screening for dermatologic conditions; and used dermoscopic or gross image datasets of the skin, hair, or nails. Articles were categorized into 4 groups based on the conditions covered: chronic wounds, inflammatory skin diseases, mixed conditions, and pigmented skin lesions. Algorithms were sorted into 4 categories: convolutional/convoluted neural network, deep learning model/deep neural network, AI/artificial neural network, and other. Details regarding Fitzpatrick skin type and skin of color (SoC) inclusion in the articles or AI algorithm datasets were recorded. Univariate and multivariate analyses were performed using Microsoft Excel and SAS Studio 3.8. Sensitivity and specificity were calculated for all included AI technology. Sensitivity, specificity, and the number of clinical images were compared among the included articles using analysis of variance and t tests (α=0.05; P<.05 indicated statistical significance).

Results

Our search yielded 1016 articles, 58 of which met the inclusion criteria. Overall, 25.9% (15/58) of the articles utilized AI to diagnose or classify mixed skin diseases; 22.4% (13/58) for pigmented skin lesions; 19.0% (11/58) for wounds; 17.2% (10/58) for inflammatory skin diseases; and 5.2% (3/58) each for acne, psoriasis, and onychomycosis. Overall, 24.0% (14/58) of articles provided information about Fitzpatrick skin type, and 58.7% (34/58) included clinical images depicting SoC. Furthermore, we found that only 20.7% (12/58) of articles on deep learning models included descriptions of patient ethnicity or race in at least 1 dataset, and only 10.3% (6/58) of studies included any information about skin tone in the dataset. Studies with a dermatologist as the last author (most likely to be supervising the project) were more likely to include clinical images depicting SoC than those without (82.6% [19/23] and 16.7% [3/18], respectively [P=.0411]).

The mean (SD) number of clinical images in the study articles was 28,422 (84,050). Thirty-seven (63.8%) of the study articles included gross images, 17 (29.3%) used dermoscopic images, and 4 (6.9%) used both. Twenty-seven (46.6%) articles used convolutional/convoluted neural networks, 15 (25.9%) used deep learning model/deep neural networks, 8 (13.8%) used other algorithms, 6 (10.3%) used AI/artificial neural network, and 2 (3.4%) used fuzzy algorithms. Most studies were conducted in China (29.3% [17/58]), Germany (12.1% [7/58]), India (10.3% [6/58]), multiple nations (10.3% [6/58]), and the United States (10.3% [6/58]). Overall, 82.8% (48/58) of articles included at least 1 dermatologist coauthor. Sensitivity of the AI models was 0.85, and specificity was 0.85. The average percentage of images in the dataset correctly identified by a physician was 76.87% vs 81.62% of images correctly identified by AI. Average agreement between AI and physician assessment was 77.98%, defined as AI and physician both having the same diagnosis. 

Articles authored by dermatologists contained more clinical images than those without dermatologists in key authorship roles (P<.0001)(eTable). Psoriasis-related algorithms had the fewest (mean [SD]: 3173 [4203]), and pigmented skin lesions had the most clinical images (mean [SD]: 53,19l [155,579]).

RagiCT116005184-eTable

Comment

Our results indicated that AI studies with dermatologist authors had significantly more images in their datasets (ie, the set of clinical images of skin lesions used to train AI algorithms in diagnosing or classifying lesions) than those with nondermatologist authors (P<.0001)(eTable). Similarly, in a study of AI technology for skin cancer diagnosis, AI studies with dermatologist authors (ie, included in the development of the AI algorithm) had more images than studies without dermatologist authors.1 Deep learning textbooks have suggested that 5000 clinical images or training input per output category are needed to produce acceptable algorithm performance, and more than 10 million are needed to produce results superior to human performance.4-10 Despite advances in AI for dermatologic image analysis, the creation of these models often has been directed by nondermatologists1; therefore, dermatologist involvement in AI development is necessary to facilitate collection of larger image datasets and optimal performance for image diagnosis/classification tasks.

We found that 20.7% of articles on deep learning models included descriptions of patient ethnicity or race, and only 10.3% of studies included any information about skin tone in the dataset. Furthermore, American investigators primarily trained models using clinical images of patients with lighter skin tones, whereas Chinese investigators exclusively included images depicting darker skin tones. Similarly, in a study of 52 cutaneous imaging deep learning articles, only 17.3% (9/52) reported race and/or Fitzpatrick skin type, and only 7.7% (4/52) of articles included both.2,6,8 Therefore, dermatologists are needed to contribute images representing diverse populations and collaborate in AI research studies, as their involvement is necessary to ensure the accuracy of AI models in classifying lesions or diagnosing skin lesions across all skin types.

Our search was limited to PubMed, and real-world applications could not be evaluated.

Conclusion

In summary, we found that AI studies with dermatologist authors used larger numbers of clinical images in their datasets and more images representing diverse skin types than studies without. Therefore, we advocate for greater involvement of dermatologists in AI research, which might result in better patient outcomes by improving diagnostic accuracy.

References
  1. Zakhem GA, Fakhoury JW, Motosko CC, et al. Characterizing the role of dermatologists in developing artificial intelligence for assessment of skin cancer. J Am Acad Dermatol. 2021;85:1544-1556.
  2. Daneshjou R, Vodrahalli K, Novoa RA, et al. Disparities in dermatology AI performance on a diverse, curated clinical image set. Sci Adv. 2022;8:eabq6147.
  3. Wu E, Wu K, Daneshjou R, et al. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat Med. 2021;27:582-584.
  4. Murphree DH, Puri P, Shamim H, et al. Deep learning for dermatologists: part I. Fundamental concepts. J Am Acad Dermatol. 2022;87:1343-1351.
  5. Goodfellow I, Bengio Y, Courville A. Deep Learning. The MIT Press; 2016.
  6. Kim YH, Kobic A, Vidal NY. Distribution of race and Fitzpatrick skin types in data sets for deep learning in dermatology: a systematic review. J Am Acad Dermatol. 2022;87:460-461.
  7. Liu Y, Jain A, Eng C, et al. A deep learning system for differential diagnosis of skin diseases. Nat Med. 2020;26:900-908.
  8. Zhu CY, Wang YK, Chen HP, et al. A deep learning based framework for diagnosing multiple skin diseases in a clinical environment. Front Med (Lausanne). 2021;8:626369.
  9. Capurro N, Pastore VP, Touijer L, et al. A deep learning approach to direct immunofluorescence pattern recognition in autoimmune bullous diseases. Br J Dermatol. 2024;191:261-266.
  10. Han SS, Park I, Eun Chang S, et al. Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders. J Invest Dermatol. 2020;140:1753-1761.
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Author and Disclosure Information

Dr. Ragi is from the Warren Alpert Medical School of Brown University, Providence, Rhode Island. Dr. Desai is from Rutgers New Jersey Medical School, Newark. Drs. Hill and Lipner are from Weill Cornell Medical College, New York, New York. Dr. Lipner is from the Department of Dermatology.

The authors have no relevant financial disclosures to report.

Correspondence: Shari R. Lipner, MD, PhD, Associate Professor of Clinical Dermatology, Weill Cornell Medicine, 1305 York Ave, 9th Floor, New York, NY 10021 ([email protected]).

Cutis. 2025 November;116(5):184-185, E4. doi:10.12788/cutis.1295

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Dr. Ragi is from the Warren Alpert Medical School of Brown University, Providence, Rhode Island. Dr. Desai is from Rutgers New Jersey Medical School, Newark. Drs. Hill and Lipner are from Weill Cornell Medical College, New York, New York. Dr. Lipner is from the Department of Dermatology.

The authors have no relevant financial disclosures to report.

Correspondence: Shari R. Lipner, MD, PhD, Associate Professor of Clinical Dermatology, Weill Cornell Medicine, 1305 York Ave, 9th Floor, New York, NY 10021 ([email protected]).

Cutis. 2025 November;116(5):184-185, E4. doi:10.12788/cutis.1295

Author and Disclosure Information

Dr. Ragi is from the Warren Alpert Medical School of Brown University, Providence, Rhode Island. Dr. Desai is from Rutgers New Jersey Medical School, Newark. Drs. Hill and Lipner are from Weill Cornell Medical College, New York, New York. Dr. Lipner is from the Department of Dermatology.

The authors have no relevant financial disclosures to report.

Correspondence: Shari R. Lipner, MD, PhD, Associate Professor of Clinical Dermatology, Weill Cornell Medicine, 1305 York Ave, 9th Floor, New York, NY 10021 ([email protected]).

Cutis. 2025 November;116(5):184-185, E4. doi:10.12788/cutis.1295

Article PDF
Article PDF

Use of artificial intelligence (AI) in dermatology has increased over the past decade, likely driven by advances in deep learning algorithms, computing hardware, and machine learning.1 Studies comparing the performance of AI algorithms to dermatologists in classifying skin disorders have shown conflicting results.2,3 In this study, we aimed to analyze AI tools used for diagnosing and classifying skin disease and evaluate the role of dermatologists in the creation of AI technology. We also investigated the number of clinical images used in datasets to train AI programs and compared tools that were created with dermatologist input to those created without dermatologist/clinician involvement.

Methods

A search of PubMed articles indexed for MEDLINE using the terms machine learning, artificial intelligence, and dermatology was conducted on September 18, 2022. Articles were included if they described full-length trials; used machine learning for diagnosis of or screening for dermatologic conditions; and used dermoscopic or gross image datasets of the skin, hair, or nails. Articles were categorized into 4 groups based on the conditions covered: chronic wounds, inflammatory skin diseases, mixed conditions, and pigmented skin lesions. Algorithms were sorted into 4 categories: convolutional/convoluted neural network, deep learning model/deep neural network, AI/artificial neural network, and other. Details regarding Fitzpatrick skin type and skin of color (SoC) inclusion in the articles or AI algorithm datasets were recorded. Univariate and multivariate analyses were performed using Microsoft Excel and SAS Studio 3.8. Sensitivity and specificity were calculated for all included AI technology. Sensitivity, specificity, and the number of clinical images were compared among the included articles using analysis of variance and t tests (α=0.05; P<.05 indicated statistical significance).

Results

Our search yielded 1016 articles, 58 of which met the inclusion criteria. Overall, 25.9% (15/58) of the articles utilized AI to diagnose or classify mixed skin diseases; 22.4% (13/58) for pigmented skin lesions; 19.0% (11/58) for wounds; 17.2% (10/58) for inflammatory skin diseases; and 5.2% (3/58) each for acne, psoriasis, and onychomycosis. Overall, 24.0% (14/58) of articles provided information about Fitzpatrick skin type, and 58.7% (34/58) included clinical images depicting SoC. Furthermore, we found that only 20.7% (12/58) of articles on deep learning models included descriptions of patient ethnicity or race in at least 1 dataset, and only 10.3% (6/58) of studies included any information about skin tone in the dataset. Studies with a dermatologist as the last author (most likely to be supervising the project) were more likely to include clinical images depicting SoC than those without (82.6% [19/23] and 16.7% [3/18], respectively [P=.0411]).

The mean (SD) number of clinical images in the study articles was 28,422 (84,050). Thirty-seven (63.8%) of the study articles included gross images, 17 (29.3%) used dermoscopic images, and 4 (6.9%) used both. Twenty-seven (46.6%) articles used convolutional/convoluted neural networks, 15 (25.9%) used deep learning model/deep neural networks, 8 (13.8%) used other algorithms, 6 (10.3%) used AI/artificial neural network, and 2 (3.4%) used fuzzy algorithms. Most studies were conducted in China (29.3% [17/58]), Germany (12.1% [7/58]), India (10.3% [6/58]), multiple nations (10.3% [6/58]), and the United States (10.3% [6/58]). Overall, 82.8% (48/58) of articles included at least 1 dermatologist coauthor. Sensitivity of the AI models was 0.85, and specificity was 0.85. The average percentage of images in the dataset correctly identified by a physician was 76.87% vs 81.62% of images correctly identified by AI. Average agreement between AI and physician assessment was 77.98%, defined as AI and physician both having the same diagnosis. 

Articles authored by dermatologists contained more clinical images than those without dermatologists in key authorship roles (P<.0001)(eTable). Psoriasis-related algorithms had the fewest (mean [SD]: 3173 [4203]), and pigmented skin lesions had the most clinical images (mean [SD]: 53,19l [155,579]).

RagiCT116005184-eTable

Comment

Our results indicated that AI studies with dermatologist authors had significantly more images in their datasets (ie, the set of clinical images of skin lesions used to train AI algorithms in diagnosing or classifying lesions) than those with nondermatologist authors (P<.0001)(eTable). Similarly, in a study of AI technology for skin cancer diagnosis, AI studies with dermatologist authors (ie, included in the development of the AI algorithm) had more images than studies without dermatologist authors.1 Deep learning textbooks have suggested that 5000 clinical images or training input per output category are needed to produce acceptable algorithm performance, and more than 10 million are needed to produce results superior to human performance.4-10 Despite advances in AI for dermatologic image analysis, the creation of these models often has been directed by nondermatologists1; therefore, dermatologist involvement in AI development is necessary to facilitate collection of larger image datasets and optimal performance for image diagnosis/classification tasks.

We found that 20.7% of articles on deep learning models included descriptions of patient ethnicity or race, and only 10.3% of studies included any information about skin tone in the dataset. Furthermore, American investigators primarily trained models using clinical images of patients with lighter skin tones, whereas Chinese investigators exclusively included images depicting darker skin tones. Similarly, in a study of 52 cutaneous imaging deep learning articles, only 17.3% (9/52) reported race and/or Fitzpatrick skin type, and only 7.7% (4/52) of articles included both.2,6,8 Therefore, dermatologists are needed to contribute images representing diverse populations and collaborate in AI research studies, as their involvement is necessary to ensure the accuracy of AI models in classifying lesions or diagnosing skin lesions across all skin types.

Our search was limited to PubMed, and real-world applications could not be evaluated.

Conclusion

In summary, we found that AI studies with dermatologist authors used larger numbers of clinical images in their datasets and more images representing diverse skin types than studies without. Therefore, we advocate for greater involvement of dermatologists in AI research, which might result in better patient outcomes by improving diagnostic accuracy.

Use of artificial intelligence (AI) in dermatology has increased over the past decade, likely driven by advances in deep learning algorithms, computing hardware, and machine learning.1 Studies comparing the performance of AI algorithms to dermatologists in classifying skin disorders have shown conflicting results.2,3 In this study, we aimed to analyze AI tools used for diagnosing and classifying skin disease and evaluate the role of dermatologists in the creation of AI technology. We also investigated the number of clinical images used in datasets to train AI programs and compared tools that were created with dermatologist input to those created without dermatologist/clinician involvement.

Methods

A search of PubMed articles indexed for MEDLINE using the terms machine learning, artificial intelligence, and dermatology was conducted on September 18, 2022. Articles were included if they described full-length trials; used machine learning for diagnosis of or screening for dermatologic conditions; and used dermoscopic or gross image datasets of the skin, hair, or nails. Articles were categorized into 4 groups based on the conditions covered: chronic wounds, inflammatory skin diseases, mixed conditions, and pigmented skin lesions. Algorithms were sorted into 4 categories: convolutional/convoluted neural network, deep learning model/deep neural network, AI/artificial neural network, and other. Details regarding Fitzpatrick skin type and skin of color (SoC) inclusion in the articles or AI algorithm datasets were recorded. Univariate and multivariate analyses were performed using Microsoft Excel and SAS Studio 3.8. Sensitivity and specificity were calculated for all included AI technology. Sensitivity, specificity, and the number of clinical images were compared among the included articles using analysis of variance and t tests (α=0.05; P<.05 indicated statistical significance).

Results

Our search yielded 1016 articles, 58 of which met the inclusion criteria. Overall, 25.9% (15/58) of the articles utilized AI to diagnose or classify mixed skin diseases; 22.4% (13/58) for pigmented skin lesions; 19.0% (11/58) for wounds; 17.2% (10/58) for inflammatory skin diseases; and 5.2% (3/58) each for acne, psoriasis, and onychomycosis. Overall, 24.0% (14/58) of articles provided information about Fitzpatrick skin type, and 58.7% (34/58) included clinical images depicting SoC. Furthermore, we found that only 20.7% (12/58) of articles on deep learning models included descriptions of patient ethnicity or race in at least 1 dataset, and only 10.3% (6/58) of studies included any information about skin tone in the dataset. Studies with a dermatologist as the last author (most likely to be supervising the project) were more likely to include clinical images depicting SoC than those without (82.6% [19/23] and 16.7% [3/18], respectively [P=.0411]).

The mean (SD) number of clinical images in the study articles was 28,422 (84,050). Thirty-seven (63.8%) of the study articles included gross images, 17 (29.3%) used dermoscopic images, and 4 (6.9%) used both. Twenty-seven (46.6%) articles used convolutional/convoluted neural networks, 15 (25.9%) used deep learning model/deep neural networks, 8 (13.8%) used other algorithms, 6 (10.3%) used AI/artificial neural network, and 2 (3.4%) used fuzzy algorithms. Most studies were conducted in China (29.3% [17/58]), Germany (12.1% [7/58]), India (10.3% [6/58]), multiple nations (10.3% [6/58]), and the United States (10.3% [6/58]). Overall, 82.8% (48/58) of articles included at least 1 dermatologist coauthor. Sensitivity of the AI models was 0.85, and specificity was 0.85. The average percentage of images in the dataset correctly identified by a physician was 76.87% vs 81.62% of images correctly identified by AI. Average agreement between AI and physician assessment was 77.98%, defined as AI and physician both having the same diagnosis. 

Articles authored by dermatologists contained more clinical images than those without dermatologists in key authorship roles (P<.0001)(eTable). Psoriasis-related algorithms had the fewest (mean [SD]: 3173 [4203]), and pigmented skin lesions had the most clinical images (mean [SD]: 53,19l [155,579]).

RagiCT116005184-eTable

Comment

Our results indicated that AI studies with dermatologist authors had significantly more images in their datasets (ie, the set of clinical images of skin lesions used to train AI algorithms in diagnosing or classifying lesions) than those with nondermatologist authors (P<.0001)(eTable). Similarly, in a study of AI technology for skin cancer diagnosis, AI studies with dermatologist authors (ie, included in the development of the AI algorithm) had more images than studies without dermatologist authors.1 Deep learning textbooks have suggested that 5000 clinical images or training input per output category are needed to produce acceptable algorithm performance, and more than 10 million are needed to produce results superior to human performance.4-10 Despite advances in AI for dermatologic image analysis, the creation of these models often has been directed by nondermatologists1; therefore, dermatologist involvement in AI development is necessary to facilitate collection of larger image datasets and optimal performance for image diagnosis/classification tasks.

We found that 20.7% of articles on deep learning models included descriptions of patient ethnicity or race, and only 10.3% of studies included any information about skin tone in the dataset. Furthermore, American investigators primarily trained models using clinical images of patients with lighter skin tones, whereas Chinese investigators exclusively included images depicting darker skin tones. Similarly, in a study of 52 cutaneous imaging deep learning articles, only 17.3% (9/52) reported race and/or Fitzpatrick skin type, and only 7.7% (4/52) of articles included both.2,6,8 Therefore, dermatologists are needed to contribute images representing diverse populations and collaborate in AI research studies, as their involvement is necessary to ensure the accuracy of AI models in classifying lesions or diagnosing skin lesions across all skin types.

Our search was limited to PubMed, and real-world applications could not be evaluated.

Conclusion

In summary, we found that AI studies with dermatologist authors used larger numbers of clinical images in their datasets and more images representing diverse skin types than studies without. Therefore, we advocate for greater involvement of dermatologists in AI research, which might result in better patient outcomes by improving diagnostic accuracy.

References
  1. Zakhem GA, Fakhoury JW, Motosko CC, et al. Characterizing the role of dermatologists in developing artificial intelligence for assessment of skin cancer. J Am Acad Dermatol. 2021;85:1544-1556.
  2. Daneshjou R, Vodrahalli K, Novoa RA, et al. Disparities in dermatology AI performance on a diverse, curated clinical image set. Sci Adv. 2022;8:eabq6147.
  3. Wu E, Wu K, Daneshjou R, et al. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat Med. 2021;27:582-584.
  4. Murphree DH, Puri P, Shamim H, et al. Deep learning for dermatologists: part I. Fundamental concepts. J Am Acad Dermatol. 2022;87:1343-1351.
  5. Goodfellow I, Bengio Y, Courville A. Deep Learning. The MIT Press; 2016.
  6. Kim YH, Kobic A, Vidal NY. Distribution of race and Fitzpatrick skin types in data sets for deep learning in dermatology: a systematic review. J Am Acad Dermatol. 2022;87:460-461.
  7. Liu Y, Jain A, Eng C, et al. A deep learning system for differential diagnosis of skin diseases. Nat Med. 2020;26:900-908.
  8. Zhu CY, Wang YK, Chen HP, et al. A deep learning based framework for diagnosing multiple skin diseases in a clinical environment. Front Med (Lausanne). 2021;8:626369.
  9. Capurro N, Pastore VP, Touijer L, et al. A deep learning approach to direct immunofluorescence pattern recognition in autoimmune bullous diseases. Br J Dermatol. 2024;191:261-266.
  10. Han SS, Park I, Eun Chang S, et al. Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders. J Invest Dermatol. 2020;140:1753-1761.
References
  1. Zakhem GA, Fakhoury JW, Motosko CC, et al. Characterizing the role of dermatologists in developing artificial intelligence for assessment of skin cancer. J Am Acad Dermatol. 2021;85:1544-1556.
  2. Daneshjou R, Vodrahalli K, Novoa RA, et al. Disparities in dermatology AI performance on a diverse, curated clinical image set. Sci Adv. 2022;8:eabq6147.
  3. Wu E, Wu K, Daneshjou R, et al. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat Med. 2021;27:582-584.
  4. Murphree DH, Puri P, Shamim H, et al. Deep learning for dermatologists: part I. Fundamental concepts. J Am Acad Dermatol. 2022;87:1343-1351.
  5. Goodfellow I, Bengio Y, Courville A. Deep Learning. The MIT Press; 2016.
  6. Kim YH, Kobic A, Vidal NY. Distribution of race and Fitzpatrick skin types in data sets for deep learning in dermatology: a systematic review. J Am Acad Dermatol. 2022;87:460-461.
  7. Liu Y, Jain A, Eng C, et al. A deep learning system for differential diagnosis of skin diseases. Nat Med. 2020;26:900-908.
  8. Zhu CY, Wang YK, Chen HP, et al. A deep learning based framework for diagnosing multiple skin diseases in a clinical environment. Front Med (Lausanne). 2021;8:626369.
  9. Capurro N, Pastore VP, Touijer L, et al. A deep learning approach to direct immunofluorescence pattern recognition in autoimmune bullous diseases. Br J Dermatol. 2024;191:261-266.
  10. Han SS, Park I, Eun Chang S, et al. Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders. J Invest Dermatol. 2020;140:1753-1761.
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The Role of Dermatologists in Developing AI Tools for Diagnosis and Classification of Skin Disease

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  • Artificial intelligence (AI) technology is emerging as a valuable tool in diagnosing and classifying dermatologic conditions.
  • Despite advances in AI for dermatologic image analysis, the creation of these models often has been directed by nondermatologists.
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The Current State of Postgraduate Dermatology Training Programs for Advanced Practice Providers

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The Current State of Postgraduate Dermatology Training Programs for Advanced Practice Providers

Nurse practitioners (NPs) and physician assistants (PAs) often help provide dermatologic care but lack the same mandatory specialized postgraduate training required of board-certified dermatologists (BCDs), which includes at least 3 years of dermatology-focused education in an accredited residency program in addition to an intern year of general medicine, pediatrics, or surgery. Dermatology residency is followed by a certification examination administered by the American Board of Dermatology (ABD) or the American Osteopathic Board of Dermatology, leading to board certification. Some physicians choose to do a fellowship, which typically involves an additional 1 to 2 years of postresidency subspeciality training.

Optional postgraduate dermatology training programs for advanced practice providers (APPs) have been offered by some academic institutions and private practice groups since at least 2003, including Lahey Hospital and Medical Center (Burlington, Massachusetts) as well as the University of Rochester Medical Center (Rochester, New York). Despite a lack of accreditation or standardization, the programs can be beneficial for NPs and PAs to expand their dermatologic knowledge and skills and help bridge the care gap within the specialty. Didactics often are conducted in parallel with the educational activities of the parent institution’s traditional dermatology residency program (eg, lectures, grand rounds). While these programs often are managed by practicing dermatology NPs and PAs, dermatologists also may be involved in their education with didactic instruction, curriculum development, and clinical preceptorship. 

In this cross-sectional study, we identified and evaluated 10 postgraduate dermatology training programs for APPs across the United States. With the growing number of NPs and PAs in the dermatology workforce—both in academic and private practice—it is important for BCDs to be aware of the differences in the dermatology training received in order to ensure safe and effective care is provided through supervisory or collaborative roles (depending on state independent practice laws for APPs and to be aware of the implications these programs may have on the field of dermatology.

Methods

To identify postgraduate dermatology training programs for APPs in the United States, we conducted a cross-sectional study using data obtained via a Google search of various combinations of the following terms: nurse practitioner, NP, physician assistant, PA, advance practice provider, APP, dermatology, postgraduate training, residency, and fellowship. We excluded postgraduate dermatology training programs for APPs that required tuition and did not provide a stipend, as well as programs that lacked the formal structure and credibility needed to qualify as legitimate postgraduate training. Many of the excluded programs operate in a manner that raises ethical concerns, offering pay-to-play opportunities under the guise of education. Information collected on each program included the program name, location, parent institution, program length, class size, curriculum, and any associated salary and benefits.

Results

Ten academic and private practice organizations across the United States that offer postgraduate dermatologic training programs for APPs were identified (eTable). Four (40%) programs were advertised as fellowships. Six (60%) of the programs were offered at academic medical centers, and 4 (40%) were offered by private practices. Most programs were located east of the Mississippi River, and many institutions offered instruction at 1 or more locations within the same state (eFigure). The Advanced Dermatology and Cosmetic Surgery private practice group offered training opportunities in multiple states.

MehrmalCT116005180-eTable_part1MehrmalCT116005180-eTable_part2
Mehrmal-efig
eFIGURE. Geographic distribution of postgraduate dermatology training programs for midlevel providers. Red dots indicate Advanced Dermatology and Cosmetic Surgery locations.

Six programs required APPs to become board-certified NPs or PAs prior to enrolling. Most programs enrolled both NPs and PAs, while some only enrolled NPs (eTable). Only 1 (10%) program required NPs to be board certified as a family NP, while another (10%) recommended that applicants have experience in urgent care, emergency medicine, or trauma medicine. Lahey Hospital & Medical Center required experience as an NP in a general setting for 1 to 2 years prior to applying. No program required prior experience in the field of dermatology.

Program length varied from 6 to 24 months, and cohort size typically was limited to 1 to 2 providers (eTable). Although the exact numbers could not be ascertained, most curricula focused on medical dermatology, including clinical and didactic components, but many offered electives such as cosmetic and procedural dermatology. Two institutions (20%) required independent research. Work typically was limited to 40 hours per week, and most paid a full-time employee salary and provided benefits such as health insurance, retirement, and paid leave (eTable). Kansas Medical Clinic (Topeka, Kansas) required at least 3 years of employment in an underserved community following program completion. The Oasis Dermatology private practice group in Texas required a 1-year teaching commitment after program completion. The Advanced Dermatology and Cosmetic Surgery group offered a full-time position upon program completion.

Comment

There is a large difference in the total number of training and credentialing hours when comparing graduate school training and postgraduate credentialing of medical and osteopathic physicians compared with APPs. A new graduate physician has at least twice as many clinical hours as a PA and 10 times as many clinical hours as an NP prior to starting residency. Physicians also typically complete at least 6 times the number of hours of certification examinations compared to NPs and PAs.1

Nurse practitioner students typically complete the 500 hours of prelicensure clinical training required for NP school in 2 to 4 years.2,3 The amount of time required for completion is dependent on the degree and experience of the student upon program entry (eg, bachelor of science in nursing vs master of science in nursing as a terminal degree). Physician assistant students are required to complete 2000 prelicensure clinical hours, and most PA programs are 3 years in duration.4 Many NP and PA programs require some degree of clinical experience prior to beginning graduate education.5

When comparing prelicensure examinations, questions assessing dermatologic knowledge comprise approximately 6% to 10% of the total questions on the United States Medical Licensing Examination Steps 1 and 2.6 The Comprehensive Osteopathic Medical Licensing Examination of the United States Level 1 and Level 2-Cognitive Evaluation both have at least 5% of questions dedicated to dermatology.7 Approximately 5% of the questions on the Physician Assistant National Certifying Examination are dedicated to dermatology.8 The dermatology content on either of the NP certification examinations is unclear.2,3 In the states of California, Indiana, and New York, national certification through the American Association of Nurse Practitioners or American Nurses Credentialing Center is not required for NPs to practice in their respective states.9

Regarding dermatologic board certification, a new graduate NP may obtain certification from the Dermatology Nurse Practitioner Certification Board with 3000 hours of general dermatology practice that may occur during normal working hours.10 These hours do not have to occur in one of the previously identified postgraduate APP training programs. The National Board of Dermatology Physician Assistants was founded in 2018 and has since dissolved. The National Board of Dermatology Physician Assistants was not accredited and required at least 3 years of training in dermatology with the same dermatologist in addition to completing a 125-question multiple-choice examination.11 Of note, this examination was opposed by both the ABD and the Society for Dermatology Physician Associates.12 A PA also may become a Diplomate Fellow with the Society of Dermatology Physician Associates after completion of 64.5 hours of online continuing education modules.4 Some PAs may choose to obtain a Certificate of Added Qualifications, which is a voluntary credential that helps document specialty experience and expertise in dermatology or other specialties.

In contrast, a dermatology resident physician requires nearly 11,000 to 13,000 hours of clinical training hours, which last 3 to 4 years following medical school.13 This training involves direct patient care under supervision in various settings, including hospitals, outpatient clinics, and surgical procedures. In addition to this clinical experience, dermatology residents must pass a 3-step certification examination process administered by the ABD.13 This process includes approximately 20 hours of examinations designed to assess both knowledge and practical skills. For those who wish to further specialize, additional fellowship training in areas such as pediatric dermatology, dermatopathology, or Mohs surgery may follow residency; such fellowships involve an extra 2500 to 3500 hours of training and culminate in another certification examination, further refining a resident’s expertise in a specific dermatologic field. Osteopathic physicians may opt out of the ABD 3-step pathway and obtain board certification through the American Osteopathic Board of Dermatology.14

Many of the programs we evaluated integrate APP trainees into resident education, allowing participation in equivalent didactic curricula, clinical rotations, and departmental academic activities. The salary and benefits associated with these programs are somewhat like those of resident physicians.15,16 While most tuition-based programs were excluded from our study due to their lack of credibility and alignment with our study criteria, we identified 2 specific programs that stood out as credible despite requiring students to pay tuition. These programs demonstrated a structured and rigorous curriculum with a clear focus on comprehensive dermatologic training, meeting our standards for inclusion. These programs offer dermatologic training for graduates of NP and PA programs at a cost to the student.15,16 The program at the Florida Atlantic University, Boca Raton, is largely online,15 and the program at the University of Miami, Florida, offers no direct clinical contact.16 These programs illustrate the variety of postgraduate dermatology curricula available nationally in comparison to resident salaries; however, they were not included in our formal analysis because they do not provide structured, in-person clinical training consistent with our inclusion criteria. Neither of these programs would enable participants to qualify for credentialing with the Dermatology Nurse Practitioner Certification Board after completion. While this study identified postgraduate training programs for APPs in dermatology advertised online, it is possible some were omitted or not advertised online.

While many of the postgraduate programs we evaluated provide unique educational opportunities for APPs, it is unknown if graduating providers are equipped to handle the care of patients with complex dermatologic needs. Regardless, the increased utilization of APPs by BCDs has been well documented over the past 2 decades.17-20 It has been suggested that a higher ratio of APPs to dermatologists can decrease the time it takes for a patient to be seen in a clinic.21-23 However, investigators have expressed concerns that APPs lack standardized surgical training and clinical hour requirements in the field of dermatology.24 Despite these concerns, Medicare claims data show that APPs are performing advanced surgical and cosmetic procedures at increasing rates.17,18 Other authors have questioned the cost-effectiveness of APPs, as multiple studies have shown that the number of biopsies needed to diagnose 1 case of skin cancer is higher for midlevel providers than for dermatologists.25-27

Conclusion

With the anticipated expansion of private equity in dermatology and the growth of our Medicare-eligible population, we are likely to see increased utilization of APPs to address the shortage of BCDs.28,29 Understanding the prelicensure and postlicensure clinical training requirements, examination hours, and extent of dermatology-focused education among APPs and BCDs can help dermatologists collaborate more effectively and ensure safe, high-quality patient care. Standardizing, improving, and providing high-quality education and promoting lifelong learning in the field of dermatology should be celebrated, and dermatologists are the skin experts best equipped to lead dermatologic education forward.

References
  1. Robeznieks A. Training gaps between physicians, nonphysicians are significant. American Medical Association. February 17, 2025. Accessed October 23, 2025. https://www.ama-assn.org/practice-management/scope-practice/training-gaps-between-physicians-nonphysicians-are-significant
  2. American Nurses Credentialing Center. Test content outline. Accessed October 6, 2025. https://www.nursingworld.org/globalassets/08282024-exam-24-npd-tco-website.pdf
  3. American Academy of Nurse Practitioners National Certification Board. AANPCB Family Nurse Practitioner Adult-Gerontology Primary Care Nurse Practitioner Psychiatric Mental Health Pratitioner: FNP, AGNP & PMHNP Certification Certification Handbook. American Academy of Nurse Practitioners Certification Board; 2023. Accessed October 6, 2025. https://www.aanpcert.org/resource/documents/AGNP%20FNP%20Candidate%20Handbook.pdf
  4. Society of Dermatology Physician Associates. SDPA Diplomate Fellowship. Accessed October 6, 2025. https://learning.dermpa.orgdiplomate-fellowship
  5. American Academy of Physician Associates. Become a PA. Accessed October 6, 2025. https://www.aapa.org/career-central/become-a-pa/
  6. United States Medical Licensing Examination. Prepare for your exam. Accessed October 6, 2025. https://www.usmle.org/prepare-your-exam
  7. National Board of Osteopathic Medical Examiners. Patient presentations related to the integumentary system. Accessed October 6, 2025. https://www.nbome.org/assessments/comlex-usa/comlex-usa-blueprint/d2-clinical-presentations/integumentary-system
  8. National Commission on Certification of Physician Assistants. PANCE content blueprint. Accessed October 6, 2025. https://prodcmsstoragesa.blob.core.windows.net/uploads/files/PANCEBlueprint.pdf
  9. American Association of Nurse Practitioners. Practice information by state. Accessed October 6, 2025. https://www.aanp.org/practice/practice-information-by-state
  10. Dermatology Nurse Practitioner Certification Board. Eligibility. Accessed October 6, 2025. https://www.dnpcb.org/eligibility.php
  11. National Board of Dermatology Physician Assistants. Certification. Accessed September 3, 2022.
  12. Society of Dermatology Physician Associates. SDPA statement regarding the ABDPA Board Certification Exam for derm PAs. October 8, 2019. Accessed October 6, 2025. https://www.dermpa.org/news/articles/2019-10/sdpa-statement-regarding-abdpa-board-certification-exam-derm-pas
  13. American Board of Dermatology. Residents and fellows. Accessed October 6, 2025. https://www.abderm.org/residents-and-fellows
  14. American Osteopathic Board of Dermatology. Primary certificaiton exam. Accessed October 6, 2025. https://certification.osteopathic.org/dermatology/certification-process/dermatology/written-exams/
  15. Florida Atlantic University. Christine E. Lynn College of Nursing. Dermatology nurse practitioner certificate program. Accessed October 6, 2025. https://www.fau.edu/nursing/academics/certificates/dermatology-program/
  16. Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery. Advanced Practitioner Program.
  17. Coldiron B, Ratnarathorn M. Scope of physician procedures independently billed by mid-level providers in the office setting. JAMA Dermatol. 2014;150:1153-1159.
  18. Zhang M, Zippin J, Kaffenberger B. Trends and scope of dermatology procedures billed by advanced practice professionals from 2012 through 2015. JAMA Dermatol. 2018;154:1040-1044.
  19. Resneck J Jr, Kimball AB. The dermatology workforce shortage. J Am Acad Dermatol. 2004;50:50-54.
  20. Kimball AB, Resneck JS Jr. The US dermatology workforce: a specialty remains in shortage. J Am Acad Dermatol. 2008;59:741-745.
  21. Creadore A, Desai S, Li SJ, et al. Insurance acceptance, appointment wait time, and dermatologist access across practice types in the US. JAMA Dermatol. 2021;157:181-188.
  22. Braun RT, Bond AM, Qian Y, et al. Private equity in dermatology: effect on price, utilization, and spending. Health Aff (Millwood). 2021;40:727-735.
  23. Skaljic M, Lipoff JB. Association of private equity ownership with increased employment of advanced practice professionals in outpatient dermatology offices. J Am Acad Dermatol. 2021;84:1178-1180.
  24. Jalian HR, Avram MM. Mid-level practitioners in dermatology: a need for further study and oversight. JAMA Dermatol. 2014;150:1149-1151.
  25. Sarzynski E, Barry H. Current evidence and controversies: advanced practice providers in healthcare. Am J Manag Care. 2019;25:366-368. 
  26. Nault A, Zhang C, Kim K, et al. Biopsy use in skin cancer diagnosis: comparing dermatology physicians and advanced practice professionals. JAMA Dermatol. 2015;151:899-902.
  27. Anderson AM, Matsumoto M, Saul MI, et al. Accuracy of skin cancer diagnosis by physician assistants compared with dermatologists in a large health care system. JAMA Dermatol. 2018;154:569-573.
  28. Sung C, Salem S, Oulee A, et al. A systematic review: landscape of private equity in dermatology from past to present. J Drugs Dermatol. 2023 Apr 1;22:404-409. doi: 10.36849/JDD.6892.
  29. CMS releases National Healthcare Expenditure and enrollment projections through 2031. Health Management Associates. July 13, 2023. Accessed October 23, 2025. https://www.healthmanagement.com/blog/cms-releases-national-healthcare-expenditure-and-enrollment-projections-through-2031/
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Author and Disclosure Information

Dr. Mehrmal is from Epiphany Dermatology, Saint Louis, Missouri. Dr. Mazumder is from the Department of Dermatology, Saint Francis Hospital, Chicago, Illinois. Dr. Poole is from the Division of Dermatology, WashU Medicine, Saint Louis, Missouri. Dr. Heinecke is from the Department of Dermatology, Saint Louis University School of Medicine, Missouri. Nehaa Sohail is from the Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso.

The authors have no relevant financial disclosures to report.

Correspondence: Sino Mehrmal, DO, 8888 Ladue Rd, Ste 120, St. Louis, MO 63124 ([email protected]).

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Dr. Mehrmal is from Epiphany Dermatology, Saint Louis, Missouri. Dr. Mazumder is from the Department of Dermatology, Saint Francis Hospital, Chicago, Illinois. Dr. Poole is from the Division of Dermatology, WashU Medicine, Saint Louis, Missouri. Dr. Heinecke is from the Department of Dermatology, Saint Louis University School of Medicine, Missouri. Nehaa Sohail is from the Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso.

The authors have no relevant financial disclosures to report.

Correspondence: Sino Mehrmal, DO, 8888 Ladue Rd, Ste 120, St. Louis, MO 63124 ([email protected]).

Cutis. 2025 November;116(5):180-183, E6-E8. doi:10.12788/cutis.1298

Author and Disclosure Information

Dr. Mehrmal is from Epiphany Dermatology, Saint Louis, Missouri. Dr. Mazumder is from the Department of Dermatology, Saint Francis Hospital, Chicago, Illinois. Dr. Poole is from the Division of Dermatology, WashU Medicine, Saint Louis, Missouri. Dr. Heinecke is from the Department of Dermatology, Saint Louis University School of Medicine, Missouri. Nehaa Sohail is from the Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso.

The authors have no relevant financial disclosures to report.

Correspondence: Sino Mehrmal, DO, 8888 Ladue Rd, Ste 120, St. Louis, MO 63124 ([email protected]).

Cutis. 2025 November;116(5):180-183, E6-E8. doi:10.12788/cutis.1298

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Nurse practitioners (NPs) and physician assistants (PAs) often help provide dermatologic care but lack the same mandatory specialized postgraduate training required of board-certified dermatologists (BCDs), which includes at least 3 years of dermatology-focused education in an accredited residency program in addition to an intern year of general medicine, pediatrics, or surgery. Dermatology residency is followed by a certification examination administered by the American Board of Dermatology (ABD) or the American Osteopathic Board of Dermatology, leading to board certification. Some physicians choose to do a fellowship, which typically involves an additional 1 to 2 years of postresidency subspeciality training.

Optional postgraduate dermatology training programs for advanced practice providers (APPs) have been offered by some academic institutions and private practice groups since at least 2003, including Lahey Hospital and Medical Center (Burlington, Massachusetts) as well as the University of Rochester Medical Center (Rochester, New York). Despite a lack of accreditation or standardization, the programs can be beneficial for NPs and PAs to expand their dermatologic knowledge and skills and help bridge the care gap within the specialty. Didactics often are conducted in parallel with the educational activities of the parent institution’s traditional dermatology residency program (eg, lectures, grand rounds). While these programs often are managed by practicing dermatology NPs and PAs, dermatologists also may be involved in their education with didactic instruction, curriculum development, and clinical preceptorship. 

In this cross-sectional study, we identified and evaluated 10 postgraduate dermatology training programs for APPs across the United States. With the growing number of NPs and PAs in the dermatology workforce—both in academic and private practice—it is important for BCDs to be aware of the differences in the dermatology training received in order to ensure safe and effective care is provided through supervisory or collaborative roles (depending on state independent practice laws for APPs and to be aware of the implications these programs may have on the field of dermatology.

Methods

To identify postgraduate dermatology training programs for APPs in the United States, we conducted a cross-sectional study using data obtained via a Google search of various combinations of the following terms: nurse practitioner, NP, physician assistant, PA, advance practice provider, APP, dermatology, postgraduate training, residency, and fellowship. We excluded postgraduate dermatology training programs for APPs that required tuition and did not provide a stipend, as well as programs that lacked the formal structure and credibility needed to qualify as legitimate postgraduate training. Many of the excluded programs operate in a manner that raises ethical concerns, offering pay-to-play opportunities under the guise of education. Information collected on each program included the program name, location, parent institution, program length, class size, curriculum, and any associated salary and benefits.

Results

Ten academic and private practice organizations across the United States that offer postgraduate dermatologic training programs for APPs were identified (eTable). Four (40%) programs were advertised as fellowships. Six (60%) of the programs were offered at academic medical centers, and 4 (40%) were offered by private practices. Most programs were located east of the Mississippi River, and many institutions offered instruction at 1 or more locations within the same state (eFigure). The Advanced Dermatology and Cosmetic Surgery private practice group offered training opportunities in multiple states.

MehrmalCT116005180-eTable_part1MehrmalCT116005180-eTable_part2
Mehrmal-efig
eFIGURE. Geographic distribution of postgraduate dermatology training programs for midlevel providers. Red dots indicate Advanced Dermatology and Cosmetic Surgery locations.

Six programs required APPs to become board-certified NPs or PAs prior to enrolling. Most programs enrolled both NPs and PAs, while some only enrolled NPs (eTable). Only 1 (10%) program required NPs to be board certified as a family NP, while another (10%) recommended that applicants have experience in urgent care, emergency medicine, or trauma medicine. Lahey Hospital & Medical Center required experience as an NP in a general setting for 1 to 2 years prior to applying. No program required prior experience in the field of dermatology.

Program length varied from 6 to 24 months, and cohort size typically was limited to 1 to 2 providers (eTable). Although the exact numbers could not be ascertained, most curricula focused on medical dermatology, including clinical and didactic components, but many offered electives such as cosmetic and procedural dermatology. Two institutions (20%) required independent research. Work typically was limited to 40 hours per week, and most paid a full-time employee salary and provided benefits such as health insurance, retirement, and paid leave (eTable). Kansas Medical Clinic (Topeka, Kansas) required at least 3 years of employment in an underserved community following program completion. The Oasis Dermatology private practice group in Texas required a 1-year teaching commitment after program completion. The Advanced Dermatology and Cosmetic Surgery group offered a full-time position upon program completion.

Comment

There is a large difference in the total number of training and credentialing hours when comparing graduate school training and postgraduate credentialing of medical and osteopathic physicians compared with APPs. A new graduate physician has at least twice as many clinical hours as a PA and 10 times as many clinical hours as an NP prior to starting residency. Physicians also typically complete at least 6 times the number of hours of certification examinations compared to NPs and PAs.1

Nurse practitioner students typically complete the 500 hours of prelicensure clinical training required for NP school in 2 to 4 years.2,3 The amount of time required for completion is dependent on the degree and experience of the student upon program entry (eg, bachelor of science in nursing vs master of science in nursing as a terminal degree). Physician assistant students are required to complete 2000 prelicensure clinical hours, and most PA programs are 3 years in duration.4 Many NP and PA programs require some degree of clinical experience prior to beginning graduate education.5

When comparing prelicensure examinations, questions assessing dermatologic knowledge comprise approximately 6% to 10% of the total questions on the United States Medical Licensing Examination Steps 1 and 2.6 The Comprehensive Osteopathic Medical Licensing Examination of the United States Level 1 and Level 2-Cognitive Evaluation both have at least 5% of questions dedicated to dermatology.7 Approximately 5% of the questions on the Physician Assistant National Certifying Examination are dedicated to dermatology.8 The dermatology content on either of the NP certification examinations is unclear.2,3 In the states of California, Indiana, and New York, national certification through the American Association of Nurse Practitioners or American Nurses Credentialing Center is not required for NPs to practice in their respective states.9

Regarding dermatologic board certification, a new graduate NP may obtain certification from the Dermatology Nurse Practitioner Certification Board with 3000 hours of general dermatology practice that may occur during normal working hours.10 These hours do not have to occur in one of the previously identified postgraduate APP training programs. The National Board of Dermatology Physician Assistants was founded in 2018 and has since dissolved. The National Board of Dermatology Physician Assistants was not accredited and required at least 3 years of training in dermatology with the same dermatologist in addition to completing a 125-question multiple-choice examination.11 Of note, this examination was opposed by both the ABD and the Society for Dermatology Physician Associates.12 A PA also may become a Diplomate Fellow with the Society of Dermatology Physician Associates after completion of 64.5 hours of online continuing education modules.4 Some PAs may choose to obtain a Certificate of Added Qualifications, which is a voluntary credential that helps document specialty experience and expertise in dermatology or other specialties.

In contrast, a dermatology resident physician requires nearly 11,000 to 13,000 hours of clinical training hours, which last 3 to 4 years following medical school.13 This training involves direct patient care under supervision in various settings, including hospitals, outpatient clinics, and surgical procedures. In addition to this clinical experience, dermatology residents must pass a 3-step certification examination process administered by the ABD.13 This process includes approximately 20 hours of examinations designed to assess both knowledge and practical skills. For those who wish to further specialize, additional fellowship training in areas such as pediatric dermatology, dermatopathology, or Mohs surgery may follow residency; such fellowships involve an extra 2500 to 3500 hours of training and culminate in another certification examination, further refining a resident’s expertise in a specific dermatologic field. Osteopathic physicians may opt out of the ABD 3-step pathway and obtain board certification through the American Osteopathic Board of Dermatology.14

Many of the programs we evaluated integrate APP trainees into resident education, allowing participation in equivalent didactic curricula, clinical rotations, and departmental academic activities. The salary and benefits associated with these programs are somewhat like those of resident physicians.15,16 While most tuition-based programs were excluded from our study due to their lack of credibility and alignment with our study criteria, we identified 2 specific programs that stood out as credible despite requiring students to pay tuition. These programs demonstrated a structured and rigorous curriculum with a clear focus on comprehensive dermatologic training, meeting our standards for inclusion. These programs offer dermatologic training for graduates of NP and PA programs at a cost to the student.15,16 The program at the Florida Atlantic University, Boca Raton, is largely online,15 and the program at the University of Miami, Florida, offers no direct clinical contact.16 These programs illustrate the variety of postgraduate dermatology curricula available nationally in comparison to resident salaries; however, they were not included in our formal analysis because they do not provide structured, in-person clinical training consistent with our inclusion criteria. Neither of these programs would enable participants to qualify for credentialing with the Dermatology Nurse Practitioner Certification Board after completion. While this study identified postgraduate training programs for APPs in dermatology advertised online, it is possible some were omitted or not advertised online.

While many of the postgraduate programs we evaluated provide unique educational opportunities for APPs, it is unknown if graduating providers are equipped to handle the care of patients with complex dermatologic needs. Regardless, the increased utilization of APPs by BCDs has been well documented over the past 2 decades.17-20 It has been suggested that a higher ratio of APPs to dermatologists can decrease the time it takes for a patient to be seen in a clinic.21-23 However, investigators have expressed concerns that APPs lack standardized surgical training and clinical hour requirements in the field of dermatology.24 Despite these concerns, Medicare claims data show that APPs are performing advanced surgical and cosmetic procedures at increasing rates.17,18 Other authors have questioned the cost-effectiveness of APPs, as multiple studies have shown that the number of biopsies needed to diagnose 1 case of skin cancer is higher for midlevel providers than for dermatologists.25-27

Conclusion

With the anticipated expansion of private equity in dermatology and the growth of our Medicare-eligible population, we are likely to see increased utilization of APPs to address the shortage of BCDs.28,29 Understanding the prelicensure and postlicensure clinical training requirements, examination hours, and extent of dermatology-focused education among APPs and BCDs can help dermatologists collaborate more effectively and ensure safe, high-quality patient care. Standardizing, improving, and providing high-quality education and promoting lifelong learning in the field of dermatology should be celebrated, and dermatologists are the skin experts best equipped to lead dermatologic education forward.

Nurse practitioners (NPs) and physician assistants (PAs) often help provide dermatologic care but lack the same mandatory specialized postgraduate training required of board-certified dermatologists (BCDs), which includes at least 3 years of dermatology-focused education in an accredited residency program in addition to an intern year of general medicine, pediatrics, or surgery. Dermatology residency is followed by a certification examination administered by the American Board of Dermatology (ABD) or the American Osteopathic Board of Dermatology, leading to board certification. Some physicians choose to do a fellowship, which typically involves an additional 1 to 2 years of postresidency subspeciality training.

Optional postgraduate dermatology training programs for advanced practice providers (APPs) have been offered by some academic institutions and private practice groups since at least 2003, including Lahey Hospital and Medical Center (Burlington, Massachusetts) as well as the University of Rochester Medical Center (Rochester, New York). Despite a lack of accreditation or standardization, the programs can be beneficial for NPs and PAs to expand their dermatologic knowledge and skills and help bridge the care gap within the specialty. Didactics often are conducted in parallel with the educational activities of the parent institution’s traditional dermatology residency program (eg, lectures, grand rounds). While these programs often are managed by practicing dermatology NPs and PAs, dermatologists also may be involved in their education with didactic instruction, curriculum development, and clinical preceptorship. 

In this cross-sectional study, we identified and evaluated 10 postgraduate dermatology training programs for APPs across the United States. With the growing number of NPs and PAs in the dermatology workforce—both in academic and private practice—it is important for BCDs to be aware of the differences in the dermatology training received in order to ensure safe and effective care is provided through supervisory or collaborative roles (depending on state independent practice laws for APPs and to be aware of the implications these programs may have on the field of dermatology.

Methods

To identify postgraduate dermatology training programs for APPs in the United States, we conducted a cross-sectional study using data obtained via a Google search of various combinations of the following terms: nurse practitioner, NP, physician assistant, PA, advance practice provider, APP, dermatology, postgraduate training, residency, and fellowship. We excluded postgraduate dermatology training programs for APPs that required tuition and did not provide a stipend, as well as programs that lacked the formal structure and credibility needed to qualify as legitimate postgraduate training. Many of the excluded programs operate in a manner that raises ethical concerns, offering pay-to-play opportunities under the guise of education. Information collected on each program included the program name, location, parent institution, program length, class size, curriculum, and any associated salary and benefits.

Results

Ten academic and private practice organizations across the United States that offer postgraduate dermatologic training programs for APPs were identified (eTable). Four (40%) programs were advertised as fellowships. Six (60%) of the programs were offered at academic medical centers, and 4 (40%) were offered by private practices. Most programs were located east of the Mississippi River, and many institutions offered instruction at 1 or more locations within the same state (eFigure). The Advanced Dermatology and Cosmetic Surgery private practice group offered training opportunities in multiple states.

MehrmalCT116005180-eTable_part1MehrmalCT116005180-eTable_part2
Mehrmal-efig
eFIGURE. Geographic distribution of postgraduate dermatology training programs for midlevel providers. Red dots indicate Advanced Dermatology and Cosmetic Surgery locations.

Six programs required APPs to become board-certified NPs or PAs prior to enrolling. Most programs enrolled both NPs and PAs, while some only enrolled NPs (eTable). Only 1 (10%) program required NPs to be board certified as a family NP, while another (10%) recommended that applicants have experience in urgent care, emergency medicine, or trauma medicine. Lahey Hospital & Medical Center required experience as an NP in a general setting for 1 to 2 years prior to applying. No program required prior experience in the field of dermatology.

Program length varied from 6 to 24 months, and cohort size typically was limited to 1 to 2 providers (eTable). Although the exact numbers could not be ascertained, most curricula focused on medical dermatology, including clinical and didactic components, but many offered electives such as cosmetic and procedural dermatology. Two institutions (20%) required independent research. Work typically was limited to 40 hours per week, and most paid a full-time employee salary and provided benefits such as health insurance, retirement, and paid leave (eTable). Kansas Medical Clinic (Topeka, Kansas) required at least 3 years of employment in an underserved community following program completion. The Oasis Dermatology private practice group in Texas required a 1-year teaching commitment after program completion. The Advanced Dermatology and Cosmetic Surgery group offered a full-time position upon program completion.

Comment

There is a large difference in the total number of training and credentialing hours when comparing graduate school training and postgraduate credentialing of medical and osteopathic physicians compared with APPs. A new graduate physician has at least twice as many clinical hours as a PA and 10 times as many clinical hours as an NP prior to starting residency. Physicians also typically complete at least 6 times the number of hours of certification examinations compared to NPs and PAs.1

Nurse practitioner students typically complete the 500 hours of prelicensure clinical training required for NP school in 2 to 4 years.2,3 The amount of time required for completion is dependent on the degree and experience of the student upon program entry (eg, bachelor of science in nursing vs master of science in nursing as a terminal degree). Physician assistant students are required to complete 2000 prelicensure clinical hours, and most PA programs are 3 years in duration.4 Many NP and PA programs require some degree of clinical experience prior to beginning graduate education.5

When comparing prelicensure examinations, questions assessing dermatologic knowledge comprise approximately 6% to 10% of the total questions on the United States Medical Licensing Examination Steps 1 and 2.6 The Comprehensive Osteopathic Medical Licensing Examination of the United States Level 1 and Level 2-Cognitive Evaluation both have at least 5% of questions dedicated to dermatology.7 Approximately 5% of the questions on the Physician Assistant National Certifying Examination are dedicated to dermatology.8 The dermatology content on either of the NP certification examinations is unclear.2,3 In the states of California, Indiana, and New York, national certification through the American Association of Nurse Practitioners or American Nurses Credentialing Center is not required for NPs to practice in their respective states.9

Regarding dermatologic board certification, a new graduate NP may obtain certification from the Dermatology Nurse Practitioner Certification Board with 3000 hours of general dermatology practice that may occur during normal working hours.10 These hours do not have to occur in one of the previously identified postgraduate APP training programs. The National Board of Dermatology Physician Assistants was founded in 2018 and has since dissolved. The National Board of Dermatology Physician Assistants was not accredited and required at least 3 years of training in dermatology with the same dermatologist in addition to completing a 125-question multiple-choice examination.11 Of note, this examination was opposed by both the ABD and the Society for Dermatology Physician Associates.12 A PA also may become a Diplomate Fellow with the Society of Dermatology Physician Associates after completion of 64.5 hours of online continuing education modules.4 Some PAs may choose to obtain a Certificate of Added Qualifications, which is a voluntary credential that helps document specialty experience and expertise in dermatology or other specialties.

In contrast, a dermatology resident physician requires nearly 11,000 to 13,000 hours of clinical training hours, which last 3 to 4 years following medical school.13 This training involves direct patient care under supervision in various settings, including hospitals, outpatient clinics, and surgical procedures. In addition to this clinical experience, dermatology residents must pass a 3-step certification examination process administered by the ABD.13 This process includes approximately 20 hours of examinations designed to assess both knowledge and practical skills. For those who wish to further specialize, additional fellowship training in areas such as pediatric dermatology, dermatopathology, or Mohs surgery may follow residency; such fellowships involve an extra 2500 to 3500 hours of training and culminate in another certification examination, further refining a resident’s expertise in a specific dermatologic field. Osteopathic physicians may opt out of the ABD 3-step pathway and obtain board certification through the American Osteopathic Board of Dermatology.14

Many of the programs we evaluated integrate APP trainees into resident education, allowing participation in equivalent didactic curricula, clinical rotations, and departmental academic activities. The salary and benefits associated with these programs are somewhat like those of resident physicians.15,16 While most tuition-based programs were excluded from our study due to their lack of credibility and alignment with our study criteria, we identified 2 specific programs that stood out as credible despite requiring students to pay tuition. These programs demonstrated a structured and rigorous curriculum with a clear focus on comprehensive dermatologic training, meeting our standards for inclusion. These programs offer dermatologic training for graduates of NP and PA programs at a cost to the student.15,16 The program at the Florida Atlantic University, Boca Raton, is largely online,15 and the program at the University of Miami, Florida, offers no direct clinical contact.16 These programs illustrate the variety of postgraduate dermatology curricula available nationally in comparison to resident salaries; however, they were not included in our formal analysis because they do not provide structured, in-person clinical training consistent with our inclusion criteria. Neither of these programs would enable participants to qualify for credentialing with the Dermatology Nurse Practitioner Certification Board after completion. While this study identified postgraduate training programs for APPs in dermatology advertised online, it is possible some were omitted or not advertised online.

While many of the postgraduate programs we evaluated provide unique educational opportunities for APPs, it is unknown if graduating providers are equipped to handle the care of patients with complex dermatologic needs. Regardless, the increased utilization of APPs by BCDs has been well documented over the past 2 decades.17-20 It has been suggested that a higher ratio of APPs to dermatologists can decrease the time it takes for a patient to be seen in a clinic.21-23 However, investigators have expressed concerns that APPs lack standardized surgical training and clinical hour requirements in the field of dermatology.24 Despite these concerns, Medicare claims data show that APPs are performing advanced surgical and cosmetic procedures at increasing rates.17,18 Other authors have questioned the cost-effectiveness of APPs, as multiple studies have shown that the number of biopsies needed to diagnose 1 case of skin cancer is higher for midlevel providers than for dermatologists.25-27

Conclusion

With the anticipated expansion of private equity in dermatology and the growth of our Medicare-eligible population, we are likely to see increased utilization of APPs to address the shortage of BCDs.28,29 Understanding the prelicensure and postlicensure clinical training requirements, examination hours, and extent of dermatology-focused education among APPs and BCDs can help dermatologists collaborate more effectively and ensure safe, high-quality patient care. Standardizing, improving, and providing high-quality education and promoting lifelong learning in the field of dermatology should be celebrated, and dermatologists are the skin experts best equipped to lead dermatologic education forward.

References
  1. Robeznieks A. Training gaps between physicians, nonphysicians are significant. American Medical Association. February 17, 2025. Accessed October 23, 2025. https://www.ama-assn.org/practice-management/scope-practice/training-gaps-between-physicians-nonphysicians-are-significant
  2. American Nurses Credentialing Center. Test content outline. Accessed October 6, 2025. https://www.nursingworld.org/globalassets/08282024-exam-24-npd-tco-website.pdf
  3. American Academy of Nurse Practitioners National Certification Board. AANPCB Family Nurse Practitioner Adult-Gerontology Primary Care Nurse Practitioner Psychiatric Mental Health Pratitioner: FNP, AGNP & PMHNP Certification Certification Handbook. American Academy of Nurse Practitioners Certification Board; 2023. Accessed October 6, 2025. https://www.aanpcert.org/resource/documents/AGNP%20FNP%20Candidate%20Handbook.pdf
  4. Society of Dermatology Physician Associates. SDPA Diplomate Fellowship. Accessed October 6, 2025. https://learning.dermpa.orgdiplomate-fellowship
  5. American Academy of Physician Associates. Become a PA. Accessed October 6, 2025. https://www.aapa.org/career-central/become-a-pa/
  6. United States Medical Licensing Examination. Prepare for your exam. Accessed October 6, 2025. https://www.usmle.org/prepare-your-exam
  7. National Board of Osteopathic Medical Examiners. Patient presentations related to the integumentary system. Accessed October 6, 2025. https://www.nbome.org/assessments/comlex-usa/comlex-usa-blueprint/d2-clinical-presentations/integumentary-system
  8. National Commission on Certification of Physician Assistants. PANCE content blueprint. Accessed October 6, 2025. https://prodcmsstoragesa.blob.core.windows.net/uploads/files/PANCEBlueprint.pdf
  9. American Association of Nurse Practitioners. Practice information by state. Accessed October 6, 2025. https://www.aanp.org/practice/practice-information-by-state
  10. Dermatology Nurse Practitioner Certification Board. Eligibility. Accessed October 6, 2025. https://www.dnpcb.org/eligibility.php
  11. National Board of Dermatology Physician Assistants. Certification. Accessed September 3, 2022.
  12. Society of Dermatology Physician Associates. SDPA statement regarding the ABDPA Board Certification Exam for derm PAs. October 8, 2019. Accessed October 6, 2025. https://www.dermpa.org/news/articles/2019-10/sdpa-statement-regarding-abdpa-board-certification-exam-derm-pas
  13. American Board of Dermatology. Residents and fellows. Accessed October 6, 2025. https://www.abderm.org/residents-and-fellows
  14. American Osteopathic Board of Dermatology. Primary certificaiton exam. Accessed October 6, 2025. https://certification.osteopathic.org/dermatology/certification-process/dermatology/written-exams/
  15. Florida Atlantic University. Christine E. Lynn College of Nursing. Dermatology nurse practitioner certificate program. Accessed October 6, 2025. https://www.fau.edu/nursing/academics/certificates/dermatology-program/
  16. Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery. Advanced Practitioner Program.
  17. Coldiron B, Ratnarathorn M. Scope of physician procedures independently billed by mid-level providers in the office setting. JAMA Dermatol. 2014;150:1153-1159.
  18. Zhang M, Zippin J, Kaffenberger B. Trends and scope of dermatology procedures billed by advanced practice professionals from 2012 through 2015. JAMA Dermatol. 2018;154:1040-1044.
  19. Resneck J Jr, Kimball AB. The dermatology workforce shortage. J Am Acad Dermatol. 2004;50:50-54.
  20. Kimball AB, Resneck JS Jr. The US dermatology workforce: a specialty remains in shortage. J Am Acad Dermatol. 2008;59:741-745.
  21. Creadore A, Desai S, Li SJ, et al. Insurance acceptance, appointment wait time, and dermatologist access across practice types in the US. JAMA Dermatol. 2021;157:181-188.
  22. Braun RT, Bond AM, Qian Y, et al. Private equity in dermatology: effect on price, utilization, and spending. Health Aff (Millwood). 2021;40:727-735.
  23. Skaljic M, Lipoff JB. Association of private equity ownership with increased employment of advanced practice professionals in outpatient dermatology offices. J Am Acad Dermatol. 2021;84:1178-1180.
  24. Jalian HR, Avram MM. Mid-level practitioners in dermatology: a need for further study and oversight. JAMA Dermatol. 2014;150:1149-1151.
  25. Sarzynski E, Barry H. Current evidence and controversies: advanced practice providers in healthcare. Am J Manag Care. 2019;25:366-368. 
  26. Nault A, Zhang C, Kim K, et al. Biopsy use in skin cancer diagnosis: comparing dermatology physicians and advanced practice professionals. JAMA Dermatol. 2015;151:899-902.
  27. Anderson AM, Matsumoto M, Saul MI, et al. Accuracy of skin cancer diagnosis by physician assistants compared with dermatologists in a large health care system. JAMA Dermatol. 2018;154:569-573.
  28. Sung C, Salem S, Oulee A, et al. A systematic review: landscape of private equity in dermatology from past to present. J Drugs Dermatol. 2023 Apr 1;22:404-409. doi: 10.36849/JDD.6892.
  29. CMS releases National Healthcare Expenditure and enrollment projections through 2031. Health Management Associates. July 13, 2023. Accessed October 23, 2025. https://www.healthmanagement.com/blog/cms-releases-national-healthcare-expenditure-and-enrollment-projections-through-2031/
References
  1. Robeznieks A. Training gaps between physicians, nonphysicians are significant. American Medical Association. February 17, 2025. Accessed October 23, 2025. https://www.ama-assn.org/practice-management/scope-practice/training-gaps-between-physicians-nonphysicians-are-significant
  2. American Nurses Credentialing Center. Test content outline. Accessed October 6, 2025. https://www.nursingworld.org/globalassets/08282024-exam-24-npd-tco-website.pdf
  3. American Academy of Nurse Practitioners National Certification Board. AANPCB Family Nurse Practitioner Adult-Gerontology Primary Care Nurse Practitioner Psychiatric Mental Health Pratitioner: FNP, AGNP & PMHNP Certification Certification Handbook. American Academy of Nurse Practitioners Certification Board; 2023. Accessed October 6, 2025. https://www.aanpcert.org/resource/documents/AGNP%20FNP%20Candidate%20Handbook.pdf
  4. Society of Dermatology Physician Associates. SDPA Diplomate Fellowship. Accessed October 6, 2025. https://learning.dermpa.orgdiplomate-fellowship
  5. American Academy of Physician Associates. Become a PA. Accessed October 6, 2025. https://www.aapa.org/career-central/become-a-pa/
  6. United States Medical Licensing Examination. Prepare for your exam. Accessed October 6, 2025. https://www.usmle.org/prepare-your-exam
  7. National Board of Osteopathic Medical Examiners. Patient presentations related to the integumentary system. Accessed October 6, 2025. https://www.nbome.org/assessments/comlex-usa/comlex-usa-blueprint/d2-clinical-presentations/integumentary-system
  8. National Commission on Certification of Physician Assistants. PANCE content blueprint. Accessed October 6, 2025. https://prodcmsstoragesa.blob.core.windows.net/uploads/files/PANCEBlueprint.pdf
  9. American Association of Nurse Practitioners. Practice information by state. Accessed October 6, 2025. https://www.aanp.org/practice/practice-information-by-state
  10. Dermatology Nurse Practitioner Certification Board. Eligibility. Accessed October 6, 2025. https://www.dnpcb.org/eligibility.php
  11. National Board of Dermatology Physician Assistants. Certification. Accessed September 3, 2022.
  12. Society of Dermatology Physician Associates. SDPA statement regarding the ABDPA Board Certification Exam for derm PAs. October 8, 2019. Accessed October 6, 2025. https://www.dermpa.org/news/articles/2019-10/sdpa-statement-regarding-abdpa-board-certification-exam-derm-pas
  13. American Board of Dermatology. Residents and fellows. Accessed October 6, 2025. https://www.abderm.org/residents-and-fellows
  14. American Osteopathic Board of Dermatology. Primary certificaiton exam. Accessed October 6, 2025. https://certification.osteopathic.org/dermatology/certification-process/dermatology/written-exams/
  15. Florida Atlantic University. Christine E. Lynn College of Nursing. Dermatology nurse practitioner certificate program. Accessed October 6, 2025. https://www.fau.edu/nursing/academics/certificates/dermatology-program/
  16. Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery. Advanced Practitioner Program.
  17. Coldiron B, Ratnarathorn M. Scope of physician procedures independently billed by mid-level providers in the office setting. JAMA Dermatol. 2014;150:1153-1159.
  18. Zhang M, Zippin J, Kaffenberger B. Trends and scope of dermatology procedures billed by advanced practice professionals from 2012 through 2015. JAMA Dermatol. 2018;154:1040-1044.
  19. Resneck J Jr, Kimball AB. The dermatology workforce shortage. J Am Acad Dermatol. 2004;50:50-54.
  20. Kimball AB, Resneck JS Jr. The US dermatology workforce: a specialty remains in shortage. J Am Acad Dermatol. 2008;59:741-745.
  21. Creadore A, Desai S, Li SJ, et al. Insurance acceptance, appointment wait time, and dermatologist access across practice types in the US. JAMA Dermatol. 2021;157:181-188.
  22. Braun RT, Bond AM, Qian Y, et al. Private equity in dermatology: effect on price, utilization, and spending. Health Aff (Millwood). 2021;40:727-735.
  23. Skaljic M, Lipoff JB. Association of private equity ownership with increased employment of advanced practice professionals in outpatient dermatology offices. J Am Acad Dermatol. 2021;84:1178-1180.
  24. Jalian HR, Avram MM. Mid-level practitioners in dermatology: a need for further study and oversight. JAMA Dermatol. 2014;150:1149-1151.
  25. Sarzynski E, Barry H. Current evidence and controversies: advanced practice providers in healthcare. Am J Manag Care. 2019;25:366-368. 
  26. Nault A, Zhang C, Kim K, et al. Biopsy use in skin cancer diagnosis: comparing dermatology physicians and advanced practice professionals. JAMA Dermatol. 2015;151:899-902.
  27. Anderson AM, Matsumoto M, Saul MI, et al. Accuracy of skin cancer diagnosis by physician assistants compared with dermatologists in a large health care system. JAMA Dermatol. 2018;154:569-573.
  28. Sung C, Salem S, Oulee A, et al. A systematic review: landscape of private equity in dermatology from past to present. J Drugs Dermatol. 2023 Apr 1;22:404-409. doi: 10.36849/JDD.6892.
  29. CMS releases National Healthcare Expenditure and enrollment projections through 2031. Health Management Associates. July 13, 2023. Accessed October 23, 2025. https://www.healthmanagement.com/blog/cms-releases-national-healthcare-expenditure-and-enrollment-projections-through-2031/
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The Current State of Postgraduate Dermatology Training Programs for Advanced Practice Providers

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  • Postgraduate dermatology training programs are available for advanced practice providers (APPs), but they are optional and lack a formal accreditation process.
  • Awareness of these programs and the differences between APPs and physician training may help dermatologists provide safe and effective care in collaborative or supervisory roles.
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Process Improvement for Engaging With Trauma-Focused Evidence-Based Psychotherapy for PTSD

Trauma-focused evidence-based psychotherapies (TF-EBPs), including cognitive processing therapy (CPT) and prolonged exposure therapy (PE), are recommended treatments for posttraumatic stress disorder (PTSD) in clinical practice guidelines.1-3 To increase initiation of these treatments, the US Department of Veterans Affairs (VA) used a large-scale dissemination and implementation effort to improve access to TF-EBP.4,5 These efforts achieved modest success, increasing prevalence of TF-EBP from a handful of veterans in 2004 to an annual prevalence of 14.6% for CPT and 4.3% for PE in 2014.6

Throughout these efforts, qualitative studies have been used to better understand veterans’ perspectives on receiving TF-EBP care.7-18 Barriers to initiation of and engagement in TF-EBP and PTSD care have been identified from these qualitative studies. One identified barrier was lack of knowledge—particularly lack of knowledge about what is meant by a PTSD diagnosis and available treatments.7-10 Stigma (ie, automatic negative associations) toward mental health problems or seeking mental health care also has been identified as a barrier to initiation.7,10-14 Perceptions of poor alignment between treatment and veteran goals, including lack of buy-in for the rationale, served as barriers to initiation and engagement.8,15-18

Using prior qualitative work, numerous initiatives have been developed to reduce stigma, facilitate conversations about how treatment aligns with goals, and fill knowledge gaps, particularly through online resources and shared decision-making.19,20 To better inform the state of veterans’ experiences with TF-EBP, a qualitative investigation was conducted involving veterans who recently initiated TF-EBP. Themes directly related to transitions to TF-EBP were identified; however, all veterans interviewed also described their experiences with TFEBP engagement and mental health care. Consistent with recommendations for qualitative methods, this study extends prior work on transitions to TF-EBP by describing themes with a distinct focus on the experience of engaging with TF-EBP and mental health care.21,22

Methods

The experiences of veterans who were transitioning into TF-EBPs were collected in semistructured interviews and analyzed. The semistructured interview guide was developed and refined in consultation with both qualitative methods experts and PTSD treatment experts to ensure that 6 content domains were appropriately queried: PTSD treatment options, cultural sensitivity of treatment, PTSD treatment selection, transition criteria, beliefs about stabilization treatment, and treatment needs/preferences.

Participants were identified using the VA Corporate Data Warehouse and included post-9/11 veterans who had recently initiated CPT or PE for the first time between September 1, 2021, and September 1, 2022. More details of participant selection are available in Holder et al.21 From a population of 10,814 patients, stratified random sampling generated a recruitment pool of 200 veterans for further outreach. The strata were defined such that this recruitment pool had similar proportions of demographic characteristics (ie, gender, race, ethnicity) to the population of eligible veterans, equivalent distributions of time to CPT or PE initiation (ie, 33.3% < 1 year, 33.3% 1-3 years, and 33.3% > 3 years), and adequate variability in TF-EBP type (ie, 66.7% CPT, 33.3% PE). A manual chart review in the recruitment pool excluded 12 veterans who did not initiate CPT or PE, 1 veteran with evidence of current active psychosis and/or cognitive impairment that would likely preclude comprehension of study materials, and 1 who was deceased.

Eligible veterans from the recruitment pool were contacted in groups of 25. First, a recruitment letter with study information and instructions to opt-out of further contact was mailed or emailed to veterans. After 2 weeks, veterans who had not responded were contacted by phone up to 3 times. Veterans interested in participating were scheduled for a 1-time visit that included verbal consent and the qualitative interview. Metrics were established a priori to ensure an adequately diverse and inclusive sample. Specifically, a minimum number of racial and/or ethnic minority veterans (33%) and women veterans (20%) were sought. Equal distribution across the 3 categories of time from first mental health visit to CPT/PE initiation also was targeted. Throughout enrollment, recruitment efforts were adapted to meet these metrics in the emerging sample. While the goal was to generate a diverse and inclusive sample using these methods, the sample was not intended to be representative of the population.

Of the 186 eligible participants, 21 declined participation and 26 could not be reached. The targeted sample was reached after exhausting contact for 47 veterans and contacting 80 veterans for a final response rate of 40% among fully contacted veterans and 27% among veterans with any contact. The final sample included 30 veterans who received CPT or PE in VA facilities (Table).

1025FDED-ePTSD-T1

After veterans provided verbal consent for study participation, sociodemographic information was verbally reported, and a 30- to 60-minute semistructured qualitative phone interview was recorded and transcribed. Veterans received $40 for participation. All procedures were approved by the University of California San Francisco Institutional Review Board.

Qualitative Data Analysis

Rapid analysis procedures were used to analyze qualitative data. This approach is suitable for focused, moderately structured qualitative analyses in health services research and facilitates rapid dissemination to stakeholders.23 The qualitative analysts were 2 clinical psychologists with expertise in PTSD treatment (NH primary and RR secondary). Consistent with rapid analysis procedures, analysts prepared a templated summary (including relevant quotations) of each interview, organized by the prespecified content domains. Interviews were summarized independently, compared to ensure consistency, and discrepancies were resolved through review of the interview source materials. Individual summary templates were combined into a master analytic matrix to facilitate the identification of patterns and delineation of themes. Analysts routinely met to identify, discuss, and refine preliminary themes, revisiting source materials to reach consensus as needed.

Results

Fifteen themes were identified and organized into 2 distinct focus areas: themes directly related to the transition to TF-EBP (8 themes) and themes related to veterans’ experiences with TF-EBP and general mental health care with potential process-improvement implications (7 themes).21 Seven themes were identified related to experiences with TF-EBP engagement and VA mental health care. The 7 themes related to TF-EBP engagement and VA mental health care themes are summarized with exemplary quotations.

Veterans want a better understanding of psychotherapy and engaging with VA mental health. Veterans reported that they generally had a poor or “nebulous” understanding about the experience of psychotherapy. For example, veterans exhibited confusion about whether certain experiences were equivalent to participating in psychotherapy. They were sometimes unable to distinguish between interactions such as assessment, disability evaluations, peer support, and psychotherapy. One veteran described a conversation with a TFEBP therapist about prior treatment:

She [asked], have you ever been, or gone through a therapy to begin with? And I, I said, well I just chatted with somebody. And she said that’s not, that’s not therapy. So, I was like, oh, it’s not? That’s not what people do?

Veterans were surprised the VA offered a diverse range of psychotherapy interventions, rather than simply therapy. They did not realize there were different types of psychotherapy. As a result, veterans were not aware that some VA mental practitioners have specialty training and certification to provide treatment matched to specific diagnoses or needs. They thought that all clinicians could provide the same care. One veteran described their understanding:

I just figured all mental health people are mental health people. I didn’t have a better understanding of the system and all the different levels and how it plays out and specialties and things like that. Which, I guess, I should have because you have a primary care doctor, but then you have specialists in all these other different sectors that specialize in one particular area. I guess that should’ve been common sense, but it wasn’t.

Stigma was a barrier to seeking and engaging in mental health care. Veterans discovered they had to overcome stigma associated with seeking and engaging in mental health treatment. Military culture was often discussed as promoting stigma regarding mental health treatment. Specifically, veterans described that seeking treatment meant “either, I’m weak or I’m gonna be seen as weak.” In active-duty settings, the strategy for dealing with mental health symptoms was to “leave those feelings, you push ‘em aside,” an approach highly inconsistent with TF-EBP. In some cases, incorrect information about the VA and PTSD was presented as part of discharge from the military, leading to long-term skepticism of the VA and PTSD treatment. One veteran described his experience as part of a class on the VA compensation and pension assessment process for service-connected disabilities during his military discharge:

[A fellow discharging soldier asked] what about like PTSD, gettin’ rated for PTSD. I hear they take our weapons and stuff like we can’t own firearms and all that stuff. And [the instructor] was like, well, yes that’s a thing. He didn’t explain it like if you get compensated for PTSD you don’t lose your rights to carry a firearm or to have, to be able to go hunting.

Importantly, veterans often described how other identities (eg, race, ethnicity, gender, region of origin) interacted with military culture to enhance stigma. Hearing messaging from multiple sources reinforced beliefs that mental health treatment is inappropriate or is associated with weakness:

As a first-generation Italian, I was always taught keep your feelings to yourself. Never talk outside your family. Never bring up problems to other people and stuff like that. Same with the military. And then the old stigma working in [emergency medical services] and public safety, you’re weak if you get help.

The fundamentals of therapy, including rapport and flexibility, were important. Veterans valued nonspecific therapy factors, genuine empathy, building trust, being honest about treatment, personality, and rapport. These characteristics were almost universally described as particularly important:

I liked the fact that she made it personable and she cared. It wasn’t just like, here, we’re gonna start this. She explained it in the ways I could understand, not in medical terms, so to speak, but that’s what I liked about her. She really cared about what she did and helping me.

Flexibility was viewed as an asset, particularly when clinicians acknowledged veteran autonomy. A consistent example was when veterans were able to titrate trauma disclosure. One veteran described this flexible treatment experience: “She was right there in the room, she said, you know, at any time, you know, we could stop, we could debrief.”

Experiences of clinician flexibility and personalization of therapy were contrasted with experiences of overly rigid therapy. Overemphasis on protocols created barriers, often because treatment did not feel personalized. One veteran described how a clinician’s task-oriented approach interfered with their ability to engage in TF-EBP:

They listened, but it just didn’t seem like they were listening, because they really wanted to stay on task… So, I felt like if the person was more concerned, or more sympathetic to the things that was also going on in my life at that present time, I think I would’ve felt more comfortable talking about what was the PTSD part, too.

Veterans valued shared decision-making prior to TF-EBP initiation. Veterans typically described being involved in a shared decision-making process prior to initiating TF-EBP. During these sessions, clinicians discussed treatment options and provided veterans with a variety of materials describing treatments (eg, pamphlets, websites, videos, statistics). Most veterans appreciated being able to reflect on and discuss treatment options with their clinicians. Being given time in and out of session to review was viewed as valuable and increased confidence in treatment choice. One veteran described their experience:

I was given the information, you know, they gave me handouts, PDFs, whatever was available, and let me read over it. I didn’t have to choose anything right then and there, you know, they let me sleep on it. And I got back to them after some thought.

However, some veterans felt overwhelmed by being presented with too much information and did not believe they knew enough to make a final treatment decision. One veteran described being asked to contribute to the treatment decision:

I definitely asked [the clinician] to weigh in on maybe what he thought was best, because—I mean, I don’t know… I’m not necessarily sure I know what I think is best. I think we’re just lucky I’m here, so if you can give me a solid and help me out here by telling me just based on what I’ve said to you and the things that I’ve gone through, what do you think?

Veterans who perceived that their treatment preferences were respected had a positive outlook on TF-EBP. As part of the shared-decision making process, veterans typically described being given choices among PTSD treatments. One way that preferences were respected was through clinicians tailoring treatment descriptions to a veteran’s unique symptoms, experiences, and values. In these cases, clinicians observed specific concerns and clearly linked treatment principles to those concerns. For example, one veteran described their clinician’s recommendation for PE: “The hardest thing for me is to do the normal things like grocery store or getting on a train or anything like that. And so, he suggested that [PE] would be a good idea.”

In other cases, veterans wanted the highest quality of treatment rather than a match between treatment principles and the veteran’s presentation, goals, or strengths. These veterans wanted the best treatment available for PTSD and valued research support, recommendations from clinical practice guidelines, or clinician confidence in the effectiveness of the treatment. One veteran described this perspective:

I just wanted to be able to really tackle it in the best way possible and in the most like aggressive way possible. And it seemed like PE really was going to, they said that it’s a difficult type of therapy, but I really just wanted to kind of do the best that I could to eradicate some of the issues that I was having.

When veterans perceived a lack of respect for their preferences, they were hesitant about TF-EBP. For some veterans, a generic pitch for a TF-EBP was detrimental in the absence of the personal connection between the treatment and their own symptoms, goals, or strengths. These veterans did not question whether the treatment was effective in general but did question whether the treatment was best for them. One veteran described the contrast between their clinician’s perspective and their own.

I felt like they felt very comfortable, very confident in [CPT] being the program, because it was comfortable for them. Because they did it several times. And maybe they had a lot of success with other individuals... but they were very comfortable with that one, as a provider, more than: Is this the best fit for [me]?

Some veterans perceived little concern for their preferences and a lack of choice in available treatments, which tended to perpetuate negative perceptions of TFEBP. These veterans described their lack of choices with frustration. Alternatives to TFEBP were described by these veterans as so undesirable that they did not believe they had a real choice:

[CPT] was the only decision they had. There was nothing else for PTSD. They didn’t offer anything else. So, I mean it wasn’t a decision. It was either … take treatment or don’t take treatment at all… Actually, I need to correct myself. So, there were 2 options, group therapy or CPT. I forgot about that. I’m not a big group guy so I chose the CPT.

Another veteran was offered a choice between therapeutic approaches, but all were delivered via telehealth (consistent with the transition to virtual services during the COVID-19 pandemic). For this veteran, not only was the distinction between approaches unclear, but the choice between approaches was unimportant compared to the mode of delivery.

This happened during COVID-19 and VA stopped seeing anybody physically, face-to-face. So my only option for therapy was [telehealth]… There was like 3 of them, and I tried to figure out, you know, from the layperson’s perspective, like: I don’t know which one to go with.

Veterans wanted to be asked about their cultural identity. Veterans valued when clinicians asked questions about cultural identity as part of their mental health treatment and listened to their cultural context. Cultural identity factors extended beyond factors such as race, ethnicity, gender, and sexual orientation to religion, military culture, and regionality. Veterans often described situations where they wished clinicians would ask the question or initiate conversations about culture. A veteran highlighted the importance of their faith but noted that it was a taboo topic. Their clinician did not say “we don’t go there,” but they “never dove into it either.” Another veteran expressed a desire for their clinician to ask questions about experiences in the National Guard and as an African American veteran:

If a provider was to say like: Oh, you know, it’s a stressful situation being a part of the military, being in the National Guard. You know, just asking questions about that. I think that would really go a long way… Being African American was difficult as well. And more so because of my region, I think… I felt like it would probably be an uncomfortable subject to speak on… I mean, it wasn’t anything that my providers necessarily did, it was more so just because it wasn’t brought up.

One common area of concern for veterans was a match between veteran and therapist demographics. When asked about how their cultural identity influenced treatment, several veterans described the relevance of therapist match. Much like questions about their own cultural identity, veterans valued being asked about identity preferences in clinicians (eg, gender or race matching), rather than having to bring up the preference themselves. One veteran described relief at this question being asked directly: “I was relieved when she had asked [whether I wanted a male or female clinician] primarily because I was going to ask that or bring that up somehow. But her asking that before me was a weight off my shoulders.”

Discussing cultural identity through treatment strengthened veterans’ engagement in therapy. Many veterans appreciated when analogies used in therapy were relevant to their cultural experiences and when clinicians understood their culture (eg, military culture, race, ethnicity, religious beliefs, sexual orientation). One veteran described how their clinician understood military culture and made connections between military culture and the rationale for TF-EBP, which strengthened the veteran’s buy-in for the treatment and alliance with the clinician:

At the beginning when she was explaining PTSD, and I remember she said that your brain needed to think this way when you were in the military because it was a way of protecting and surviving, so your brain was doing that in order for you to survive in whatever areas you were because there was danger. So, your brain had you thinking that way. But now, you’re not in those situations anymore. You’re not in danger. You’re not in the military, but your brain is still thinking you are, and that’s what PTSD generally does to you.

Specific elements of TF-EBP also provided opportunities to discuss and integrate important aspects of identity. This is accomplished in PE by assigning relevant in vivo exercises. In CPT, “connecting the dots” on how prior experiences influenced trauma-related stuck points achieved this element. One veteran described their experience with a clinician who was comfortable discussing the veteran’s sexual orientation and recognized the impacts of prior trauma on intimacy:

They’re very different, and there’s a lot of things that can be accepted in gay relationships that are not in straight ones. With all that said, I think [the PE therapist] did a fantastic job being not—like never once did she laugh or make an uncomfortable comment or say she didn’t wanna talk about something when like part of the reason I wanted to get into therapy is that my partner and I weren’t having sex unless I used alcohol.

Discussion

As part of a larger national qualitative investigation of the experiences of veterans who recently initiated TF-EBP, veterans discussed their experiences with therapy and mental health care that have important implications for continued process improvement.21 Three key areas for continued process improvement were identified: (1) providing information about the diverse range of mental health care services at the VA and the implications of this continuum of care; (2) consideration of veteran preferences in treatment decision-making, including the importance of perceived choice; and (3) incorporating cultural assessment and cultural responsiveness into case conceptualization and treatment.

One area of process improvement identified was increasing knowledge about different types of psychotherapy and the continuum of care available at the VA. Veterans in this study confused or conflated participating in psychotherapy with talking about mental health symptoms with a clinician (eg, assessment, disability evaluation). They were sometimes surprised that psychotherapy is an umbrella term referring to a variety of different modalities. The downstream impact of these misunderstandings was a perception of VA mental health care as nebulous. Veterans were surprised that all mental health practitioners were unable to provide the same care. Confusion may have been compounded by highly variable referral processes across VA.24 To address this, clinicians have developed local educational resources and handouts for both veterans and referring clinicians from nonmental health and general mental health specialties.25 Given the variability in referral processes both between and within VA medical centers, national dissemination of these educational materials may be more difficult compared to materials for TF-EBPs.24 The VA started to use behavioral health interdisciplinary program (BHIP) teams, which are designed to be clinical homes for veterans connected with a central clinician who can explain and coordinate their mental health care as well as bring more consistency to the referral process.26 The ongoing transition toward the BHIP model of mental health care at VA may provide the opportunity to consolidate and integrate knowledge about the VA approach to mental health care, potentially filling knowledge gaps.

A second area of process improvement focused on the shared decision-making process. Consistent with mental health initiatives, veterans generally believed they had received sufficient information about TF-EBP and engaged in shared decision-making with clinicians.20,27 Veterans were given educational materials to review and had the opportunity to discuss these materials with clinicians. However, veterans described variability in the success of shared decision-making. Although veterans valued receiving accurate, comprehensible information to support treatment decisions, some preferred to defer to clinicians’ expertise regarding which treatment to pursue. While these veterans valued information, they also valued the expertise of clinicians in explaining why specific treatments would be beneficial. A key contributor to veterans satisfaction was assessing how veterans wanted to engage in the decision-making process and respecting those preferences.28 Veterans approached shared decision-making differently, from making decisions independently after receiving information to relying solely on clinician recommendation. The process was most successful when clinicians articulated how their recommended treatment aligned with a veteran’s preferences, including recommendations based on specific values (eg, personalized match vs being the best). Another important consideration is ensuring veterans know they can receive a variety of different types of mental health services available in different modalities (eg, virtual vs in-person; group vs individual). When veterans did not perceive choice in treatment aspects important to them (typically despite having choices), they were less satisfied with their TF-EBP experience.

A final area of process improvement identified involves how therapists address important aspects of culture. Veterans often described mental health stigma coming from intersecting cultural identities and expressed appreciation when therapists helped them recognize the impact of these beliefs on treatment. Some veterans did not discuss important aspects of their identity with clinicians, including race/ethnicity, religion, and military culture. Veterans did not report negative interactions with clinicians or experiences suggesting it was inappropriate to discuss identity; however, they were reluctant to independently raise these identity factors. Strategies such as the ADDRESSING framework, a mnemonic acronym that describes a series of potentially relevant characteristics, can help clinicians comprehensively consider different aspects that may be relevant to veterans, modeling that discussion of relevant these characteristics is welcome in TF-EBP.29 Veterans reported that making culturally relevant connections enhanced the TF-EBP experience, most commonly with military culture. These data support that TF-EBP delivery with attention to culture should be an integrated part of treatment, supporting engagement and therapeutic alliance.30 The VA National Center for PTSD consultation program is a resource to support clinicians in assessing and incorporating relevant aspects of cultural identity.31 For example, the National Center for PTSD provides a guide for using case conceptualization to address patient reactions to race-based violence during PTSD treatment.32 Both manualized design and therapist certification training can reinforce that assessing and attending to case conceptualization (including identity factors) is an integral component of TF-EBP.33,34

Limitations

While the current study has numerous strengths (eg, national veteran sampling, robust qualitative methods), results should be considered within the context of study limitations. First, veteran participants all received TF-EBP, and the perspectives of veterans who never initiate TF-EBP may differ. Despite the strong sampling approach, the study design is not intended to be generalizable to all veterans receiving TF-EBP for PTSD. Qualitative analysis yielded 15 themes, described in this study and prior research, consistent with recommendations.21,22 This approach allows rich description of distinct focus areas that would not be possible in a single manuscript. Nonetheless, all veterans interviewed described their experiences in TF-EBP and general mental health care, the focus of the semistructured interview guide was on the experience of transitioning from other treatment to TF-EBP.

Conclusion

This study describes themes related to general mental health and TF-EBP process improvement as part of a larger study on transitions in PTSD care.21,22 Veterans valued the fundamentals of therapy, including rapport and flexibility. Treatment-specific rapport (eg, pointing out treatment progress and effort in completing treatment components) and flexibility within the context of fidelity (ie, personalizing treatment while maintaining core treatment elements) may be most effective at engaging veterans in recommended PTSD treatments.18,34 In addition to successes, themes suggest multiple opportunities for process improvement. Ongoing VA initiatives and priorities (ie, BHIP, shared decision-making, consultation services) aim to improve processes consistent with veteran recommendations. Future research is needed to evaluate the success of these and other programs to optimize access to and engagement in recommended PTSD treatments.

References
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Author and Disclosure Information

Nicholas Holder, PhDa,b,c; Rachel M. Ranney, PhDa,b,c,d; Natalie Purcell, PhD, MPAc,e,f; Gayle Y. Iwamasa, PhD, HSPPg; Alejandra K. Delgado, BAa,b; Adam Batten, MS, PSTATa; Thomas C. Neylan, MDa,b,d; Brian Shiner, MD, MPHg,h,i; Shira Maguen, PhDa,b,c,d

Author affiliations
aSan Francisco Veterans Affairs Health Care System, California
bUniversity of California San Francisco School of Medicine
cCenter for Data to Discovery and Delivery Innovation, San Francisco, California
dSierra Pacific Mental Illness Research, Education, and Clinical Center, Palo Alto, California
eUniversity of California San Francisco School of Nursing
fDepartment of Veterans Affairs, Washington DC
gWhite River Junction Veterans Affairs Health Care System, Vermont
hNational Center for Posttraumatic Stress Disorder, White River Junction, Vermont
iGeisel School of Medicine at Dartmouth, Hanover, New Hampshire

Correspondence: Nicholas Holder (nicholas.davis.holder@ gmail.com)

Fed Pract. 2025; November 7. Published online ahead of print. doi:10.12788/fp.0627

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Nicholas Holder, PhDa,b,c; Rachel M. Ranney, PhDa,b,c,d; Natalie Purcell, PhD, MPAc,e,f; Gayle Y. Iwamasa, PhD, HSPPg; Alejandra K. Delgado, BAa,b; Adam Batten, MS, PSTATa; Thomas C. Neylan, MDa,b,d; Brian Shiner, MD, MPHg,h,i; Shira Maguen, PhDa,b,c,d

Author affiliations
aSan Francisco Veterans Affairs Health Care System, California
bUniversity of California San Francisco School of Medicine
cCenter for Data to Discovery and Delivery Innovation, San Francisco, California
dSierra Pacific Mental Illness Research, Education, and Clinical Center, Palo Alto, California
eUniversity of California San Francisco School of Nursing
fDepartment of Veterans Affairs, Washington DC
gWhite River Junction Veterans Affairs Health Care System, Vermont
hNational Center for Posttraumatic Stress Disorder, White River Junction, Vermont
iGeisel School of Medicine at Dartmouth, Hanover, New Hampshire

Correspondence: Nicholas Holder (nicholas.davis.holder@ gmail.com)

Fed Pract. 2025; November 7. Published online ahead of print. doi:10.12788/fp.0627

Author and Disclosure Information

Nicholas Holder, PhDa,b,c; Rachel M. Ranney, PhDa,b,c,d; Natalie Purcell, PhD, MPAc,e,f; Gayle Y. Iwamasa, PhD, HSPPg; Alejandra K. Delgado, BAa,b; Adam Batten, MS, PSTATa; Thomas C. Neylan, MDa,b,d; Brian Shiner, MD, MPHg,h,i; Shira Maguen, PhDa,b,c,d

Author affiliations
aSan Francisco Veterans Affairs Health Care System, California
bUniversity of California San Francisco School of Medicine
cCenter for Data to Discovery and Delivery Innovation, San Francisco, California
dSierra Pacific Mental Illness Research, Education, and Clinical Center, Palo Alto, California
eUniversity of California San Francisco School of Nursing
fDepartment of Veterans Affairs, Washington DC
gWhite River Junction Veterans Affairs Health Care System, Vermont
hNational Center for Posttraumatic Stress Disorder, White River Junction, Vermont
iGeisel School of Medicine at Dartmouth, Hanover, New Hampshire

Correspondence: Nicholas Holder (nicholas.davis.holder@ gmail.com)

Fed Pract. 2025; November 7. Published online ahead of print. doi:10.12788/fp.0627

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Article PDF

Trauma-focused evidence-based psychotherapies (TF-EBPs), including cognitive processing therapy (CPT) and prolonged exposure therapy (PE), are recommended treatments for posttraumatic stress disorder (PTSD) in clinical practice guidelines.1-3 To increase initiation of these treatments, the US Department of Veterans Affairs (VA) used a large-scale dissemination and implementation effort to improve access to TF-EBP.4,5 These efforts achieved modest success, increasing prevalence of TF-EBP from a handful of veterans in 2004 to an annual prevalence of 14.6% for CPT and 4.3% for PE in 2014.6

Throughout these efforts, qualitative studies have been used to better understand veterans’ perspectives on receiving TF-EBP care.7-18 Barriers to initiation of and engagement in TF-EBP and PTSD care have been identified from these qualitative studies. One identified barrier was lack of knowledge—particularly lack of knowledge about what is meant by a PTSD diagnosis and available treatments.7-10 Stigma (ie, automatic negative associations) toward mental health problems or seeking mental health care also has been identified as a barrier to initiation.7,10-14 Perceptions of poor alignment between treatment and veteran goals, including lack of buy-in for the rationale, served as barriers to initiation and engagement.8,15-18

Using prior qualitative work, numerous initiatives have been developed to reduce stigma, facilitate conversations about how treatment aligns with goals, and fill knowledge gaps, particularly through online resources and shared decision-making.19,20 To better inform the state of veterans’ experiences with TF-EBP, a qualitative investigation was conducted involving veterans who recently initiated TF-EBP. Themes directly related to transitions to TF-EBP were identified; however, all veterans interviewed also described their experiences with TFEBP engagement and mental health care. Consistent with recommendations for qualitative methods, this study extends prior work on transitions to TF-EBP by describing themes with a distinct focus on the experience of engaging with TF-EBP and mental health care.21,22

Methods

The experiences of veterans who were transitioning into TF-EBPs were collected in semistructured interviews and analyzed. The semistructured interview guide was developed and refined in consultation with both qualitative methods experts and PTSD treatment experts to ensure that 6 content domains were appropriately queried: PTSD treatment options, cultural sensitivity of treatment, PTSD treatment selection, transition criteria, beliefs about stabilization treatment, and treatment needs/preferences.

Participants were identified using the VA Corporate Data Warehouse and included post-9/11 veterans who had recently initiated CPT or PE for the first time between September 1, 2021, and September 1, 2022. More details of participant selection are available in Holder et al.21 From a population of 10,814 patients, stratified random sampling generated a recruitment pool of 200 veterans for further outreach. The strata were defined such that this recruitment pool had similar proportions of demographic characteristics (ie, gender, race, ethnicity) to the population of eligible veterans, equivalent distributions of time to CPT or PE initiation (ie, 33.3% < 1 year, 33.3% 1-3 years, and 33.3% > 3 years), and adequate variability in TF-EBP type (ie, 66.7% CPT, 33.3% PE). A manual chart review in the recruitment pool excluded 12 veterans who did not initiate CPT or PE, 1 veteran with evidence of current active psychosis and/or cognitive impairment that would likely preclude comprehension of study materials, and 1 who was deceased.

Eligible veterans from the recruitment pool were contacted in groups of 25. First, a recruitment letter with study information and instructions to opt-out of further contact was mailed or emailed to veterans. After 2 weeks, veterans who had not responded were contacted by phone up to 3 times. Veterans interested in participating were scheduled for a 1-time visit that included verbal consent and the qualitative interview. Metrics were established a priori to ensure an adequately diverse and inclusive sample. Specifically, a minimum number of racial and/or ethnic minority veterans (33%) and women veterans (20%) were sought. Equal distribution across the 3 categories of time from first mental health visit to CPT/PE initiation also was targeted. Throughout enrollment, recruitment efforts were adapted to meet these metrics in the emerging sample. While the goal was to generate a diverse and inclusive sample using these methods, the sample was not intended to be representative of the population.

Of the 186 eligible participants, 21 declined participation and 26 could not be reached. The targeted sample was reached after exhausting contact for 47 veterans and contacting 80 veterans for a final response rate of 40% among fully contacted veterans and 27% among veterans with any contact. The final sample included 30 veterans who received CPT or PE in VA facilities (Table).

1025FDED-ePTSD-T1

After veterans provided verbal consent for study participation, sociodemographic information was verbally reported, and a 30- to 60-minute semistructured qualitative phone interview was recorded and transcribed. Veterans received $40 for participation. All procedures were approved by the University of California San Francisco Institutional Review Board.

Qualitative Data Analysis

Rapid analysis procedures were used to analyze qualitative data. This approach is suitable for focused, moderately structured qualitative analyses in health services research and facilitates rapid dissemination to stakeholders.23 The qualitative analysts were 2 clinical psychologists with expertise in PTSD treatment (NH primary and RR secondary). Consistent with rapid analysis procedures, analysts prepared a templated summary (including relevant quotations) of each interview, organized by the prespecified content domains. Interviews were summarized independently, compared to ensure consistency, and discrepancies were resolved through review of the interview source materials. Individual summary templates were combined into a master analytic matrix to facilitate the identification of patterns and delineation of themes. Analysts routinely met to identify, discuss, and refine preliminary themes, revisiting source materials to reach consensus as needed.

Results

Fifteen themes were identified and organized into 2 distinct focus areas: themes directly related to the transition to TF-EBP (8 themes) and themes related to veterans’ experiences with TF-EBP and general mental health care with potential process-improvement implications (7 themes).21 Seven themes were identified related to experiences with TF-EBP engagement and VA mental health care. The 7 themes related to TF-EBP engagement and VA mental health care themes are summarized with exemplary quotations.

Veterans want a better understanding of psychotherapy and engaging with VA mental health. Veterans reported that they generally had a poor or “nebulous” understanding about the experience of psychotherapy. For example, veterans exhibited confusion about whether certain experiences were equivalent to participating in psychotherapy. They were sometimes unable to distinguish between interactions such as assessment, disability evaluations, peer support, and psychotherapy. One veteran described a conversation with a TFEBP therapist about prior treatment:

She [asked], have you ever been, or gone through a therapy to begin with? And I, I said, well I just chatted with somebody. And she said that’s not, that’s not therapy. So, I was like, oh, it’s not? That’s not what people do?

Veterans were surprised the VA offered a diverse range of psychotherapy interventions, rather than simply therapy. They did not realize there were different types of psychotherapy. As a result, veterans were not aware that some VA mental practitioners have specialty training and certification to provide treatment matched to specific diagnoses or needs. They thought that all clinicians could provide the same care. One veteran described their understanding:

I just figured all mental health people are mental health people. I didn’t have a better understanding of the system and all the different levels and how it plays out and specialties and things like that. Which, I guess, I should have because you have a primary care doctor, but then you have specialists in all these other different sectors that specialize in one particular area. I guess that should’ve been common sense, but it wasn’t.

Stigma was a barrier to seeking and engaging in mental health care. Veterans discovered they had to overcome stigma associated with seeking and engaging in mental health treatment. Military culture was often discussed as promoting stigma regarding mental health treatment. Specifically, veterans described that seeking treatment meant “either, I’m weak or I’m gonna be seen as weak.” In active-duty settings, the strategy for dealing with mental health symptoms was to “leave those feelings, you push ‘em aside,” an approach highly inconsistent with TF-EBP. In some cases, incorrect information about the VA and PTSD was presented as part of discharge from the military, leading to long-term skepticism of the VA and PTSD treatment. One veteran described his experience as part of a class on the VA compensation and pension assessment process for service-connected disabilities during his military discharge:

[A fellow discharging soldier asked] what about like PTSD, gettin’ rated for PTSD. I hear they take our weapons and stuff like we can’t own firearms and all that stuff. And [the instructor] was like, well, yes that’s a thing. He didn’t explain it like if you get compensated for PTSD you don’t lose your rights to carry a firearm or to have, to be able to go hunting.

Importantly, veterans often described how other identities (eg, race, ethnicity, gender, region of origin) interacted with military culture to enhance stigma. Hearing messaging from multiple sources reinforced beliefs that mental health treatment is inappropriate or is associated with weakness:

As a first-generation Italian, I was always taught keep your feelings to yourself. Never talk outside your family. Never bring up problems to other people and stuff like that. Same with the military. And then the old stigma working in [emergency medical services] and public safety, you’re weak if you get help.

The fundamentals of therapy, including rapport and flexibility, were important. Veterans valued nonspecific therapy factors, genuine empathy, building trust, being honest about treatment, personality, and rapport. These characteristics were almost universally described as particularly important:

I liked the fact that she made it personable and she cared. It wasn’t just like, here, we’re gonna start this. She explained it in the ways I could understand, not in medical terms, so to speak, but that’s what I liked about her. She really cared about what she did and helping me.

Flexibility was viewed as an asset, particularly when clinicians acknowledged veteran autonomy. A consistent example was when veterans were able to titrate trauma disclosure. One veteran described this flexible treatment experience: “She was right there in the room, she said, you know, at any time, you know, we could stop, we could debrief.”

Experiences of clinician flexibility and personalization of therapy were contrasted with experiences of overly rigid therapy. Overemphasis on protocols created barriers, often because treatment did not feel personalized. One veteran described how a clinician’s task-oriented approach interfered with their ability to engage in TF-EBP:

They listened, but it just didn’t seem like they were listening, because they really wanted to stay on task… So, I felt like if the person was more concerned, or more sympathetic to the things that was also going on in my life at that present time, I think I would’ve felt more comfortable talking about what was the PTSD part, too.

Veterans valued shared decision-making prior to TF-EBP initiation. Veterans typically described being involved in a shared decision-making process prior to initiating TF-EBP. During these sessions, clinicians discussed treatment options and provided veterans with a variety of materials describing treatments (eg, pamphlets, websites, videos, statistics). Most veterans appreciated being able to reflect on and discuss treatment options with their clinicians. Being given time in and out of session to review was viewed as valuable and increased confidence in treatment choice. One veteran described their experience:

I was given the information, you know, they gave me handouts, PDFs, whatever was available, and let me read over it. I didn’t have to choose anything right then and there, you know, they let me sleep on it. And I got back to them after some thought.

However, some veterans felt overwhelmed by being presented with too much information and did not believe they knew enough to make a final treatment decision. One veteran described being asked to contribute to the treatment decision:

I definitely asked [the clinician] to weigh in on maybe what he thought was best, because—I mean, I don’t know… I’m not necessarily sure I know what I think is best. I think we’re just lucky I’m here, so if you can give me a solid and help me out here by telling me just based on what I’ve said to you and the things that I’ve gone through, what do you think?

Veterans who perceived that their treatment preferences were respected had a positive outlook on TF-EBP. As part of the shared-decision making process, veterans typically described being given choices among PTSD treatments. One way that preferences were respected was through clinicians tailoring treatment descriptions to a veteran’s unique symptoms, experiences, and values. In these cases, clinicians observed specific concerns and clearly linked treatment principles to those concerns. For example, one veteran described their clinician’s recommendation for PE: “The hardest thing for me is to do the normal things like grocery store or getting on a train or anything like that. And so, he suggested that [PE] would be a good idea.”

In other cases, veterans wanted the highest quality of treatment rather than a match between treatment principles and the veteran’s presentation, goals, or strengths. These veterans wanted the best treatment available for PTSD and valued research support, recommendations from clinical practice guidelines, or clinician confidence in the effectiveness of the treatment. One veteran described this perspective:

I just wanted to be able to really tackle it in the best way possible and in the most like aggressive way possible. And it seemed like PE really was going to, they said that it’s a difficult type of therapy, but I really just wanted to kind of do the best that I could to eradicate some of the issues that I was having.

When veterans perceived a lack of respect for their preferences, they were hesitant about TF-EBP. For some veterans, a generic pitch for a TF-EBP was detrimental in the absence of the personal connection between the treatment and their own symptoms, goals, or strengths. These veterans did not question whether the treatment was effective in general but did question whether the treatment was best for them. One veteran described the contrast between their clinician’s perspective and their own.

I felt like they felt very comfortable, very confident in [CPT] being the program, because it was comfortable for them. Because they did it several times. And maybe they had a lot of success with other individuals... but they were very comfortable with that one, as a provider, more than: Is this the best fit for [me]?

Some veterans perceived little concern for their preferences and a lack of choice in available treatments, which tended to perpetuate negative perceptions of TFEBP. These veterans described their lack of choices with frustration. Alternatives to TFEBP were described by these veterans as so undesirable that they did not believe they had a real choice:

[CPT] was the only decision they had. There was nothing else for PTSD. They didn’t offer anything else. So, I mean it wasn’t a decision. It was either … take treatment or don’t take treatment at all… Actually, I need to correct myself. So, there were 2 options, group therapy or CPT. I forgot about that. I’m not a big group guy so I chose the CPT.

Another veteran was offered a choice between therapeutic approaches, but all were delivered via telehealth (consistent with the transition to virtual services during the COVID-19 pandemic). For this veteran, not only was the distinction between approaches unclear, but the choice between approaches was unimportant compared to the mode of delivery.

This happened during COVID-19 and VA stopped seeing anybody physically, face-to-face. So my only option for therapy was [telehealth]… There was like 3 of them, and I tried to figure out, you know, from the layperson’s perspective, like: I don’t know which one to go with.

Veterans wanted to be asked about their cultural identity. Veterans valued when clinicians asked questions about cultural identity as part of their mental health treatment and listened to their cultural context. Cultural identity factors extended beyond factors such as race, ethnicity, gender, and sexual orientation to religion, military culture, and regionality. Veterans often described situations where they wished clinicians would ask the question or initiate conversations about culture. A veteran highlighted the importance of their faith but noted that it was a taboo topic. Their clinician did not say “we don’t go there,” but they “never dove into it either.” Another veteran expressed a desire for their clinician to ask questions about experiences in the National Guard and as an African American veteran:

If a provider was to say like: Oh, you know, it’s a stressful situation being a part of the military, being in the National Guard. You know, just asking questions about that. I think that would really go a long way… Being African American was difficult as well. And more so because of my region, I think… I felt like it would probably be an uncomfortable subject to speak on… I mean, it wasn’t anything that my providers necessarily did, it was more so just because it wasn’t brought up.

One common area of concern for veterans was a match between veteran and therapist demographics. When asked about how their cultural identity influenced treatment, several veterans described the relevance of therapist match. Much like questions about their own cultural identity, veterans valued being asked about identity preferences in clinicians (eg, gender or race matching), rather than having to bring up the preference themselves. One veteran described relief at this question being asked directly: “I was relieved when she had asked [whether I wanted a male or female clinician] primarily because I was going to ask that or bring that up somehow. But her asking that before me was a weight off my shoulders.”

Discussing cultural identity through treatment strengthened veterans’ engagement in therapy. Many veterans appreciated when analogies used in therapy were relevant to their cultural experiences and when clinicians understood their culture (eg, military culture, race, ethnicity, religious beliefs, sexual orientation). One veteran described how their clinician understood military culture and made connections between military culture and the rationale for TF-EBP, which strengthened the veteran’s buy-in for the treatment and alliance with the clinician:

At the beginning when she was explaining PTSD, and I remember she said that your brain needed to think this way when you were in the military because it was a way of protecting and surviving, so your brain was doing that in order for you to survive in whatever areas you were because there was danger. So, your brain had you thinking that way. But now, you’re not in those situations anymore. You’re not in danger. You’re not in the military, but your brain is still thinking you are, and that’s what PTSD generally does to you.

Specific elements of TF-EBP also provided opportunities to discuss and integrate important aspects of identity. This is accomplished in PE by assigning relevant in vivo exercises. In CPT, “connecting the dots” on how prior experiences influenced trauma-related stuck points achieved this element. One veteran described their experience with a clinician who was comfortable discussing the veteran’s sexual orientation and recognized the impacts of prior trauma on intimacy:

They’re very different, and there’s a lot of things that can be accepted in gay relationships that are not in straight ones. With all that said, I think [the PE therapist] did a fantastic job being not—like never once did she laugh or make an uncomfortable comment or say she didn’t wanna talk about something when like part of the reason I wanted to get into therapy is that my partner and I weren’t having sex unless I used alcohol.

Discussion

As part of a larger national qualitative investigation of the experiences of veterans who recently initiated TF-EBP, veterans discussed their experiences with therapy and mental health care that have important implications for continued process improvement.21 Three key areas for continued process improvement were identified: (1) providing information about the diverse range of mental health care services at the VA and the implications of this continuum of care; (2) consideration of veteran preferences in treatment decision-making, including the importance of perceived choice; and (3) incorporating cultural assessment and cultural responsiveness into case conceptualization and treatment.

One area of process improvement identified was increasing knowledge about different types of psychotherapy and the continuum of care available at the VA. Veterans in this study confused or conflated participating in psychotherapy with talking about mental health symptoms with a clinician (eg, assessment, disability evaluation). They were sometimes surprised that psychotherapy is an umbrella term referring to a variety of different modalities. The downstream impact of these misunderstandings was a perception of VA mental health care as nebulous. Veterans were surprised that all mental health practitioners were unable to provide the same care. Confusion may have been compounded by highly variable referral processes across VA.24 To address this, clinicians have developed local educational resources and handouts for both veterans and referring clinicians from nonmental health and general mental health specialties.25 Given the variability in referral processes both between and within VA medical centers, national dissemination of these educational materials may be more difficult compared to materials for TF-EBPs.24 The VA started to use behavioral health interdisciplinary program (BHIP) teams, which are designed to be clinical homes for veterans connected with a central clinician who can explain and coordinate their mental health care as well as bring more consistency to the referral process.26 The ongoing transition toward the BHIP model of mental health care at VA may provide the opportunity to consolidate and integrate knowledge about the VA approach to mental health care, potentially filling knowledge gaps.

A second area of process improvement focused on the shared decision-making process. Consistent with mental health initiatives, veterans generally believed they had received sufficient information about TF-EBP and engaged in shared decision-making with clinicians.20,27 Veterans were given educational materials to review and had the opportunity to discuss these materials with clinicians. However, veterans described variability in the success of shared decision-making. Although veterans valued receiving accurate, comprehensible information to support treatment decisions, some preferred to defer to clinicians’ expertise regarding which treatment to pursue. While these veterans valued information, they also valued the expertise of clinicians in explaining why specific treatments would be beneficial. A key contributor to veterans satisfaction was assessing how veterans wanted to engage in the decision-making process and respecting those preferences.28 Veterans approached shared decision-making differently, from making decisions independently after receiving information to relying solely on clinician recommendation. The process was most successful when clinicians articulated how their recommended treatment aligned with a veteran’s preferences, including recommendations based on specific values (eg, personalized match vs being the best). Another important consideration is ensuring veterans know they can receive a variety of different types of mental health services available in different modalities (eg, virtual vs in-person; group vs individual). When veterans did not perceive choice in treatment aspects important to them (typically despite having choices), they were less satisfied with their TF-EBP experience.

A final area of process improvement identified involves how therapists address important aspects of culture. Veterans often described mental health stigma coming from intersecting cultural identities and expressed appreciation when therapists helped them recognize the impact of these beliefs on treatment. Some veterans did not discuss important aspects of their identity with clinicians, including race/ethnicity, religion, and military culture. Veterans did not report negative interactions with clinicians or experiences suggesting it was inappropriate to discuss identity; however, they were reluctant to independently raise these identity factors. Strategies such as the ADDRESSING framework, a mnemonic acronym that describes a series of potentially relevant characteristics, can help clinicians comprehensively consider different aspects that may be relevant to veterans, modeling that discussion of relevant these characteristics is welcome in TF-EBP.29 Veterans reported that making culturally relevant connections enhanced the TF-EBP experience, most commonly with military culture. These data support that TF-EBP delivery with attention to culture should be an integrated part of treatment, supporting engagement and therapeutic alliance.30 The VA National Center for PTSD consultation program is a resource to support clinicians in assessing and incorporating relevant aspects of cultural identity.31 For example, the National Center for PTSD provides a guide for using case conceptualization to address patient reactions to race-based violence during PTSD treatment.32 Both manualized design and therapist certification training can reinforce that assessing and attending to case conceptualization (including identity factors) is an integral component of TF-EBP.33,34

Limitations

While the current study has numerous strengths (eg, national veteran sampling, robust qualitative methods), results should be considered within the context of study limitations. First, veteran participants all received TF-EBP, and the perspectives of veterans who never initiate TF-EBP may differ. Despite the strong sampling approach, the study design is not intended to be generalizable to all veterans receiving TF-EBP for PTSD. Qualitative analysis yielded 15 themes, described in this study and prior research, consistent with recommendations.21,22 This approach allows rich description of distinct focus areas that would not be possible in a single manuscript. Nonetheless, all veterans interviewed described their experiences in TF-EBP and general mental health care, the focus of the semistructured interview guide was on the experience of transitioning from other treatment to TF-EBP.

Conclusion

This study describes themes related to general mental health and TF-EBP process improvement as part of a larger study on transitions in PTSD care.21,22 Veterans valued the fundamentals of therapy, including rapport and flexibility. Treatment-specific rapport (eg, pointing out treatment progress and effort in completing treatment components) and flexibility within the context of fidelity (ie, personalizing treatment while maintaining core treatment elements) may be most effective at engaging veterans in recommended PTSD treatments.18,34 In addition to successes, themes suggest multiple opportunities for process improvement. Ongoing VA initiatives and priorities (ie, BHIP, shared decision-making, consultation services) aim to improve processes consistent with veteran recommendations. Future research is needed to evaluate the success of these and other programs to optimize access to and engagement in recommended PTSD treatments.

Trauma-focused evidence-based psychotherapies (TF-EBPs), including cognitive processing therapy (CPT) and prolonged exposure therapy (PE), are recommended treatments for posttraumatic stress disorder (PTSD) in clinical practice guidelines.1-3 To increase initiation of these treatments, the US Department of Veterans Affairs (VA) used a large-scale dissemination and implementation effort to improve access to TF-EBP.4,5 These efforts achieved modest success, increasing prevalence of TF-EBP from a handful of veterans in 2004 to an annual prevalence of 14.6% for CPT and 4.3% for PE in 2014.6

Throughout these efforts, qualitative studies have been used to better understand veterans’ perspectives on receiving TF-EBP care.7-18 Barriers to initiation of and engagement in TF-EBP and PTSD care have been identified from these qualitative studies. One identified barrier was lack of knowledge—particularly lack of knowledge about what is meant by a PTSD diagnosis and available treatments.7-10 Stigma (ie, automatic negative associations) toward mental health problems or seeking mental health care also has been identified as a barrier to initiation.7,10-14 Perceptions of poor alignment between treatment and veteran goals, including lack of buy-in for the rationale, served as barriers to initiation and engagement.8,15-18

Using prior qualitative work, numerous initiatives have been developed to reduce stigma, facilitate conversations about how treatment aligns with goals, and fill knowledge gaps, particularly through online resources and shared decision-making.19,20 To better inform the state of veterans’ experiences with TF-EBP, a qualitative investigation was conducted involving veterans who recently initiated TF-EBP. Themes directly related to transitions to TF-EBP were identified; however, all veterans interviewed also described their experiences with TFEBP engagement and mental health care. Consistent with recommendations for qualitative methods, this study extends prior work on transitions to TF-EBP by describing themes with a distinct focus on the experience of engaging with TF-EBP and mental health care.21,22

Methods

The experiences of veterans who were transitioning into TF-EBPs were collected in semistructured interviews and analyzed. The semistructured interview guide was developed and refined in consultation with both qualitative methods experts and PTSD treatment experts to ensure that 6 content domains were appropriately queried: PTSD treatment options, cultural sensitivity of treatment, PTSD treatment selection, transition criteria, beliefs about stabilization treatment, and treatment needs/preferences.

Participants were identified using the VA Corporate Data Warehouse and included post-9/11 veterans who had recently initiated CPT or PE for the first time between September 1, 2021, and September 1, 2022. More details of participant selection are available in Holder et al.21 From a population of 10,814 patients, stratified random sampling generated a recruitment pool of 200 veterans for further outreach. The strata were defined such that this recruitment pool had similar proportions of demographic characteristics (ie, gender, race, ethnicity) to the population of eligible veterans, equivalent distributions of time to CPT or PE initiation (ie, 33.3% < 1 year, 33.3% 1-3 years, and 33.3% > 3 years), and adequate variability in TF-EBP type (ie, 66.7% CPT, 33.3% PE). A manual chart review in the recruitment pool excluded 12 veterans who did not initiate CPT or PE, 1 veteran with evidence of current active psychosis and/or cognitive impairment that would likely preclude comprehension of study materials, and 1 who was deceased.

Eligible veterans from the recruitment pool were contacted in groups of 25. First, a recruitment letter with study information and instructions to opt-out of further contact was mailed or emailed to veterans. After 2 weeks, veterans who had not responded were contacted by phone up to 3 times. Veterans interested in participating were scheduled for a 1-time visit that included verbal consent and the qualitative interview. Metrics were established a priori to ensure an adequately diverse and inclusive sample. Specifically, a minimum number of racial and/or ethnic minority veterans (33%) and women veterans (20%) were sought. Equal distribution across the 3 categories of time from first mental health visit to CPT/PE initiation also was targeted. Throughout enrollment, recruitment efforts were adapted to meet these metrics in the emerging sample. While the goal was to generate a diverse and inclusive sample using these methods, the sample was not intended to be representative of the population.

Of the 186 eligible participants, 21 declined participation and 26 could not be reached. The targeted sample was reached after exhausting contact for 47 veterans and contacting 80 veterans for a final response rate of 40% among fully contacted veterans and 27% among veterans with any contact. The final sample included 30 veterans who received CPT or PE in VA facilities (Table).

1025FDED-ePTSD-T1

After veterans provided verbal consent for study participation, sociodemographic information was verbally reported, and a 30- to 60-minute semistructured qualitative phone interview was recorded and transcribed. Veterans received $40 for participation. All procedures were approved by the University of California San Francisco Institutional Review Board.

Qualitative Data Analysis

Rapid analysis procedures were used to analyze qualitative data. This approach is suitable for focused, moderately structured qualitative analyses in health services research and facilitates rapid dissemination to stakeholders.23 The qualitative analysts were 2 clinical psychologists with expertise in PTSD treatment (NH primary and RR secondary). Consistent with rapid analysis procedures, analysts prepared a templated summary (including relevant quotations) of each interview, organized by the prespecified content domains. Interviews were summarized independently, compared to ensure consistency, and discrepancies were resolved through review of the interview source materials. Individual summary templates were combined into a master analytic matrix to facilitate the identification of patterns and delineation of themes. Analysts routinely met to identify, discuss, and refine preliminary themes, revisiting source materials to reach consensus as needed.

Results

Fifteen themes were identified and organized into 2 distinct focus areas: themes directly related to the transition to TF-EBP (8 themes) and themes related to veterans’ experiences with TF-EBP and general mental health care with potential process-improvement implications (7 themes).21 Seven themes were identified related to experiences with TF-EBP engagement and VA mental health care. The 7 themes related to TF-EBP engagement and VA mental health care themes are summarized with exemplary quotations.

Veterans want a better understanding of psychotherapy and engaging with VA mental health. Veterans reported that they generally had a poor or “nebulous” understanding about the experience of psychotherapy. For example, veterans exhibited confusion about whether certain experiences were equivalent to participating in psychotherapy. They were sometimes unable to distinguish between interactions such as assessment, disability evaluations, peer support, and psychotherapy. One veteran described a conversation with a TFEBP therapist about prior treatment:

She [asked], have you ever been, or gone through a therapy to begin with? And I, I said, well I just chatted with somebody. And she said that’s not, that’s not therapy. So, I was like, oh, it’s not? That’s not what people do?

Veterans were surprised the VA offered a diverse range of psychotherapy interventions, rather than simply therapy. They did not realize there were different types of psychotherapy. As a result, veterans were not aware that some VA mental practitioners have specialty training and certification to provide treatment matched to specific diagnoses or needs. They thought that all clinicians could provide the same care. One veteran described their understanding:

I just figured all mental health people are mental health people. I didn’t have a better understanding of the system and all the different levels and how it plays out and specialties and things like that. Which, I guess, I should have because you have a primary care doctor, but then you have specialists in all these other different sectors that specialize in one particular area. I guess that should’ve been common sense, but it wasn’t.

Stigma was a barrier to seeking and engaging in mental health care. Veterans discovered they had to overcome stigma associated with seeking and engaging in mental health treatment. Military culture was often discussed as promoting stigma regarding mental health treatment. Specifically, veterans described that seeking treatment meant “either, I’m weak or I’m gonna be seen as weak.” In active-duty settings, the strategy for dealing with mental health symptoms was to “leave those feelings, you push ‘em aside,” an approach highly inconsistent with TF-EBP. In some cases, incorrect information about the VA and PTSD was presented as part of discharge from the military, leading to long-term skepticism of the VA and PTSD treatment. One veteran described his experience as part of a class on the VA compensation and pension assessment process for service-connected disabilities during his military discharge:

[A fellow discharging soldier asked] what about like PTSD, gettin’ rated for PTSD. I hear they take our weapons and stuff like we can’t own firearms and all that stuff. And [the instructor] was like, well, yes that’s a thing. He didn’t explain it like if you get compensated for PTSD you don’t lose your rights to carry a firearm or to have, to be able to go hunting.

Importantly, veterans often described how other identities (eg, race, ethnicity, gender, region of origin) interacted with military culture to enhance stigma. Hearing messaging from multiple sources reinforced beliefs that mental health treatment is inappropriate or is associated with weakness:

As a first-generation Italian, I was always taught keep your feelings to yourself. Never talk outside your family. Never bring up problems to other people and stuff like that. Same with the military. And then the old stigma working in [emergency medical services] and public safety, you’re weak if you get help.

The fundamentals of therapy, including rapport and flexibility, were important. Veterans valued nonspecific therapy factors, genuine empathy, building trust, being honest about treatment, personality, and rapport. These characteristics were almost universally described as particularly important:

I liked the fact that she made it personable and she cared. It wasn’t just like, here, we’re gonna start this. She explained it in the ways I could understand, not in medical terms, so to speak, but that’s what I liked about her. She really cared about what she did and helping me.

Flexibility was viewed as an asset, particularly when clinicians acknowledged veteran autonomy. A consistent example was when veterans were able to titrate trauma disclosure. One veteran described this flexible treatment experience: “She was right there in the room, she said, you know, at any time, you know, we could stop, we could debrief.”

Experiences of clinician flexibility and personalization of therapy were contrasted with experiences of overly rigid therapy. Overemphasis on protocols created barriers, often because treatment did not feel personalized. One veteran described how a clinician’s task-oriented approach interfered with their ability to engage in TF-EBP:

They listened, but it just didn’t seem like they were listening, because they really wanted to stay on task… So, I felt like if the person was more concerned, or more sympathetic to the things that was also going on in my life at that present time, I think I would’ve felt more comfortable talking about what was the PTSD part, too.

Veterans valued shared decision-making prior to TF-EBP initiation. Veterans typically described being involved in a shared decision-making process prior to initiating TF-EBP. During these sessions, clinicians discussed treatment options and provided veterans with a variety of materials describing treatments (eg, pamphlets, websites, videos, statistics). Most veterans appreciated being able to reflect on and discuss treatment options with their clinicians. Being given time in and out of session to review was viewed as valuable and increased confidence in treatment choice. One veteran described their experience:

I was given the information, you know, they gave me handouts, PDFs, whatever was available, and let me read over it. I didn’t have to choose anything right then and there, you know, they let me sleep on it. And I got back to them after some thought.

However, some veterans felt overwhelmed by being presented with too much information and did not believe they knew enough to make a final treatment decision. One veteran described being asked to contribute to the treatment decision:

I definitely asked [the clinician] to weigh in on maybe what he thought was best, because—I mean, I don’t know… I’m not necessarily sure I know what I think is best. I think we’re just lucky I’m here, so if you can give me a solid and help me out here by telling me just based on what I’ve said to you and the things that I’ve gone through, what do you think?

Veterans who perceived that their treatment preferences were respected had a positive outlook on TF-EBP. As part of the shared-decision making process, veterans typically described being given choices among PTSD treatments. One way that preferences were respected was through clinicians tailoring treatment descriptions to a veteran’s unique symptoms, experiences, and values. In these cases, clinicians observed specific concerns and clearly linked treatment principles to those concerns. For example, one veteran described their clinician’s recommendation for PE: “The hardest thing for me is to do the normal things like grocery store or getting on a train or anything like that. And so, he suggested that [PE] would be a good idea.”

In other cases, veterans wanted the highest quality of treatment rather than a match between treatment principles and the veteran’s presentation, goals, or strengths. These veterans wanted the best treatment available for PTSD and valued research support, recommendations from clinical practice guidelines, or clinician confidence in the effectiveness of the treatment. One veteran described this perspective:

I just wanted to be able to really tackle it in the best way possible and in the most like aggressive way possible. And it seemed like PE really was going to, they said that it’s a difficult type of therapy, but I really just wanted to kind of do the best that I could to eradicate some of the issues that I was having.

When veterans perceived a lack of respect for their preferences, they were hesitant about TF-EBP. For some veterans, a generic pitch for a TF-EBP was detrimental in the absence of the personal connection between the treatment and their own symptoms, goals, or strengths. These veterans did not question whether the treatment was effective in general but did question whether the treatment was best for them. One veteran described the contrast between their clinician’s perspective and their own.

I felt like they felt very comfortable, very confident in [CPT] being the program, because it was comfortable for them. Because they did it several times. And maybe they had a lot of success with other individuals... but they were very comfortable with that one, as a provider, more than: Is this the best fit for [me]?

Some veterans perceived little concern for their preferences and a lack of choice in available treatments, which tended to perpetuate negative perceptions of TFEBP. These veterans described their lack of choices with frustration. Alternatives to TFEBP were described by these veterans as so undesirable that they did not believe they had a real choice:

[CPT] was the only decision they had. There was nothing else for PTSD. They didn’t offer anything else. So, I mean it wasn’t a decision. It was either … take treatment or don’t take treatment at all… Actually, I need to correct myself. So, there were 2 options, group therapy or CPT. I forgot about that. I’m not a big group guy so I chose the CPT.

Another veteran was offered a choice between therapeutic approaches, but all were delivered via telehealth (consistent with the transition to virtual services during the COVID-19 pandemic). For this veteran, not only was the distinction between approaches unclear, but the choice between approaches was unimportant compared to the mode of delivery.

This happened during COVID-19 and VA stopped seeing anybody physically, face-to-face. So my only option for therapy was [telehealth]… There was like 3 of them, and I tried to figure out, you know, from the layperson’s perspective, like: I don’t know which one to go with.

Veterans wanted to be asked about their cultural identity. Veterans valued when clinicians asked questions about cultural identity as part of their mental health treatment and listened to their cultural context. Cultural identity factors extended beyond factors such as race, ethnicity, gender, and sexual orientation to religion, military culture, and regionality. Veterans often described situations where they wished clinicians would ask the question or initiate conversations about culture. A veteran highlighted the importance of their faith but noted that it was a taboo topic. Their clinician did not say “we don’t go there,” but they “never dove into it either.” Another veteran expressed a desire for their clinician to ask questions about experiences in the National Guard and as an African American veteran:

If a provider was to say like: Oh, you know, it’s a stressful situation being a part of the military, being in the National Guard. You know, just asking questions about that. I think that would really go a long way… Being African American was difficult as well. And more so because of my region, I think… I felt like it would probably be an uncomfortable subject to speak on… I mean, it wasn’t anything that my providers necessarily did, it was more so just because it wasn’t brought up.

One common area of concern for veterans was a match between veteran and therapist demographics. When asked about how their cultural identity influenced treatment, several veterans described the relevance of therapist match. Much like questions about their own cultural identity, veterans valued being asked about identity preferences in clinicians (eg, gender or race matching), rather than having to bring up the preference themselves. One veteran described relief at this question being asked directly: “I was relieved when she had asked [whether I wanted a male or female clinician] primarily because I was going to ask that or bring that up somehow. But her asking that before me was a weight off my shoulders.”

Discussing cultural identity through treatment strengthened veterans’ engagement in therapy. Many veterans appreciated when analogies used in therapy were relevant to their cultural experiences and when clinicians understood their culture (eg, military culture, race, ethnicity, religious beliefs, sexual orientation). One veteran described how their clinician understood military culture and made connections between military culture and the rationale for TF-EBP, which strengthened the veteran’s buy-in for the treatment and alliance with the clinician:

At the beginning when she was explaining PTSD, and I remember she said that your brain needed to think this way when you were in the military because it was a way of protecting and surviving, so your brain was doing that in order for you to survive in whatever areas you were because there was danger. So, your brain had you thinking that way. But now, you’re not in those situations anymore. You’re not in danger. You’re not in the military, but your brain is still thinking you are, and that’s what PTSD generally does to you.

Specific elements of TF-EBP also provided opportunities to discuss and integrate important aspects of identity. This is accomplished in PE by assigning relevant in vivo exercises. In CPT, “connecting the dots” on how prior experiences influenced trauma-related stuck points achieved this element. One veteran described their experience with a clinician who was comfortable discussing the veteran’s sexual orientation and recognized the impacts of prior trauma on intimacy:

They’re very different, and there’s a lot of things that can be accepted in gay relationships that are not in straight ones. With all that said, I think [the PE therapist] did a fantastic job being not—like never once did she laugh or make an uncomfortable comment or say she didn’t wanna talk about something when like part of the reason I wanted to get into therapy is that my partner and I weren’t having sex unless I used alcohol.

Discussion

As part of a larger national qualitative investigation of the experiences of veterans who recently initiated TF-EBP, veterans discussed their experiences with therapy and mental health care that have important implications for continued process improvement.21 Three key areas for continued process improvement were identified: (1) providing information about the diverse range of mental health care services at the VA and the implications of this continuum of care; (2) consideration of veteran preferences in treatment decision-making, including the importance of perceived choice; and (3) incorporating cultural assessment and cultural responsiveness into case conceptualization and treatment.

One area of process improvement identified was increasing knowledge about different types of psychotherapy and the continuum of care available at the VA. Veterans in this study confused or conflated participating in psychotherapy with talking about mental health symptoms with a clinician (eg, assessment, disability evaluation). They were sometimes surprised that psychotherapy is an umbrella term referring to a variety of different modalities. The downstream impact of these misunderstandings was a perception of VA mental health care as nebulous. Veterans were surprised that all mental health practitioners were unable to provide the same care. Confusion may have been compounded by highly variable referral processes across VA.24 To address this, clinicians have developed local educational resources and handouts for both veterans and referring clinicians from nonmental health and general mental health specialties.25 Given the variability in referral processes both between and within VA medical centers, national dissemination of these educational materials may be more difficult compared to materials for TF-EBPs.24 The VA started to use behavioral health interdisciplinary program (BHIP) teams, which are designed to be clinical homes for veterans connected with a central clinician who can explain and coordinate their mental health care as well as bring more consistency to the referral process.26 The ongoing transition toward the BHIP model of mental health care at VA may provide the opportunity to consolidate and integrate knowledge about the VA approach to mental health care, potentially filling knowledge gaps.

A second area of process improvement focused on the shared decision-making process. Consistent with mental health initiatives, veterans generally believed they had received sufficient information about TF-EBP and engaged in shared decision-making with clinicians.20,27 Veterans were given educational materials to review and had the opportunity to discuss these materials with clinicians. However, veterans described variability in the success of shared decision-making. Although veterans valued receiving accurate, comprehensible information to support treatment decisions, some preferred to defer to clinicians’ expertise regarding which treatment to pursue. While these veterans valued information, they also valued the expertise of clinicians in explaining why specific treatments would be beneficial. A key contributor to veterans satisfaction was assessing how veterans wanted to engage in the decision-making process and respecting those preferences.28 Veterans approached shared decision-making differently, from making decisions independently after receiving information to relying solely on clinician recommendation. The process was most successful when clinicians articulated how their recommended treatment aligned with a veteran’s preferences, including recommendations based on specific values (eg, personalized match vs being the best). Another important consideration is ensuring veterans know they can receive a variety of different types of mental health services available in different modalities (eg, virtual vs in-person; group vs individual). When veterans did not perceive choice in treatment aspects important to them (typically despite having choices), they were less satisfied with their TF-EBP experience.

A final area of process improvement identified involves how therapists address important aspects of culture. Veterans often described mental health stigma coming from intersecting cultural identities and expressed appreciation when therapists helped them recognize the impact of these beliefs on treatment. Some veterans did not discuss important aspects of their identity with clinicians, including race/ethnicity, religion, and military culture. Veterans did not report negative interactions with clinicians or experiences suggesting it was inappropriate to discuss identity; however, they were reluctant to independently raise these identity factors. Strategies such as the ADDRESSING framework, a mnemonic acronym that describes a series of potentially relevant characteristics, can help clinicians comprehensively consider different aspects that may be relevant to veterans, modeling that discussion of relevant these characteristics is welcome in TF-EBP.29 Veterans reported that making culturally relevant connections enhanced the TF-EBP experience, most commonly with military culture. These data support that TF-EBP delivery with attention to culture should be an integrated part of treatment, supporting engagement and therapeutic alliance.30 The VA National Center for PTSD consultation program is a resource to support clinicians in assessing and incorporating relevant aspects of cultural identity.31 For example, the National Center for PTSD provides a guide for using case conceptualization to address patient reactions to race-based violence during PTSD treatment.32 Both manualized design and therapist certification training can reinforce that assessing and attending to case conceptualization (including identity factors) is an integral component of TF-EBP.33,34

Limitations

While the current study has numerous strengths (eg, national veteran sampling, robust qualitative methods), results should be considered within the context of study limitations. First, veteran participants all received TF-EBP, and the perspectives of veterans who never initiate TF-EBP may differ. Despite the strong sampling approach, the study design is not intended to be generalizable to all veterans receiving TF-EBP for PTSD. Qualitative analysis yielded 15 themes, described in this study and prior research, consistent with recommendations.21,22 This approach allows rich description of distinct focus areas that would not be possible in a single manuscript. Nonetheless, all veterans interviewed described their experiences in TF-EBP and general mental health care, the focus of the semistructured interview guide was on the experience of transitioning from other treatment to TF-EBP.

Conclusion

This study describes themes related to general mental health and TF-EBP process improvement as part of a larger study on transitions in PTSD care.21,22 Veterans valued the fundamentals of therapy, including rapport and flexibility. Treatment-specific rapport (eg, pointing out treatment progress and effort in completing treatment components) and flexibility within the context of fidelity (ie, personalizing treatment while maintaining core treatment elements) may be most effective at engaging veterans in recommended PTSD treatments.18,34 In addition to successes, themes suggest multiple opportunities for process improvement. Ongoing VA initiatives and priorities (ie, BHIP, shared decision-making, consultation services) aim to improve processes consistent with veteran recommendations. Future research is needed to evaluate the success of these and other programs to optimize access to and engagement in recommended PTSD treatments.

References
  1. US Department of Veterans Affairs; US Department of Defense. VA/DoD clinical practice guideline for the management of posttraumatic stress disorder and acute stress disorder. 2023. Updated August 20, 2025. Accessed October 17, 2025. https://www.healthquality.va.gov/guidelines/MH/ptsd/
  2. International Society for Traumatic Stress Studies. ISTSS PTSD prevention and treatment guidelines: methodology and recommendations. Accessed August 13, 2025. http://www.istss.org/getattachment/Treating-Trauma/New-ISTSS-Prevention-and-TreatmentGuidelines/ISTSS_PreventionTreatmentGuidelines_FNL-March-19-2019.pdf.aspx
  3. American Psychological Association. Clinical practice guideline for the treatment of posttraumatic stress disorder in adults. Accessed August 13, 2025. https://www.apa.org/ptsd-guideline/ptsd.pdf
  4. Karlin BE, Cross G. From the laboratory to the therapy room: National dissemination and implementation of evidence- based psychotherapies in the U.S. Department of Veterans Affairs Health Care System. Am Psychol. 2014;69:19-33. doi:10.1037/a0033888
  5. Rosen CS, Matthieu MM, Wiltsey Stirman S, et al. A review of studies on the system-wide implementation of evidencebased psychotherapies for posttraumatic stress disorder in the Veterans Health Administration. Adm Policy Ment Health. 2016;43:957-977. doi:10.1007/s10488-016-0755-0
  6. Maguen S, Holder N, Madden E, et al. Evidence-based psychotherapy trends among posttraumatic stress disorder patients in a national healthcare system, 2001-2014. Depress Anxiety. 2020;37:356-364. doi:10.1002/da.22983
  7. Cheney AM, Koenig CJ, Miller CJ, et al. Veteran-centered barriers to VA mental healthcare services use. BMC Health Serv Res. 2018;18:591. doi:10.1186/s12913-018-3346-9
  8. Hundt NE, Mott JM, Miles SR, et al. Veterans’ perspectives on initiating evidence-based psychotherapy for posttraumatic stress disorder. Psychol Trauma. 2015;7:539-546. doi:10.1037/tra0000035
  9. Hundt NE, Helm A, Smith TL, et al. Failure to engage: a qualitative study of veterans who decline evidence-based psychotherapies for PTSD. Psychol Serv. 2018;15:536- 542. doi:10.1037/ser0000212
  10. Sayer NA, Friedemann-Sanchez G, Spoont M, et al. A qualitative study of determinants of PTSD treatment initiation in veterans. Psychiatry. 2009;72:238-255. doi:10.1521/psyc.2009.72.3.238
  11. Mittal D, Drummond KL, Blevins D, et al. Stigma associated with PTSD: perceptions of treatment seeking combat veterans. Psychiatr Rehabil J. 2013;36:86-92. doi:10.1037/h0094976
  12. Possemato K, Wray LO, Johnson E, et al. Facilitators and barriers to seeking mental health care among primary care veterans with posttraumatic stress disorder. J Trauma Stress. 2018;31:742-752. doi:10.1002/jts.22327
  13. Silvestrini M, Chen JA. “It’s a sign of weakness”: Masculinity and help-seeking behaviors among male veterans accessing posttraumatic stress disorder care. Psychol Trauma. 2023;15:665-671. doi:10.1037/tra0001382
  14. Stecker T, Shiner B, Watts BV, et al. Treatment-seeking barriers for veterans of the Iraq and Afghanistan conflicts who screen positive for PTSD. Psychiatr Serv. 2013;64:280-283. doi:10.1176/appi.ps.001372012
  15. Etingen B, Grubbs KM, Harik JM. Drivers of preference for evidence-based PTSD treatment: a qualitative assessment. Mil Med. 2020;185:303-310. doi:10.1093/milmed/usz220
  16. Hundt NE, Ecker AH, Thompson K, et al. “It didn’t fit for me:” A qualitative examination of dropout from prolonged exposure and cognitive processing therapy in veterans. Psychol Serv. 2020;17:414-421. doi:10.1037/ser0000316
  17. Kehle-Forbes SM, Gerould H, Polusny MA, et al. “It leaves me very skeptical” messaging in marketing prolonged exposure and cognitive processing therapy to veterans with PTSD. Psychol Trauma. 2022;14:849-852. doi:10.1037/tra0000550
  18. Kehle-Forbes SM, Ackland PE, Spoont MR, et al. Divergent experiences of U.S. veterans who did and did not complete trauma-focused therapies for PTSD: a national qualitative study of treatment dropout. Behav Res Ther. 2022;154:104123. doi:10.1016/j.brat.2022.104123
  19. Hessinger JD, London MJ, Baer SM. Evaluation of a shared decision-making intervention on the utilization of evidence-based psychotherapy in a VA outpatient PTSD clinic. Psychol Serv. 2018;15:437-441. doi:10.1037/ser0000141
  20. Hamblen JL, Grubbs KM, Cole B, et al. “Will it work for me?” Developing patient-friendly graphical displays of posttraumatic stress disorder treatment effectiveness. J Trauma Stress. 2022;35:999-1010. doi:10.1002/jts.22808
  21. Holder N, Ranney RM, Delgado AK, et al. Transitioning into trauma-focused evidence-based psychotherapy for posttraumatic stress disorder from other treatments: a qualitative investigation. Cogn Behav Ther. 2025;54:391-407. doi:10.1080/16506073.2024.2408386
  22. Levitt HM, Bamberg M, Creswell JW, et al. Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA Publications and Communications Board task force report. Am Psychol. 2018;73:26-46. doi:10.1037/amp0000151
  23. Palinkas LA, Mendon SJ, Hamilton AB. Innovations in mixed methods evaluations. Annu Rev Public Health. 2019;40:423- 442. doi:10.1146/annurev-publhealth-040218-044215
  24. Ranney RM, Cordova MJ, Maguen S. A review of the referral process for evidence-based psychotherapies for PTSD among veterans. Prof Psychol Res Pr. 2022;53:276-285. doi:10.1037/pro0000463
  25. Holder N, Ranney RM, Delgado AK, et al. Transitions to trauma-focused evidence-based psychotherapy for posttraumatic stress disorder from other treatment: a qualitative investigation of clinician’s perspectives. Cogn Behav Ther. 2025;1-19. doi:10.1080/16506073.2025.2481475
  26. Barry CN, Abraham KM, Weaver KR, et al. Innovating team-based outpatient mental health care in the Veterans Health Administration: staff-perceived benefits and challenges to pilot implementation of the Behavioral Health Interdisciplinary Program (BHIP). Psychol Serv. 2016;13:148-155. doi:10.1037/ser0000072
  27. Harik JM, Hundt NE, Bernardy NC, et al. Desired involvement in treatment decisions among adults with PTSD symptoms. J Trauma Stress. 2016;29:221-228. doi:10.1002/jts.22102
  28. Larsen SE, Hooyer K, Kehle-Forbes SM, et al. Patient experiences in making PTSD treatment decisions. Psychol Serv. 2024;21:529-537. doi:10.1037/ser0000817
  29. Hays PA. Four steps toward intersectionality in psychotherapy using the ADDRESSING framework. Prof Psychol Res Pr. 2024;55:454-462. doi:10.1037/pro0000577
  30. Galovski TE, Nixon RDV, Kaysen D. Flexible Applications of Cognitive Processing Therapy: Evidence-Based Treatment Methods. Academic Press; 2020.
  31. Larsen SE, McKee T, Fielstein E, et al. The development of a posttraumatic stress disorder (PTSD) consultation program to support system-wide implementation of high-quality PTSD care for veterans. Psychol Serv. 2025;22:342-348. doi:10.1037/ser0000867
  32. Galovski T, Kaysen D, McClendon J, et al. Provider guide to addressing patient reactions to race-based violence during PTSD treatment. PTSD.va.gov. Accessed August 3, 2025. www.ptsd.va.gov/professional/treat/specific/patient_reactions_race_violence.asp
  33. Galovski TE, Nixon RDV, Kehle-Forbes S. Walking the line between fidelity and flexibility: a conceptual review of personalized approaches to manualized treatments for posttraumatic stress disorder. J Trauma Stress. 2024;37:768-774. doi:10.1002/jts.23073
  34. Galovski TE, McSweeney LB, Nixon RDV, et al. Personalizing cognitive processing therapy with a case formulation approach to intentionally target impairment in psychosocial functioning associated with PTSD. Contemp Clin Trials Commun. 2024;42:101385. doi:10.1016/j.conctc.2024.101385
References
  1. US Department of Veterans Affairs; US Department of Defense. VA/DoD clinical practice guideline for the management of posttraumatic stress disorder and acute stress disorder. 2023. Updated August 20, 2025. Accessed October 17, 2025. https://www.healthquality.va.gov/guidelines/MH/ptsd/
  2. International Society for Traumatic Stress Studies. ISTSS PTSD prevention and treatment guidelines: methodology and recommendations. Accessed August 13, 2025. http://www.istss.org/getattachment/Treating-Trauma/New-ISTSS-Prevention-and-TreatmentGuidelines/ISTSS_PreventionTreatmentGuidelines_FNL-March-19-2019.pdf.aspx
  3. American Psychological Association. Clinical practice guideline for the treatment of posttraumatic stress disorder in adults. Accessed August 13, 2025. https://www.apa.org/ptsd-guideline/ptsd.pdf
  4. Karlin BE, Cross G. From the laboratory to the therapy room: National dissemination and implementation of evidence- based psychotherapies in the U.S. Department of Veterans Affairs Health Care System. Am Psychol. 2014;69:19-33. doi:10.1037/a0033888
  5. Rosen CS, Matthieu MM, Wiltsey Stirman S, et al. A review of studies on the system-wide implementation of evidencebased psychotherapies for posttraumatic stress disorder in the Veterans Health Administration. Adm Policy Ment Health. 2016;43:957-977. doi:10.1007/s10488-016-0755-0
  6. Maguen S, Holder N, Madden E, et al. Evidence-based psychotherapy trends among posttraumatic stress disorder patients in a national healthcare system, 2001-2014. Depress Anxiety. 2020;37:356-364. doi:10.1002/da.22983
  7. Cheney AM, Koenig CJ, Miller CJ, et al. Veteran-centered barriers to VA mental healthcare services use. BMC Health Serv Res. 2018;18:591. doi:10.1186/s12913-018-3346-9
  8. Hundt NE, Mott JM, Miles SR, et al. Veterans’ perspectives on initiating evidence-based psychotherapy for posttraumatic stress disorder. Psychol Trauma. 2015;7:539-546. doi:10.1037/tra0000035
  9. Hundt NE, Helm A, Smith TL, et al. Failure to engage: a qualitative study of veterans who decline evidence-based psychotherapies for PTSD. Psychol Serv. 2018;15:536- 542. doi:10.1037/ser0000212
  10. Sayer NA, Friedemann-Sanchez G, Spoont M, et al. A qualitative study of determinants of PTSD treatment initiation in veterans. Psychiatry. 2009;72:238-255. doi:10.1521/psyc.2009.72.3.238
  11. Mittal D, Drummond KL, Blevins D, et al. Stigma associated with PTSD: perceptions of treatment seeking combat veterans. Psychiatr Rehabil J. 2013;36:86-92. doi:10.1037/h0094976
  12. Possemato K, Wray LO, Johnson E, et al. Facilitators and barriers to seeking mental health care among primary care veterans with posttraumatic stress disorder. J Trauma Stress. 2018;31:742-752. doi:10.1002/jts.22327
  13. Silvestrini M, Chen JA. “It’s a sign of weakness”: Masculinity and help-seeking behaviors among male veterans accessing posttraumatic stress disorder care. Psychol Trauma. 2023;15:665-671. doi:10.1037/tra0001382
  14. Stecker T, Shiner B, Watts BV, et al. Treatment-seeking barriers for veterans of the Iraq and Afghanistan conflicts who screen positive for PTSD. Psychiatr Serv. 2013;64:280-283. doi:10.1176/appi.ps.001372012
  15. Etingen B, Grubbs KM, Harik JM. Drivers of preference for evidence-based PTSD treatment: a qualitative assessment. Mil Med. 2020;185:303-310. doi:10.1093/milmed/usz220
  16. Hundt NE, Ecker AH, Thompson K, et al. “It didn’t fit for me:” A qualitative examination of dropout from prolonged exposure and cognitive processing therapy in veterans. Psychol Serv. 2020;17:414-421. doi:10.1037/ser0000316
  17. Kehle-Forbes SM, Gerould H, Polusny MA, et al. “It leaves me very skeptical” messaging in marketing prolonged exposure and cognitive processing therapy to veterans with PTSD. Psychol Trauma. 2022;14:849-852. doi:10.1037/tra0000550
  18. Kehle-Forbes SM, Ackland PE, Spoont MR, et al. Divergent experiences of U.S. veterans who did and did not complete trauma-focused therapies for PTSD: a national qualitative study of treatment dropout. Behav Res Ther. 2022;154:104123. doi:10.1016/j.brat.2022.104123
  19. Hessinger JD, London MJ, Baer SM. Evaluation of a shared decision-making intervention on the utilization of evidence-based psychotherapy in a VA outpatient PTSD clinic. Psychol Serv. 2018;15:437-441. doi:10.1037/ser0000141
  20. Hamblen JL, Grubbs KM, Cole B, et al. “Will it work for me?” Developing patient-friendly graphical displays of posttraumatic stress disorder treatment effectiveness. J Trauma Stress. 2022;35:999-1010. doi:10.1002/jts.22808
  21. Holder N, Ranney RM, Delgado AK, et al. Transitioning into trauma-focused evidence-based psychotherapy for posttraumatic stress disorder from other treatments: a qualitative investigation. Cogn Behav Ther. 2025;54:391-407. doi:10.1080/16506073.2024.2408386
  22. Levitt HM, Bamberg M, Creswell JW, et al. Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA Publications and Communications Board task force report. Am Psychol. 2018;73:26-46. doi:10.1037/amp0000151
  23. Palinkas LA, Mendon SJ, Hamilton AB. Innovations in mixed methods evaluations. Annu Rev Public Health. 2019;40:423- 442. doi:10.1146/annurev-publhealth-040218-044215
  24. Ranney RM, Cordova MJ, Maguen S. A review of the referral process for evidence-based psychotherapies for PTSD among veterans. Prof Psychol Res Pr. 2022;53:276-285. doi:10.1037/pro0000463
  25. Holder N, Ranney RM, Delgado AK, et al. Transitions to trauma-focused evidence-based psychotherapy for posttraumatic stress disorder from other treatment: a qualitative investigation of clinician’s perspectives. Cogn Behav Ther. 2025;1-19. doi:10.1080/16506073.2025.2481475
  26. Barry CN, Abraham KM, Weaver KR, et al. Innovating team-based outpatient mental health care in the Veterans Health Administration: staff-perceived benefits and challenges to pilot implementation of the Behavioral Health Interdisciplinary Program (BHIP). Psychol Serv. 2016;13:148-155. doi:10.1037/ser0000072
  27. Harik JM, Hundt NE, Bernardy NC, et al. Desired involvement in treatment decisions among adults with PTSD symptoms. J Trauma Stress. 2016;29:221-228. doi:10.1002/jts.22102
  28. Larsen SE, Hooyer K, Kehle-Forbes SM, et al. Patient experiences in making PTSD treatment decisions. Psychol Serv. 2024;21:529-537. doi:10.1037/ser0000817
  29. Hays PA. Four steps toward intersectionality in psychotherapy using the ADDRESSING framework. Prof Psychol Res Pr. 2024;55:454-462. doi:10.1037/pro0000577
  30. Galovski TE, Nixon RDV, Kaysen D. Flexible Applications of Cognitive Processing Therapy: Evidence-Based Treatment Methods. Academic Press; 2020.
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  34. Galovski TE, McSweeney LB, Nixon RDV, et al. Personalizing cognitive processing therapy with a case formulation approach to intentionally target impairment in psychosocial functioning associated with PTSD. Contemp Clin Trials Commun. 2024;42:101385. doi:10.1016/j.conctc.2024.101385
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Process Improvement for Engaging With Trauma-Focused Evidence-Based Psychotherapy for PTSD

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Process Improvement for Engaging With Trauma-Focused Evidence-Based Psychotherapy for PTSD

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