Hospital Medicine

Allergies to β-lactam antibiotics are among the most documented drug allergies, and approximately 10% of the US population reports an allergy specifically to penicillin.^1,2 Many allergic reactions are mediated via the antibody immunoglobulin E (IgE), producing an immediate hypersensitivity response, such as hives or anaphylaxis, which can be life threatening. Reactions also may be mediated by T cells of the immune system, which target various cell lines and can cause a drug reaction with eosinophilia and systemic symptoms or Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN).³Although β-lactam and penicillin allergies are frequently reported, < 5% manifest as either an IgE or T-cell–mediated response.⁴Furthermore, for the small proportion of patients who once had a true IgE-mediated reaction, including anaphylaxis, 80% experience a decrease in IgE antibodies over time, resulting in a loss of allergic response after about 10 years.² Due to this decline in IgE response and the initial mislabeling of mild non-IgE penicillin reactions, 95% of patients who are labeled as penicillin-allergic can eventually tolerate a penicillin.²

When a patient’s β-lactam allergy is never reevaluated, negative consequences can ensue. This allergy in a patient’s medical record can lead to the inappropriate avoidance of the entire β-lactam antibiotic class, which includes all penicillins, cephalosporins, and carbapenems. Withholding these antibiotics in certain situations can lead to negative patient outcomes.^5-7 For example, the drugs of choice for the infections syphilis and methicillin-susceptible Staphylococcus aureus (S aureus) are a penicillin or cephalosporin, and patients labeled as penicillin-allergic are more likely to experience treatment failure from using second-line therapies.⁸ Additionally, receiving non-β-lactam antibiotics puts patients at risk of multidrug-resistant pathogens like methicillin-resistant S aureus and vancomycin-resistant Enterococcus (VRE) as well as adverse effects, such as Clostridioides difficile infection.⁹ Using alternative, and likely broad-spectrum, antibiotics also can be financially detrimental: These medications often are more costly than their β-lactam alternatives, and the inappropriate use of therapies can result in longer hospital courses.^9-11

Penicillin allergies can complicate the antibiotic treatment strategy. The Memphis Veterans Affairs Medical Center (MVAMC) in Tennessee recently examined the negative sequelae of β-lactam allergies and found that more than half the patients received inappropriate antibiotics based on guideline recommendations, allergy history, and culture and sensitivity data.¹² To mitigate the problems for patients with β-lactam allergies, the 2016 guidelines from the Infectious Diseases Society of America (IDSA) on the Implementation of Antimicrobial Stewardship Programs (ASP) recommend that these patients undergo allergy assessment and penicillin skin testing.¹³In November 2017, MVAMC implemented such a process. The purpose of this study was to describe our pharmacist-run β-lactam allergy assessment (BLAA) protocol and penicillin allergy clinic (PAC) and evaluate their overall outcomes: the proportion of patients who have been cleared to receive an alternative β-lactam antibiotic or who have had their allergy removed altogether.

Methods

We conducted a retrospective, observational study with approval from the institutional review board at MVAMC. This institution is an academic teaching center with 240 acute care beds and a variety of outpatient clinics available at the main campus, serving veterans in Memphis and the Mid-South area, including west Tennessee, northern Mississippi, and northeastern Arkansas. Patients were consecutively evaluated from November 2017 through February 2020. All MVAMC patients with a documented β-lactam allergy were eligible for inclusion; there were no exclusion criteria. Electronic health record data were assessed and included basic patient demographics, allergy history, and the outcome of the BLAA and PAC. Descriptive statistics were used for data analysis.

The purpose of the BLAA process is to evaluate, clarify, and potentially clear patients of their β-lactam allergies. Started in November 2017, the process includes appropriate patient screening with documentation of the β-lactam allergy. When patients with a β-lactam allergy are admitted to the hospital, they are interviewed by an inpatient CPS. This pharmacist then enters an assessment into the patient’s chart, which includes details of the allergen, reaction, and timing of the event. Based on this information, the CPS provides recommendations: clearance for alternative β-lactams, avoidance of all β-lactams, or removal of the allergy.

In January 2019, the pharmacist-driven penicillin allergy clinic (PAC) was started. Eligible patients receive a skin test to confirm or rule out their allergy after hospital discharge. To facilitate patient identification and screening, the ASP/infectious diseases (ID) clinical pharmacist runs a daily report of hospitalized patients with documented β-lactam allergies. All inpatient CPSs had access to this report and could easily identify and interview patients. Following the interview, the pharmacist enters a note in the patient’s chart, using the BLAA template (eFigures 1 and 2). On completion, a note is viewable in the Notes section adjacent to the patient’s allergies. The pharmacist then can enter a PAC consult for eligible patients. Although most patients qualify for PAC, exclusion criteria include non–IgE-mediated allergies (ie, SJS/TEN), allergies to β-lactams other than penicillins, or recent reactions (ie, within the past 5 years). Each inpatient CPS is trained on this BLAA process, which includes patient screening, chart review, patient interviewing, and the BLAA template and note completion. Pharmacists must demonstrate competency in completing 5 BLAA notes with review from the ASP/ID pharmacist. Once training is completed, this process is integrated into the pharmacist’s everyday workflow.

Results

We evaluated 278 patients, using the BLAA protocol. In this veteran population, patients were generally older males and evenly split between African American and White patients (Table 2). Most patients reported an allergy to penicillin, with a rash being the most common reaction (Table 3).

Of the 278 assessed, 246 patients were evaluated via our BLAA alone and were not seen in PAC. We were able to remove 25% of these patients’ allergies by performing a thorough assessment. Of the 184 patients whose allergies could not be removed via the BLAA alone, 147 (80%) were still eligible for PAC but are awaiting scheduling. Patients ineligible for PAC included those with a cephalosporin allergy or a severe and non–IgE-mediated reaction. Other ineligible patients who were not eligible included those with diseases where risk of testing outweighed the benefits.

Of the 32 patients who were seen in PAC, 75% of allergies were removed through direct testing. No differences between race or gender were observed. Of the 8 patients (25%) whose allergies were not removed, 5 had confirmed penicillin allergies with a positive reaction; 4 of these patients have since tolerated an alternative β-lactam (either a cephalosporin or carbapenem). Three patients had inconclusive tests, most often because their positive control was nonreactive during the percutaneous portion of the skin test; these allergies could neither be confirmed nor removed. Two of these patients have since tolerated alternative β-lactams (both cephalosporins). Although these 8 patients should not be rechallenged with a penicillin antibiotic, they could still be considered for alternative β-lactams, based on the nature and histories of their allergies.

Discussion

We have implemented a unique and efficient way to evaluate, clarify, and clear β-lactam allergies. Our BLAA protocol allows for a smooth process by distributing the workload of evaluating and clarifying patients’ allergies over many inpatient CPS. Furthermore, the BLAA is readily accessible to health care providers (HCPs), allowing for optimal clinical decision making. HCPs can quickly gather further information on the β-lactam allergy, while seeing actionable recommendations, along with documentation of the PAC visit and subsequent events, if the patient has been seen.

This study demonstrated the promotion of alternative β-lactam use for nearly all patients: 99% of our patient population were deemed candidates for a β-lactam type antibiotic. This percentage included patients whose allergies have been fully cleared, both through BLAA alone and in PAC. Also included are patients who have been cleared for an alternative β-lactam and not necessarily a penicillin.

In our PAC, 8 patients were not cleared for penicillins: 5 had penicillin allergies confirmed, and 3 had inconclusive results. Based on the nature of their reactions and previous tolerance of alternative β-lactams, those 5 patients are still eligible for alternative β-lactams. Additionally, the 3 patients with inconclusive results are also eligible for alternative β-lactams for the same reasons. The patients for whom β-lactam antibiotic avoidance was recommended (4 patients, 1%) have not been seen in PAC, as their reactions disqualify them from penicillin skin testing. Two of these patients had anaphylaxis < 5 years ago and will be eligible for PAC if they do not experience anaphylaxis within the 5-year period.

Limitations

Although our study demonstrates many benefits of implementation of a BLAA protocol and PAC, it has several limitations. This analysis was a retrospective review of the limited number of patients who had assessments completed. Additionally, many patients were waiting to be seen in PAC. This delay is largely due to the length of time to establish our pharmacist-run PAC, the limited number of pharmacists trained and available for skin testing, the time constraints of our staff, and COVID-19 pandemic. Additionally, only pharmacists administer the BLAA questionnaire, but this process could be expanded to other professionals such as nursing staff. Also, this study was not set up as a before-and-after analysis that examined outcomes associated with individual patients. Future directions include assessing the clinical impact of this protocol, such as evaluating provider utilization of β-lactam antibiotics for patients with penicillin allergies and determining associated cost savings.

Conclusions

This study demonstrated that implementation of a pharmacist-driven BLAA protocol and PAC can effectively remove inaccurate penicillin allergy labels and clear patients for alternative β-lactam antibiotic use. The BLAA process in conjunction with PAC will continue to be used to better evaluate, clarify, and clear patient allergies to optimize their care.

Allergies to β-lactam antibiotics are among the most documented drug allergies, and approximately 10% of the US population reports an allergy specifically to penicillin.^1,2 Many allergic reactions are mediated via the antibody immunoglobulin E (IgE), producing an immediate hypersensitivity response, such as hives or anaphylaxis, which can be life threatening. Reactions also may be mediated by T cells of the immune system, which target various cell lines and can cause a drug reaction with eosinophilia and systemic symptoms or Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN).³Although β-lactam and penicillin allergies are frequently reported, < 5% manifest as either an IgE or T-cell–mediated response.⁴Furthermore, for the small proportion of patients who once had a true IgE-mediated reaction, including anaphylaxis, 80% experience a decrease in IgE antibodies over time, resulting in a loss of allergic response after about 10 years.² Due to this decline in IgE response and the initial mislabeling of mild non-IgE penicillin reactions, 95% of patients who are labeled as penicillin-allergic can eventually tolerate a penicillin.²

When a patient’s β-lactam allergy is never reevaluated, negative consequences can ensue. This allergy in a patient’s medical record can lead to the inappropriate avoidance of the entire β-lactam antibiotic class, which includes all penicillins, cephalosporins, and carbapenems. Withholding these antibiotics in certain situations can lead to negative patient outcomes.^5-7 For example, the drugs of choice for the infections syphilis and methicillin-susceptible Staphylococcus aureus (S aureus) are a penicillin or cephalosporin, and patients labeled as penicillin-allergic are more likely to experience treatment failure from using second-line therapies.⁸ Additionally, receiving non-β-lactam antibiotics puts patients at risk of multidrug-resistant pathogens like methicillin-resistant S aureus and vancomycin-resistant Enterococcus (VRE) as well as adverse effects, such as Clostridioides difficile infection.⁹ Using alternative, and likely broad-spectrum, antibiotics also can be financially detrimental: These medications often are more costly than their β-lactam alternatives, and the inappropriate use of therapies can result in longer hospital courses.^9-11

Penicillin allergies can complicate the antibiotic treatment strategy. The Memphis Veterans Affairs Medical Center (MVAMC) in Tennessee recently examined the negative sequelae of β-lactam allergies and found that more than half the patients received inappropriate antibiotics based on guideline recommendations, allergy history, and culture and sensitivity data.¹² To mitigate the problems for patients with β-lactam allergies, the 2016 guidelines from the Infectious Diseases Society of America (IDSA) on the Implementation of Antimicrobial Stewardship Programs (ASP) recommend that these patients undergo allergy assessment and penicillin skin testing.¹³In November 2017, MVAMC implemented such a process. The purpose of this study was to describe our pharmacist-run β-lactam allergy assessment (BLAA) protocol and penicillin allergy clinic (PAC) and evaluate their overall outcomes: the proportion of patients who have been cleared to receive an alternative β-lactam antibiotic or who have had their allergy removed altogether.

Methods

We conducted a retrospective, observational study with approval from the institutional review board at MVAMC. This institution is an academic teaching center with 240 acute care beds and a variety of outpatient clinics available at the main campus, serving veterans in Memphis and the Mid-South area, including west Tennessee, northern Mississippi, and northeastern Arkansas. Patients were consecutively evaluated from November 2017 through February 2020. All MVAMC patients with a documented β-lactam allergy were eligible for inclusion; there were no exclusion criteria. Electronic health record data were assessed and included basic patient demographics, allergy history, and the outcome of the BLAA and PAC. Descriptive statistics were used for data analysis.

The purpose of the BLAA process is to evaluate, clarify, and potentially clear patients of their β-lactam allergies. Started in November 2017, the process includes appropriate patient screening with documentation of the β-lactam allergy. When patients with a β-lactam allergy are admitted to the hospital, they are interviewed by an inpatient CPS. This pharmacist then enters an assessment into the patient’s chart, which includes details of the allergen, reaction, and timing of the event. Based on this information, the CPS provides recommendations: clearance for alternative β-lactams, avoidance of all β-lactams, or removal of the allergy.

In January 2019, the pharmacist-driven penicillin allergy clinic (PAC) was started. Eligible patients receive a skin test to confirm or rule out their allergy after hospital discharge. To facilitate patient identification and screening, the ASP/infectious diseases (ID) clinical pharmacist runs a daily report of hospitalized patients with documented β-lactam allergies. All inpatient CPSs had access to this report and could easily identify and interview patients. Following the interview, the pharmacist enters a note in the patient’s chart, using the BLAA template (eFigures 1 and 2). On completion, a note is viewable in the Notes section adjacent to the patient’s allergies. The pharmacist then can enter a PAC consult for eligible patients. Although most patients qualify for PAC, exclusion criteria include non–IgE-mediated allergies (ie, SJS/TEN), allergies to β-lactams other than penicillins, or recent reactions (ie, within the past 5 years). Each inpatient CPS is trained on this BLAA process, which includes patient screening, chart review, patient interviewing, and the BLAA template and note completion. Pharmacists must demonstrate competency in completing 5 BLAA notes with review from the ASP/ID pharmacist. Once training is completed, this process is integrated into the pharmacist’s everyday workflow.

Results

We evaluated 278 patients, using the BLAA protocol. In this veteran population, patients were generally older males and evenly split between African American and White patients (Table 2). Most patients reported an allergy to penicillin, with a rash being the most common reaction (Table 3).

Of the 278 assessed, 246 patients were evaluated via our BLAA alone and were not seen in PAC. We were able to remove 25% of these patients’ allergies by performing a thorough assessment. Of the 184 patients whose allergies could not be removed via the BLAA alone, 147 (80%) were still eligible for PAC but are awaiting scheduling. Patients ineligible for PAC included those with a cephalosporin allergy or a severe and non–IgE-mediated reaction. Other ineligible patients who were not eligible included those with diseases where risk of testing outweighed the benefits.

Of the 32 patients who were seen in PAC, 75% of allergies were removed through direct testing. No differences between race or gender were observed. Of the 8 patients (25%) whose allergies were not removed, 5 had confirmed penicillin allergies with a positive reaction; 4 of these patients have since tolerated an alternative β-lactam (either a cephalosporin or carbapenem). Three patients had inconclusive tests, most often because their positive control was nonreactive during the percutaneous portion of the skin test; these allergies could neither be confirmed nor removed. Two of these patients have since tolerated alternative β-lactams (both cephalosporins). Although these 8 patients should not be rechallenged with a penicillin antibiotic, they could still be considered for alternative β-lactams, based on the nature and histories of their allergies.

Discussion

We have implemented a unique and efficient way to evaluate, clarify, and clear β-lactam allergies. Our BLAA protocol allows for a smooth process by distributing the workload of evaluating and clarifying patients’ allergies over many inpatient CPS. Furthermore, the BLAA is readily accessible to health care providers (HCPs), allowing for optimal clinical decision making. HCPs can quickly gather further information on the β-lactam allergy, while seeing actionable recommendations, along with documentation of the PAC visit and subsequent events, if the patient has been seen.

This study demonstrated the promotion of alternative β-lactam use for nearly all patients: 99% of our patient population were deemed candidates for a β-lactam type antibiotic. This percentage included patients whose allergies have been fully cleared, both through BLAA alone and in PAC. Also included are patients who have been cleared for an alternative β-lactam and not necessarily a penicillin.

In our PAC, 8 patients were not cleared for penicillins: 5 had penicillin allergies confirmed, and 3 had inconclusive results. Based on the nature of their reactions and previous tolerance of alternative β-lactams, those 5 patients are still eligible for alternative β-lactams. Additionally, the 3 patients with inconclusive results are also eligible for alternative β-lactams for the same reasons. The patients for whom β-lactam antibiotic avoidance was recommended (4 patients, 1%) have not been seen in PAC, as their reactions disqualify them from penicillin skin testing. Two of these patients had anaphylaxis < 5 years ago and will be eligible for PAC if they do not experience anaphylaxis within the 5-year period.

Limitations

Although our study demonstrates many benefits of implementation of a BLAA protocol and PAC, it has several limitations. This analysis was a retrospective review of the limited number of patients who had assessments completed. Additionally, many patients were waiting to be seen in PAC. This delay is largely due to the length of time to establish our pharmacist-run PAC, the limited number of pharmacists trained and available for skin testing, the time constraints of our staff, and COVID-19 pandemic. Additionally, only pharmacists administer the BLAA questionnaire, but this process could be expanded to other professionals such as nursing staff. Also, this study was not set up as a before-and-after analysis that examined outcomes associated with individual patients. Future directions include assessing the clinical impact of this protocol, such as evaluating provider utilization of β-lactam antibiotics for patients with penicillin allergies and determining associated cost savings.

Conclusions

This study demonstrated that implementation of a pharmacist-driven BLAA protocol and PAC can effectively remove inaccurate penicillin allergy labels and clear patients for alternative β-lactam antibiotic use. The BLAA process in conjunction with PAC will continue to be used to better evaluate, clarify, and clear patient allergies to optimize their care.

Allergies to β-lactam antibiotics are among the most documented drug allergies, and approximately 10% of the US population reports an allergy specifically to penicillin.^1,2 Many allergic reactions are mediated via the antibody immunoglobulin E (IgE), producing an immediate hypersensitivity response, such as hives or anaphylaxis, which can be life threatening. Reactions also may be mediated by T cells of the immune system, which target various cell lines and can cause a drug reaction with eosinophilia and systemic symptoms or Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN).³Although β-lactam and penicillin allergies are frequently reported, < 5% manifest as either an IgE or T-cell–mediated response.⁴Furthermore, for the small proportion of patients who once had a true IgE-mediated reaction, including anaphylaxis, 80% experience a decrease in IgE antibodies over time, resulting in a loss of allergic response after about 10 years.² Due to this decline in IgE response and the initial mislabeling of mild non-IgE penicillin reactions, 95% of patients who are labeled as penicillin-allergic can eventually tolerate a penicillin.²

When a patient’s β-lactam allergy is never reevaluated, negative consequences can ensue. This allergy in a patient’s medical record can lead to the inappropriate avoidance of the entire β-lactam antibiotic class, which includes all penicillins, cephalosporins, and carbapenems. Withholding these antibiotics in certain situations can lead to negative patient outcomes.^5-7 For example, the drugs of choice for the infections syphilis and methicillin-susceptible Staphylococcus aureus (S aureus) are a penicillin or cephalosporin, and patients labeled as penicillin-allergic are more likely to experience treatment failure from using second-line therapies.⁸ Additionally, receiving non-β-lactam antibiotics puts patients at risk of multidrug-resistant pathogens like methicillin-resistant S aureus and vancomycin-resistant Enterococcus (VRE) as well as adverse effects, such as Clostridioides difficile infection.⁹ Using alternative, and likely broad-spectrum, antibiotics also can be financially detrimental: These medications often are more costly than their β-lactam alternatives, and the inappropriate use of therapies can result in longer hospital courses.^9-11

Penicillin allergies can complicate the antibiotic treatment strategy. The Memphis Veterans Affairs Medical Center (MVAMC) in Tennessee recently examined the negative sequelae of β-lactam allergies and found that more than half the patients received inappropriate antibiotics based on guideline recommendations, allergy history, and culture and sensitivity data.¹² To mitigate the problems for patients with β-lactam allergies, the 2016 guidelines from the Infectious Diseases Society of America (IDSA) on the Implementation of Antimicrobial Stewardship Programs (ASP) recommend that these patients undergo allergy assessment and penicillin skin testing.¹³In November 2017, MVAMC implemented such a process. The purpose of this study was to describe our pharmacist-run β-lactam allergy assessment (BLAA) protocol and penicillin allergy clinic (PAC) and evaluate their overall outcomes: the proportion of patients who have been cleared to receive an alternative β-lactam antibiotic or who have had their allergy removed altogether.

Methods

We conducted a retrospective, observational study with approval from the institutional review board at MVAMC. This institution is an academic teaching center with 240 acute care beds and a variety of outpatient clinics available at the main campus, serving veterans in Memphis and the Mid-South area, including west Tennessee, northern Mississippi, and northeastern Arkansas. Patients were consecutively evaluated from November 2017 through February 2020. All MVAMC patients with a documented β-lactam allergy were eligible for inclusion; there were no exclusion criteria. Electronic health record data were assessed and included basic patient demographics, allergy history, and the outcome of the BLAA and PAC. Descriptive statistics were used for data analysis.

The purpose of the BLAA process is to evaluate, clarify, and potentially clear patients of their β-lactam allergies. Started in November 2017, the process includes appropriate patient screening with documentation of the β-lactam allergy. When patients with a β-lactam allergy are admitted to the hospital, they are interviewed by an inpatient CPS. This pharmacist then enters an assessment into the patient’s chart, which includes details of the allergen, reaction, and timing of the event. Based on this information, the CPS provides recommendations: clearance for alternative β-lactams, avoidance of all β-lactams, or removal of the allergy.

In January 2019, the pharmacist-driven penicillin allergy clinic (PAC) was started. Eligible patients receive a skin test to confirm or rule out their allergy after hospital discharge. To facilitate patient identification and screening, the ASP/infectious diseases (ID) clinical pharmacist runs a daily report of hospitalized patients with documented β-lactam allergies. All inpatient CPSs had access to this report and could easily identify and interview patients. Following the interview, the pharmacist enters a note in the patient’s chart, using the BLAA template (eFigures 1 and 2). On completion, a note is viewable in the Notes section adjacent to the patient’s allergies. The pharmacist then can enter a PAC consult for eligible patients. Although most patients qualify for PAC, exclusion criteria include non–IgE-mediated allergies (ie, SJS/TEN), allergies to β-lactams other than penicillins, or recent reactions (ie, within the past 5 years). Each inpatient CPS is trained on this BLAA process, which includes patient screening, chart review, patient interviewing, and the BLAA template and note completion. Pharmacists must demonstrate competency in completing 5 BLAA notes with review from the ASP/ID pharmacist. Once training is completed, this process is integrated into the pharmacist’s everyday workflow.

Results

We evaluated 278 patients, using the BLAA protocol. In this veteran population, patients were generally older males and evenly split between African American and White patients (Table 2). Most patients reported an allergy to penicillin, with a rash being the most common reaction (Table 3).

Of the 278 assessed, 246 patients were evaluated via our BLAA alone and were not seen in PAC. We were able to remove 25% of these patients’ allergies by performing a thorough assessment. Of the 184 patients whose allergies could not be removed via the BLAA alone, 147 (80%) were still eligible for PAC but are awaiting scheduling. Patients ineligible for PAC included those with a cephalosporin allergy or a severe and non–IgE-mediated reaction. Other ineligible patients who were not eligible included those with diseases where risk of testing outweighed the benefits.

Of the 32 patients who were seen in PAC, 75% of allergies were removed through direct testing. No differences between race or gender were observed. Of the 8 patients (25%) whose allergies were not removed, 5 had confirmed penicillin allergies with a positive reaction; 4 of these patients have since tolerated an alternative β-lactam (either a cephalosporin or carbapenem). Three patients had inconclusive tests, most often because their positive control was nonreactive during the percutaneous portion of the skin test; these allergies could neither be confirmed nor removed. Two of these patients have since tolerated alternative β-lactams (both cephalosporins). Although these 8 patients should not be rechallenged with a penicillin antibiotic, they could still be considered for alternative β-lactams, based on the nature and histories of their allergies.

Discussion

We have implemented a unique and efficient way to evaluate, clarify, and clear β-lactam allergies. Our BLAA protocol allows for a smooth process by distributing the workload of evaluating and clarifying patients’ allergies over many inpatient CPS. Furthermore, the BLAA is readily accessible to health care providers (HCPs), allowing for optimal clinical decision making. HCPs can quickly gather further information on the β-lactam allergy, while seeing actionable recommendations, along with documentation of the PAC visit and subsequent events, if the patient has been seen.

This study demonstrated the promotion of alternative β-lactam use for nearly all patients: 99% of our patient population were deemed candidates for a β-lactam type antibiotic. This percentage included patients whose allergies have been fully cleared, both through BLAA alone and in PAC. Also included are patients who have been cleared for an alternative β-lactam and not necessarily a penicillin.

In our PAC, 8 patients were not cleared for penicillins: 5 had penicillin allergies confirmed, and 3 had inconclusive results. Based on the nature of their reactions and previous tolerance of alternative β-lactams, those 5 patients are still eligible for alternative β-lactams. Additionally, the 3 patients with inconclusive results are also eligible for alternative β-lactams for the same reasons. The patients for whom β-lactam antibiotic avoidance was recommended (4 patients, 1%) have not been seen in PAC, as their reactions disqualify them from penicillin skin testing. Two of these patients had anaphylaxis < 5 years ago and will be eligible for PAC if they do not experience anaphylaxis within the 5-year period.

Limitations

Although our study demonstrates many benefits of implementation of a BLAA protocol and PAC, it has several limitations. This analysis was a retrospective review of the limited number of patients who had assessments completed. Additionally, many patients were waiting to be seen in PAC. This delay is largely due to the length of time to establish our pharmacist-run PAC, the limited number of pharmacists trained and available for skin testing, the time constraints of our staff, and COVID-19 pandemic. Additionally, only pharmacists administer the BLAA questionnaire, but this process could be expanded to other professionals such as nursing staff. Also, this study was not set up as a before-and-after analysis that examined outcomes associated with individual patients. Future directions include assessing the clinical impact of this protocol, such as evaluating provider utilization of β-lactam antibiotics for patients with penicillin allergies and determining associated cost savings.

Conclusions

This study demonstrated that implementation of a pharmacist-driven BLAA protocol and PAC can effectively remove inaccurate penicillin allergy labels and clear patients for alternative β-lactam antibiotic use. The BLAA process in conjunction with PAC will continue to be used to better evaluate, clarify, and clear patient allergies to optimize their care.

The United States is facing an opioid crisis in which approximately 10 million people have misused opioids in the past year, and an estimated 2 million people have an opioid use disorder (OUD).¹ Compared with the general population, veterans treated in the Veterans Health Administration (VHA) facilities are at nearly twice the risk for accidental opioid overdose.² The implementation of opioid safety measures in VHA facilities across all care settings is a priority in addressing this public health crisis. Hence, VHA leadership is working to minimize veteran risk of fatal opioid overdoses and to increase veteran access to medication-assisted treatments (MAT) for OUD.³

Since the administration of our survey, the VHA has shifted to using the term medication for opioid use disorder (MOUD) instead of MAT for OUD. However, for consistency with the survey we distributed, we use MAT in this analysis.

Acute care settings represent an opportunity to offer appropriate opioid care and treatment options to patients at risk for OUD or opioid-related overdose. VHA facilities offer 2 outpatient acute care settings for emergent ambulatory care: emergency departments (EDs) and urgent care centers (UCCs). Annually, these settings see an estimated 2.5 million patients each year, making EDs and UCCs critical access points of OUD care for veterans. Partnering with key national VHA stakeholders from Pharmacy Benefits Management (PBM), the Office of Emergency Medicine, and Academic Detailing Services (ADS), we developed the Emergency Department Opioid Safety Initiative (ED OSI) aimed at implementing and evaluating opioid safety measures in VHA outpatient acute care settings.

The US Department of Veterans Affairs (VA)/Department of Defense (DoD) Clinical Practice Guidelines for Opioid Therapy for Chronic Pain (CPG) makes recommendations for the initiation and continuation of opioids, risk mitigation, taper of opioids, and opioid therapy for acute pain in VHA facilities.⁴ Using these recommendations, we developed the broad aims of the ED OSI quality improvement (QI) program. The CPG is clear about the prioritization of safe opioid prescribing practices. New opioid prescriptions written in the ED have been associated with continued and chronic opioid use.⁵ At the time of prescription, patients not currently and chronically on opioids who receive more than a 3-day supply are at increased risk of becoming long-term opioid users.⁶ Given the annual volume of patients seen, VHA ED/UCCs are a crucial area for implementing better opioid prescribing practices.

The CPG also includes recommendations for the prescribing or coprescribing of naloxone rescue kits. The administration of naloxone following opioid overdose has been found to be an effective measure against fatal overdose. Increasing provider awareness of common risk factors for opioid-related overdose (eg, frequent ED visits or hospitalizations) helps facilitate a discussion on naloxone prescribing at discharge. Prior studies provide evidence that naloxone distribution and accompanying education also are effective in reducing opioid overdose mortalityand ED visits related to adverse opioid-related events.^7,8

Similarly, the guidelines provide recommendations for the use of MAT for veterans with OUD. MAT for OUD is considered a first-line treatment option for patients with moderate-to-severe OUD. When used to treat patients with unsafe opioid use, this treatment helps alleviate symptoms of withdrawal, which can increase opioid taper adherence and has a protective effect against opioid overdose mortality.⁹ MAT initiated in the ED can increase patient engagement to addiction services.¹⁰

These 3 CPG recommendations serve as the basis for the broad goals of the ED OSI program. We aim to develop, implement, and evaluate programs and initiatives to (aim 1) reduce inappropriate opioid prescribing from VHA EDs; (aim 2) increase naloxone distribution from VHA EDs; and (aim 3) increase access to MAT initiation from VHA EDs through the implementation of ED-based MAT-initiation programs with EDs across the VHA. Aim 1 was a focused and strategic QI effort to implement an ED-based program to reduce inappropriate opioid prescribing. The ED OSI prescribing program offered a 4-step bundled approach: (1) sharing of opioid prescribing dashboard data with ED medical director and academic detailer; (2) education of ED providers and implementation of toolkit resources; (3) academic detailers conduct audit and feedback session(s) with highest prescribers; and (4) quarterly reports of opioid prescribing data to ED providers.

Results from the pilot suggested that our program was associated with accelerating the rate at which ED prescribing rates decreased.¹¹ In addition, the pilot found that ED-based QI initiatives in VHA facilities are a feasible practice. As we work to develop and implement the next 2 phases of the QI program, a major consideration is to identify facilitators and address any existing barriers to the implementation of naloxone distribution (aim 2) and MAT-initiation (aim 3) programs for treatment-naïve patients from VHA EDs. To date, there have been no recent published studies examining the barriers and facilitators to use or implementation of MAT initiation or naloxone distribution in VHA facilities or, more specifically, from VHA EDs.¹² As part of our QI program, we set out to better understand VHA ED provider perceptions of barriers and facilitators to implementation of programs aimed at increasing naloxone distribution and initiation of MAT for treatment-naïve patients in the ED.

Methods 

This project received a QI designation from the Office of PBM Academic Detailing Service Institutional Review Board at the Edward Hines, Jr. Veterans Affairs Hospital VA Medical Center (VAMC). This designation was reviewed and approved by the Rocky Mountain Regional VAMC Research and Development service. In addition, we received national union approval to disseminate this survey nationally across all VA Integrated Service Networks (VISNs).

Survey

We worked with VHA subject matter experts, key stakeholders, and the VA Collaborative Evaluation Center (VACE) to develop the survey. Subject matter experts and stakeholders included VHA emergency medicine leadership, ADS leadership, and mental health and substance treatment providers. VACE is an interdisciplinary group of mixed-method researchers. The survey questions aimed to capture perceptions and experiences regarding naloxone distribution and new MAT initiation of VHA ED/UCC providers.

We used a variety of survey question formats. Close-ended questions with a predefined list of answer options were used to capture discrete domains, such as demographic information, comfort level, and experience level. To capture health care provider (HCP) perceptions on barriers and facilitators, we used multiple-answer multiple-choice questions. Built into this question format was a free-response option, which allowed respondents to offer additional barriers or facilitators. Respondents also had the option of not answering individual questions.

We identified physicians, nurse practitioners (NPs), and physician assistants (PAs) who saw at least 100 patients in the ED or UCC in at least one 3-month period in the prior year and obtained an email address for each. In total, 2228 ED or UCC providers across 132 facilities were emailed a survey; 1883 (84.5%) were ED providers and 345 (15.5%) were UCC providers.

We used Research Electronic Data Capture (REDCap) software to build and disseminate the survey via email. Surveys were initially disseminated in late January 2019. During the 3-month survey period, recipients received 3 automated email reminders from REDCap to complete the survey. Survey data were exported from REDCap. Results were analyzed using descriptive statistics analyses with Microsoft Excel.

Results 

One respondent received the survey in error and was excluded from the analysis. The survey response rate was 16.7%: 372 responses from 103 unique facilities. Each VISN had a mean 20 respondents. The majority of respondents (n = 286, 76.9%) worked in highly complex level 1 facilities characterized by high patient volume and more high-risk patients and were teaching and research facilities. Respondents were asked to describe their most recent ED or UCC role. While 281 respondents (75.5%) were medical doctors, 61 respondents (16.4%) were NPs, 30 (8.1%) were PAs, and 26 (7.0%) were ED/UCC chiefs or medical directors (Table 1). Most respondents (80.4%) reported at least 10 years of health care experience.

The majority of respondents (72.9%) believed that HCPs at their VHA facility should be prescribing naloxone. When asked to specify which HCPs should be prescribing naloxone, most HCP respondents selected pharmacists (76.4%) and substance abuse providers (71.6%). Less than half of respondents (45.0%) felt that VA ED/UCC providers also should be prescribing naloxone. However, 58.1% of most HCP respondents reported being comfortable or very comfortable with prescribing naloxone to a patient in the ED or UCC who already had an existing prescription of opioids. Similarly, 52.7% of respondents reported being comfortable or very comfortable with coprescribing naloxone when discharging a patient with an opioid prescription from the ED/UCC. Notably, while 36.7% of PAs reported being comfortable/very comfortable coprescribing naloxone, 46.7% reported being comfortable/very comfortable prescribing naloxone to a patient with an existing opioid prescription. Physicians and NPs expressed similar levels of comfort with coprescribing and prescribing naloxone.

Respondents across provider types indicated a number of barriers to prescribing naloxone to medically appropriate patients (Table 2). Many respondents indicated prescribing naloxone was beyond the ED/UCC provider scope of practice (35.2%), followed by the perceived stigma associated with naloxone (33.3%), time required to prescribe naloxone (23.9%), and concern with patient’s ability to use naloxone (22.8%).

Discussion

To the best of our knowledge, there have not been any studies examining HCP perceptions of the barriers and facilitators to naloxone distribution or the initiation of MAT in VHA ED and UCCs. Veterans are at an increased risk of overdose when compared with the general population, and increasing access to opioid safety measures (eg, safer prescribing practices, naloxone distribution) and treatment with MAT for OUD across all clinical settings has been a VHA priority.³

National guidance from VHA leadership, the Centers for Disease Control and Prevention (CDC), the US Surgeon General, and the US Department of Health and Human Services (HHS) call for an all-hands-on-deck approach to combatting opioid overdose with naloxone distribution or MAT (such as buprenorphine) initiation.¹³ VHA ED and UCC settings provide acute outpatient care to patients with medical or psychiatric illnesses or injuries that the patient believes requires emergent or immediate medical attention or for which there is a critical need for treatment to prevent deterioration of the condition or the possible impairment of recovery.¹⁴ However, ED and UCC environments are often regarded as settings meant to stabilize a patient until they can be seen by a primary care or long-term care provider.

A major barrier identified by HCPs was that MAT for OUD was outside their ED/UCC scope of practice, which suggests a need for a top-down or peer-to-peer reexamination of the role of HCPs in ED/UCC settings. Any naloxone distribution and/or MAT-initiation program in VHA ED/UCCs should consider education about the role of ED/UCC HCPs in opioid safety and treatment. According to a VHA Support Service Center (VSSC) employee report database, in fiscal year 2018, per diem/fee-basis and contract HCPs comprised nearly 40% of clinical emergency medicine physician full-time equivalent employees, which presents a unique barrier to HCP education. Fee-basis and per diem HCPs may be less aware of, engaged in, or committed to VHA goals. Additionally, short-term HCPs may have fewer opportunities for training and education regarding naloxone or MAT use.

Only 25.3% of HCPs reported that their facility leadership was supportive or very supportive of MAT prescribing. This suggests that facility leadership should be engaged in any efforts to implement a MAT-initiation program in the facility’s ED. Engaging leadership in efforts to implement ED-based MAT programs will allow for a better understanding of leadership goals as related to opioid safety and an opportunity to address concerns regarding prescribing MAT in the ED. We recommend engaging facility leadership early in MAT implementation efforts. Respectively, 12.4% and 28.2% of HCP respondents reported discomfort prescribing naloxone or using MAT, suggesting a need for more education. Similarly, only 6.8% of HCPs reported comfort with using MAT.

A consideration for implementing ED/UCC-based MAT should be the inclusion of a training component. An evidence-based clinical treatment pathway that is appropriate to the ED/UCC setting and facility on the administration of MAT also could be beneficial. A clinical treatment pathway that includes ED/UCC-initiated discharge recommendations would address HCP concerns of unclear follow-up plans and system for referral of care. To this end, a key implementation task is coordinating with other outpatient services (eg, pain management clinic, substance use disorder treatment clinic) equipped for long-term patient follow-up to develop a system for referral of care. For example, as part of the clinical treatment pathway, an ED can develop a system of referral for patients initiated on MAT in the ED in which patients are referred for follow-up at the facility’s substance use disorder treatment clinic to be seen within 72 hours to continue the administration of MAT (such as buprenorphine).

In addition to HCP education, results suggest that patient/veteran education regarding naloxone and/or MAT should be considered. HCPs indicated that having help from a pharmacist to educate the patient about the medications would be a facilitator to naloxone distribution and MAT initiation. Similarly, patient knowledge of the medications also was endorsed as a facilitator. As such, a consideration for any future ED/UCC-based naloxone distribution or MAT-initiation programs in the VHA should be patient education whether by a clinically trained professional or an educational campaign for veterans.

Expanded naloxone distribution and initiation of MAT for OUD for EDs/UCCs across the VHA could impact the lives of veterans on long-term opioid therapy, with OUD, or who are otherwise at risk for opioid overdose. Steps taken to address the barriers and leverage the facilitators identified by HCP respondents can greatly reduce current obstacles to widespread implementation of ED/UCC-based naloxone distribution and MAT initiation nationally within the VHA.

Limitations

This survey had a low response rate (16.7%). One potential explanation for the low response rate is that when the survey was deployed, many of the VHA ED/UCC physicians were per-diem employees. Per-diem physicians may be less engaged and aware of site facilitators or barriers to naloxone and MAT prescribing. This, too, may have potentially skewed the collected data. However, the survey did not ask HCPs to disclose their employment status; thus, exact rates of per diem respondents are unknown.

We aimed to capture only self-perceived barriers to prescribing naloxone and MAT in the ED, but we did not capture or measure HCP respondent’s actual prescribing rates of MAT or naloxone. Understanding HCP perceptions of naloxone distribution and MAT initiation in the ED may have been further informed by comparing HCP responses to their actual clinical practice as related to their prescribing of these medications. In future research, we will link HCPs with the actual numbers of naloxone and MAT medications prescribed. Additionally, we do not know how many of these barriers or proposed facilitators will impact clinical practice.

Conclusions

A key aim for VHA leadership is to increase veteran access to naloxone distribution and MAT for OUD across clinical areas. The present study aimed to identify HCP perceptions of barriers and facilitators to the naloxone distribution and MAT-initiation programs in VHA ED/UCCs to inform the development of a targeted QI program to implement these opioid safety measures. Although the survey yielded a low response rate, results allowed us to identify important action items for our QI program, such as the development of clear protocols, follow-up plans, and systems for referral of care and HCP educational materials related to MAT and naloxone. We hope this work will serve as the basis for ED/UCC-tailored programs that can provide customized educational programs for HCPs designed to overcome known barriers to naloxone and MAT initiation.

Acknowledgments
This work was supported by the VA Office of Specialty Care Services 10P11 and through funding provided by the Comprehensive Addiction and Recovery Act (CARA).

The United States is facing an opioid crisis in which approximately 10 million people have misused opioids in the past year, and an estimated 2 million people have an opioid use disorder (OUD).¹ Compared with the general population, veterans treated in the Veterans Health Administration (VHA) facilities are at nearly twice the risk for accidental opioid overdose.² The implementation of opioid safety measures in VHA facilities across all care settings is a priority in addressing this public health crisis. Hence, VHA leadership is working to minimize veteran risk of fatal opioid overdoses and to increase veteran access to medication-assisted treatments (MAT) for OUD.³

Since the administration of our survey, the VHA has shifted to using the term medication for opioid use disorder (MOUD) instead of MAT for OUD. However, for consistency with the survey we distributed, we use MAT in this analysis.

Acute care settings represent an opportunity to offer appropriate opioid care and treatment options to patients at risk for OUD or opioid-related overdose. VHA facilities offer 2 outpatient acute care settings for emergent ambulatory care: emergency departments (EDs) and urgent care centers (UCCs). Annually, these settings see an estimated 2.5 million patients each year, making EDs and UCCs critical access points of OUD care for veterans. Partnering with key national VHA stakeholders from Pharmacy Benefits Management (PBM), the Office of Emergency Medicine, and Academic Detailing Services (ADS), we developed the Emergency Department Opioid Safety Initiative (ED OSI) aimed at implementing and evaluating opioid safety measures in VHA outpatient acute care settings.

The US Department of Veterans Affairs (VA)/Department of Defense (DoD) Clinical Practice Guidelines for Opioid Therapy for Chronic Pain (CPG) makes recommendations for the initiation and continuation of opioids, risk mitigation, taper of opioids, and opioid therapy for acute pain in VHA facilities.⁴ Using these recommendations, we developed the broad aims of the ED OSI quality improvement (QI) program. The CPG is clear about the prioritization of safe opioid prescribing practices. New opioid prescriptions written in the ED have been associated with continued and chronic opioid use.⁵ At the time of prescription, patients not currently and chronically on opioids who receive more than a 3-day supply are at increased risk of becoming long-term opioid users.⁶ Given the annual volume of patients seen, VHA ED/UCCs are a crucial area for implementing better opioid prescribing practices.

The CPG also includes recommendations for the prescribing or coprescribing of naloxone rescue kits. The administration of naloxone following opioid overdose has been found to be an effective measure against fatal overdose. Increasing provider awareness of common risk factors for opioid-related overdose (eg, frequent ED visits or hospitalizations) helps facilitate a discussion on naloxone prescribing at discharge. Prior studies provide evidence that naloxone distribution and accompanying education also are effective in reducing opioid overdose mortalityand ED visits related to adverse opioid-related events.^7,8

Similarly, the guidelines provide recommendations for the use of MAT for veterans with OUD. MAT for OUD is considered a first-line treatment option for patients with moderate-to-severe OUD. When used to treat patients with unsafe opioid use, this treatment helps alleviate symptoms of withdrawal, which can increase opioid taper adherence and has a protective effect against opioid overdose mortality.⁹ MAT initiated in the ED can increase patient engagement to addiction services.¹⁰

These 3 CPG recommendations serve as the basis for the broad goals of the ED OSI program. We aim to develop, implement, and evaluate programs and initiatives to (aim 1) reduce inappropriate opioid prescribing from VHA EDs; (aim 2) increase naloxone distribution from VHA EDs; and (aim 3) increase access to MAT initiation from VHA EDs through the implementation of ED-based MAT-initiation programs with EDs across the VHA. Aim 1 was a focused and strategic QI effort to implement an ED-based program to reduce inappropriate opioid prescribing. The ED OSI prescribing program offered a 4-step bundled approach: (1) sharing of opioid prescribing dashboard data with ED medical director and academic detailer; (2) education of ED providers and implementation of toolkit resources; (3) academic detailers conduct audit and feedback session(s) with highest prescribers; and (4) quarterly reports of opioid prescribing data to ED providers.

Results from the pilot suggested that our program was associated with accelerating the rate at which ED prescribing rates decreased.¹¹ In addition, the pilot found that ED-based QI initiatives in VHA facilities are a feasible practice. As we work to develop and implement the next 2 phases of the QI program, a major consideration is to identify facilitators and address any existing barriers to the implementation of naloxone distribution (aim 2) and MAT-initiation (aim 3) programs for treatment-naïve patients from VHA EDs. To date, there have been no recent published studies examining the barriers and facilitators to use or implementation of MAT initiation or naloxone distribution in VHA facilities or, more specifically, from VHA EDs.¹² As part of our QI program, we set out to better understand VHA ED provider perceptions of barriers and facilitators to implementation of programs aimed at increasing naloxone distribution and initiation of MAT for treatment-naïve patients in the ED.

Methods 

This project received a QI designation from the Office of PBM Academic Detailing Service Institutional Review Board at the Edward Hines, Jr. Veterans Affairs Hospital VA Medical Center (VAMC). This designation was reviewed and approved by the Rocky Mountain Regional VAMC Research and Development service. In addition, we received national union approval to disseminate this survey nationally across all VA Integrated Service Networks (VISNs).

Survey

We worked with VHA subject matter experts, key stakeholders, and the VA Collaborative Evaluation Center (VACE) to develop the survey. Subject matter experts and stakeholders included VHA emergency medicine leadership, ADS leadership, and mental health and substance treatment providers. VACE is an interdisciplinary group of mixed-method researchers. The survey questions aimed to capture perceptions and experiences regarding naloxone distribution and new MAT initiation of VHA ED/UCC providers.

We used a variety of survey question formats. Close-ended questions with a predefined list of answer options were used to capture discrete domains, such as demographic information, comfort level, and experience level. To capture health care provider (HCP) perceptions on barriers and facilitators, we used multiple-answer multiple-choice questions. Built into this question format was a free-response option, which allowed respondents to offer additional barriers or facilitators. Respondents also had the option of not answering individual questions.

We identified physicians, nurse practitioners (NPs), and physician assistants (PAs) who saw at least 100 patients in the ED or UCC in at least one 3-month period in the prior year and obtained an email address for each. In total, 2228 ED or UCC providers across 132 facilities were emailed a survey; 1883 (84.5%) were ED providers and 345 (15.5%) were UCC providers.

We used Research Electronic Data Capture (REDCap) software to build and disseminate the survey via email. Surveys were initially disseminated in late January 2019. During the 3-month survey period, recipients received 3 automated email reminders from REDCap to complete the survey. Survey data were exported from REDCap. Results were analyzed using descriptive statistics analyses with Microsoft Excel.

Results 

One respondent received the survey in error and was excluded from the analysis. The survey response rate was 16.7%: 372 responses from 103 unique facilities. Each VISN had a mean 20 respondents. The majority of respondents (n = 286, 76.9%) worked in highly complex level 1 facilities characterized by high patient volume and more high-risk patients and were teaching and research facilities. Respondents were asked to describe their most recent ED or UCC role. While 281 respondents (75.5%) were medical doctors, 61 respondents (16.4%) were NPs, 30 (8.1%) were PAs, and 26 (7.0%) were ED/UCC chiefs or medical directors (Table 1). Most respondents (80.4%) reported at least 10 years of health care experience.

The majority of respondents (72.9%) believed that HCPs at their VHA facility should be prescribing naloxone. When asked to specify which HCPs should be prescribing naloxone, most HCP respondents selected pharmacists (76.4%) and substance abuse providers (71.6%). Less than half of respondents (45.0%) felt that VA ED/UCC providers also should be prescribing naloxone. However, 58.1% of most HCP respondents reported being comfortable or very comfortable with prescribing naloxone to a patient in the ED or UCC who already had an existing prescription of opioids. Similarly, 52.7% of respondents reported being comfortable or very comfortable with coprescribing naloxone when discharging a patient with an opioid prescription from the ED/UCC. Notably, while 36.7% of PAs reported being comfortable/very comfortable coprescribing naloxone, 46.7% reported being comfortable/very comfortable prescribing naloxone to a patient with an existing opioid prescription. Physicians and NPs expressed similar levels of comfort with coprescribing and prescribing naloxone.

Respondents across provider types indicated a number of barriers to prescribing naloxone to medically appropriate patients (Table 2). Many respondents indicated prescribing naloxone was beyond the ED/UCC provider scope of practice (35.2%), followed by the perceived stigma associated with naloxone (33.3%), time required to prescribe naloxone (23.9%), and concern with patient’s ability to use naloxone (22.8%).

Discussion

To the best of our knowledge, there have not been any studies examining HCP perceptions of the barriers and facilitators to naloxone distribution or the initiation of MAT in VHA ED and UCCs. Veterans are at an increased risk of overdose when compared with the general population, and increasing access to opioid safety measures (eg, safer prescribing practices, naloxone distribution) and treatment with MAT for OUD across all clinical settings has been a VHA priority.³

National guidance from VHA leadership, the Centers for Disease Control and Prevention (CDC), the US Surgeon General, and the US Department of Health and Human Services (HHS) call for an all-hands-on-deck approach to combatting opioid overdose with naloxone distribution or MAT (such as buprenorphine) initiation.¹³ VHA ED and UCC settings provide acute outpatient care to patients with medical or psychiatric illnesses or injuries that the patient believes requires emergent or immediate medical attention or for which there is a critical need for treatment to prevent deterioration of the condition or the possible impairment of recovery.¹⁴ However, ED and UCC environments are often regarded as settings meant to stabilize a patient until they can be seen by a primary care or long-term care provider.

A major barrier identified by HCPs was that MAT for OUD was outside their ED/UCC scope of practice, which suggests a need for a top-down or peer-to-peer reexamination of the role of HCPs in ED/UCC settings. Any naloxone distribution and/or MAT-initiation program in VHA ED/UCCs should consider education about the role of ED/UCC HCPs in opioid safety and treatment. According to a VHA Support Service Center (VSSC) employee report database, in fiscal year 2018, per diem/fee-basis and contract HCPs comprised nearly 40% of clinical emergency medicine physician full-time equivalent employees, which presents a unique barrier to HCP education. Fee-basis and per diem HCPs may be less aware of, engaged in, or committed to VHA goals. Additionally, short-term HCPs may have fewer opportunities for training and education regarding naloxone or MAT use.

Only 25.3% of HCPs reported that their facility leadership was supportive or very supportive of MAT prescribing. This suggests that facility leadership should be engaged in any efforts to implement a MAT-initiation program in the facility’s ED. Engaging leadership in efforts to implement ED-based MAT programs will allow for a better understanding of leadership goals as related to opioid safety and an opportunity to address concerns regarding prescribing MAT in the ED. We recommend engaging facility leadership early in MAT implementation efforts. Respectively, 12.4% and 28.2% of HCP respondents reported discomfort prescribing naloxone or using MAT, suggesting a need for more education. Similarly, only 6.8% of HCPs reported comfort with using MAT.

A consideration for implementing ED/UCC-based MAT should be the inclusion of a training component. An evidence-based clinical treatment pathway that is appropriate to the ED/UCC setting and facility on the administration of MAT also could be beneficial. A clinical treatment pathway that includes ED/UCC-initiated discharge recommendations would address HCP concerns of unclear follow-up plans and system for referral of care. To this end, a key implementation task is coordinating with other outpatient services (eg, pain management clinic, substance use disorder treatment clinic) equipped for long-term patient follow-up to develop a system for referral of care. For example, as part of the clinical treatment pathway, an ED can develop a system of referral for patients initiated on MAT in the ED in which patients are referred for follow-up at the facility’s substance use disorder treatment clinic to be seen within 72 hours to continue the administration of MAT (such as buprenorphine).

In addition to HCP education, results suggest that patient/veteran education regarding naloxone and/or MAT should be considered. HCPs indicated that having help from a pharmacist to educate the patient about the medications would be a facilitator to naloxone distribution and MAT initiation. Similarly, patient knowledge of the medications also was endorsed as a facilitator. As such, a consideration for any future ED/UCC-based naloxone distribution or MAT-initiation programs in the VHA should be patient education whether by a clinically trained professional or an educational campaign for veterans.

Expanded naloxone distribution and initiation of MAT for OUD for EDs/UCCs across the VHA could impact the lives of veterans on long-term opioid therapy, with OUD, or who are otherwise at risk for opioid overdose. Steps taken to address the barriers and leverage the facilitators identified by HCP respondents can greatly reduce current obstacles to widespread implementation of ED/UCC-based naloxone distribution and MAT initiation nationally within the VHA.

Limitations

This survey had a low response rate (16.7%). One potential explanation for the low response rate is that when the survey was deployed, many of the VHA ED/UCC physicians were per-diem employees. Per-diem physicians may be less engaged and aware of site facilitators or barriers to naloxone and MAT prescribing. This, too, may have potentially skewed the collected data. However, the survey did not ask HCPs to disclose their employment status; thus, exact rates of per diem respondents are unknown.

We aimed to capture only self-perceived barriers to prescribing naloxone and MAT in the ED, but we did not capture or measure HCP respondent’s actual prescribing rates of MAT or naloxone. Understanding HCP perceptions of naloxone distribution and MAT initiation in the ED may have been further informed by comparing HCP responses to their actual clinical practice as related to their prescribing of these medications. In future research, we will link HCPs with the actual numbers of naloxone and MAT medications prescribed. Additionally, we do not know how many of these barriers or proposed facilitators will impact clinical practice.

Conclusions

A key aim for VHA leadership is to increase veteran access to naloxone distribution and MAT for OUD across clinical areas. The present study aimed to identify HCP perceptions of barriers and facilitators to the naloxone distribution and MAT-initiation programs in VHA ED/UCCs to inform the development of a targeted QI program to implement these opioid safety measures. Although the survey yielded a low response rate, results allowed us to identify important action items for our QI program, such as the development of clear protocols, follow-up plans, and systems for referral of care and HCP educational materials related to MAT and naloxone. We hope this work will serve as the basis for ED/UCC-tailored programs that can provide customized educational programs for HCPs designed to overcome known barriers to naloxone and MAT initiation.

Acknowledgments
This work was supported by the VA Office of Specialty Care Services 10P11 and through funding provided by the Comprehensive Addiction and Recovery Act (CARA).

The United States is facing an opioid crisis in which approximately 10 million people have misused opioids in the past year, and an estimated 2 million people have an opioid use disorder (OUD).¹ Compared with the general population, veterans treated in the Veterans Health Administration (VHA) facilities are at nearly twice the risk for accidental opioid overdose.² The implementation of opioid safety measures in VHA facilities across all care settings is a priority in addressing this public health crisis. Hence, VHA leadership is working to minimize veteran risk of fatal opioid overdoses and to increase veteran access to medication-assisted treatments (MAT) for OUD.³

Since the administration of our survey, the VHA has shifted to using the term medication for opioid use disorder (MOUD) instead of MAT for OUD. However, for consistency with the survey we distributed, we use MAT in this analysis.

Acute care settings represent an opportunity to offer appropriate opioid care and treatment options to patients at risk for OUD or opioid-related overdose. VHA facilities offer 2 outpatient acute care settings for emergent ambulatory care: emergency departments (EDs) and urgent care centers (UCCs). Annually, these settings see an estimated 2.5 million patients each year, making EDs and UCCs critical access points of OUD care for veterans. Partnering with key national VHA stakeholders from Pharmacy Benefits Management (PBM), the Office of Emergency Medicine, and Academic Detailing Services (ADS), we developed the Emergency Department Opioid Safety Initiative (ED OSI) aimed at implementing and evaluating opioid safety measures in VHA outpatient acute care settings.

The US Department of Veterans Affairs (VA)/Department of Defense (DoD) Clinical Practice Guidelines for Opioid Therapy for Chronic Pain (CPG) makes recommendations for the initiation and continuation of opioids, risk mitigation, taper of opioids, and opioid therapy for acute pain in VHA facilities.⁴ Using these recommendations, we developed the broad aims of the ED OSI quality improvement (QI) program. The CPG is clear about the prioritization of safe opioid prescribing practices. New opioid prescriptions written in the ED have been associated with continued and chronic opioid use.⁵ At the time of prescription, patients not currently and chronically on opioids who receive more than a 3-day supply are at increased risk of becoming long-term opioid users.⁶ Given the annual volume of patients seen, VHA ED/UCCs are a crucial area for implementing better opioid prescribing practices.

The CPG also includes recommendations for the prescribing or coprescribing of naloxone rescue kits. The administration of naloxone following opioid overdose has been found to be an effective measure against fatal overdose. Increasing provider awareness of common risk factors for opioid-related overdose (eg, frequent ED visits or hospitalizations) helps facilitate a discussion on naloxone prescribing at discharge. Prior studies provide evidence that naloxone distribution and accompanying education also are effective in reducing opioid overdose mortalityand ED visits related to adverse opioid-related events.^7,8

Similarly, the guidelines provide recommendations for the use of MAT for veterans with OUD. MAT for OUD is considered a first-line treatment option for patients with moderate-to-severe OUD. When used to treat patients with unsafe opioid use, this treatment helps alleviate symptoms of withdrawal, which can increase opioid taper adherence and has a protective effect against opioid overdose mortality.⁹ MAT initiated in the ED can increase patient engagement to addiction services.¹⁰

These 3 CPG recommendations serve as the basis for the broad goals of the ED OSI program. We aim to develop, implement, and evaluate programs and initiatives to (aim 1) reduce inappropriate opioid prescribing from VHA EDs; (aim 2) increase naloxone distribution from VHA EDs; and (aim 3) increase access to MAT initiation from VHA EDs through the implementation of ED-based MAT-initiation programs with EDs across the VHA. Aim 1 was a focused and strategic QI effort to implement an ED-based program to reduce inappropriate opioid prescribing. The ED OSI prescribing program offered a 4-step bundled approach: (1) sharing of opioid prescribing dashboard data with ED medical director and academic detailer; (2) education of ED providers and implementation of toolkit resources; (3) academic detailers conduct audit and feedback session(s) with highest prescribers; and (4) quarterly reports of opioid prescribing data to ED providers.

Results from the pilot suggested that our program was associated with accelerating the rate at which ED prescribing rates decreased.¹¹ In addition, the pilot found that ED-based QI initiatives in VHA facilities are a feasible practice. As we work to develop and implement the next 2 phases of the QI program, a major consideration is to identify facilitators and address any existing barriers to the implementation of naloxone distribution (aim 2) and MAT-initiation (aim 3) programs for treatment-naïve patients from VHA EDs. To date, there have been no recent published studies examining the barriers and facilitators to use or implementation of MAT initiation or naloxone distribution in VHA facilities or, more specifically, from VHA EDs.¹² As part of our QI program, we set out to better understand VHA ED provider perceptions of barriers and facilitators to implementation of programs aimed at increasing naloxone distribution and initiation of MAT for treatment-naïve patients in the ED.

Methods 

This project received a QI designation from the Office of PBM Academic Detailing Service Institutional Review Board at the Edward Hines, Jr. Veterans Affairs Hospital VA Medical Center (VAMC). This designation was reviewed and approved by the Rocky Mountain Regional VAMC Research and Development service. In addition, we received national union approval to disseminate this survey nationally across all VA Integrated Service Networks (VISNs).

Survey

We worked with VHA subject matter experts, key stakeholders, and the VA Collaborative Evaluation Center (VACE) to develop the survey. Subject matter experts and stakeholders included VHA emergency medicine leadership, ADS leadership, and mental health and substance treatment providers. VACE is an interdisciplinary group of mixed-method researchers. The survey questions aimed to capture perceptions and experiences regarding naloxone distribution and new MAT initiation of VHA ED/UCC providers.

We used a variety of survey question formats. Close-ended questions with a predefined list of answer options were used to capture discrete domains, such as demographic information, comfort level, and experience level. To capture health care provider (HCP) perceptions on barriers and facilitators, we used multiple-answer multiple-choice questions. Built into this question format was a free-response option, which allowed respondents to offer additional barriers or facilitators. Respondents also had the option of not answering individual questions.

We identified physicians, nurse practitioners (NPs), and physician assistants (PAs) who saw at least 100 patients in the ED or UCC in at least one 3-month period in the prior year and obtained an email address for each. In total, 2228 ED or UCC providers across 132 facilities were emailed a survey; 1883 (84.5%) were ED providers and 345 (15.5%) were UCC providers.

We used Research Electronic Data Capture (REDCap) software to build and disseminate the survey via email. Surveys were initially disseminated in late January 2019. During the 3-month survey period, recipients received 3 automated email reminders from REDCap to complete the survey. Survey data were exported from REDCap. Results were analyzed using descriptive statistics analyses with Microsoft Excel.

Results 

One respondent received the survey in error and was excluded from the analysis. The survey response rate was 16.7%: 372 responses from 103 unique facilities. Each VISN had a mean 20 respondents. The majority of respondents (n = 286, 76.9%) worked in highly complex level 1 facilities characterized by high patient volume and more high-risk patients and were teaching and research facilities. Respondents were asked to describe their most recent ED or UCC role. While 281 respondents (75.5%) were medical doctors, 61 respondents (16.4%) were NPs, 30 (8.1%) were PAs, and 26 (7.0%) were ED/UCC chiefs or medical directors (Table 1). Most respondents (80.4%) reported at least 10 years of health care experience.

The majority of respondents (72.9%) believed that HCPs at their VHA facility should be prescribing naloxone. When asked to specify which HCPs should be prescribing naloxone, most HCP respondents selected pharmacists (76.4%) and substance abuse providers (71.6%). Less than half of respondents (45.0%) felt that VA ED/UCC providers also should be prescribing naloxone. However, 58.1% of most HCP respondents reported being comfortable or very comfortable with prescribing naloxone to a patient in the ED or UCC who already had an existing prescription of opioids. Similarly, 52.7% of respondents reported being comfortable or very comfortable with coprescribing naloxone when discharging a patient with an opioid prescription from the ED/UCC. Notably, while 36.7% of PAs reported being comfortable/very comfortable coprescribing naloxone, 46.7% reported being comfortable/very comfortable prescribing naloxone to a patient with an existing opioid prescription. Physicians and NPs expressed similar levels of comfort with coprescribing and prescribing naloxone.

Respondents across provider types indicated a number of barriers to prescribing naloxone to medically appropriate patients (Table 2). Many respondents indicated prescribing naloxone was beyond the ED/UCC provider scope of practice (35.2%), followed by the perceived stigma associated with naloxone (33.3%), time required to prescribe naloxone (23.9%), and concern with patient’s ability to use naloxone (22.8%).

Discussion

To the best of our knowledge, there have not been any studies examining HCP perceptions of the barriers and facilitators to naloxone distribution or the initiation of MAT in VHA ED and UCCs. Veterans are at an increased risk of overdose when compared with the general population, and increasing access to opioid safety measures (eg, safer prescribing practices, naloxone distribution) and treatment with MAT for OUD across all clinical settings has been a VHA priority.³

National guidance from VHA leadership, the Centers for Disease Control and Prevention (CDC), the US Surgeon General, and the US Department of Health and Human Services (HHS) call for an all-hands-on-deck approach to combatting opioid overdose with naloxone distribution or MAT (such as buprenorphine) initiation.¹³ VHA ED and UCC settings provide acute outpatient care to patients with medical or psychiatric illnesses or injuries that the patient believes requires emergent or immediate medical attention or for which there is a critical need for treatment to prevent deterioration of the condition or the possible impairment of recovery.¹⁴ However, ED and UCC environments are often regarded as settings meant to stabilize a patient until they can be seen by a primary care or long-term care provider.

A major barrier identified by HCPs was that MAT for OUD was outside their ED/UCC scope of practice, which suggests a need for a top-down or peer-to-peer reexamination of the role of HCPs in ED/UCC settings. Any naloxone distribution and/or MAT-initiation program in VHA ED/UCCs should consider education about the role of ED/UCC HCPs in opioid safety and treatment. According to a VHA Support Service Center (VSSC) employee report database, in fiscal year 2018, per diem/fee-basis and contract HCPs comprised nearly 40% of clinical emergency medicine physician full-time equivalent employees, which presents a unique barrier to HCP education. Fee-basis and per diem HCPs may be less aware of, engaged in, or committed to VHA goals. Additionally, short-term HCPs may have fewer opportunities for training and education regarding naloxone or MAT use.

Only 25.3% of HCPs reported that their facility leadership was supportive or very supportive of MAT prescribing. This suggests that facility leadership should be engaged in any efforts to implement a MAT-initiation program in the facility’s ED. Engaging leadership in efforts to implement ED-based MAT programs will allow for a better understanding of leadership goals as related to opioid safety and an opportunity to address concerns regarding prescribing MAT in the ED. We recommend engaging facility leadership early in MAT implementation efforts. Respectively, 12.4% and 28.2% of HCP respondents reported discomfort prescribing naloxone or using MAT, suggesting a need for more education. Similarly, only 6.8% of HCPs reported comfort with using MAT.

A consideration for implementing ED/UCC-based MAT should be the inclusion of a training component. An evidence-based clinical treatment pathway that is appropriate to the ED/UCC setting and facility on the administration of MAT also could be beneficial. A clinical treatment pathway that includes ED/UCC-initiated discharge recommendations would address HCP concerns of unclear follow-up plans and system for referral of care. To this end, a key implementation task is coordinating with other outpatient services (eg, pain management clinic, substance use disorder treatment clinic) equipped for long-term patient follow-up to develop a system for referral of care. For example, as part of the clinical treatment pathway, an ED can develop a system of referral for patients initiated on MAT in the ED in which patients are referred for follow-up at the facility’s substance use disorder treatment clinic to be seen within 72 hours to continue the administration of MAT (such as buprenorphine).

In addition to HCP education, results suggest that patient/veteran education regarding naloxone and/or MAT should be considered. HCPs indicated that having help from a pharmacist to educate the patient about the medications would be a facilitator to naloxone distribution and MAT initiation. Similarly, patient knowledge of the medications also was endorsed as a facilitator. As such, a consideration for any future ED/UCC-based naloxone distribution or MAT-initiation programs in the VHA should be patient education whether by a clinically trained professional or an educational campaign for veterans.

Expanded naloxone distribution and initiation of MAT for OUD for EDs/UCCs across the VHA could impact the lives of veterans on long-term opioid therapy, with OUD, or who are otherwise at risk for opioid overdose. Steps taken to address the barriers and leverage the facilitators identified by HCP respondents can greatly reduce current obstacles to widespread implementation of ED/UCC-based naloxone distribution and MAT initiation nationally within the VHA.

Limitations

This survey had a low response rate (16.7%). One potential explanation for the low response rate is that when the survey was deployed, many of the VHA ED/UCC physicians were per-diem employees. Per-diem physicians may be less engaged and aware of site facilitators or barriers to naloxone and MAT prescribing. This, too, may have potentially skewed the collected data. However, the survey did not ask HCPs to disclose their employment status; thus, exact rates of per diem respondents are unknown.

We aimed to capture only self-perceived barriers to prescribing naloxone and MAT in the ED, but we did not capture or measure HCP respondent’s actual prescribing rates of MAT or naloxone. Understanding HCP perceptions of naloxone distribution and MAT initiation in the ED may have been further informed by comparing HCP responses to their actual clinical practice as related to their prescribing of these medications. In future research, we will link HCPs with the actual numbers of naloxone and MAT medications prescribed. Additionally, we do not know how many of these barriers or proposed facilitators will impact clinical practice.

Conclusions

A key aim for VHA leadership is to increase veteran access to naloxone distribution and MAT for OUD across clinical areas. The present study aimed to identify HCP perceptions of barriers and facilitators to the naloxone distribution and MAT-initiation programs in VHA ED/UCCs to inform the development of a targeted QI program to implement these opioid safety measures. Although the survey yielded a low response rate, results allowed us to identify important action items for our QI program, such as the development of clear protocols, follow-up plans, and systems for referral of care and HCP educational materials related to MAT and naloxone. We hope this work will serve as the basis for ED/UCC-tailored programs that can provide customized educational programs for HCPs designed to overcome known barriers to naloxone and MAT initiation.

Acknowledgments
This work was supported by the VA Office of Specialty Care Services 10P11 and through funding provided by the Comprehensive Addiction and Recovery Act (CARA).

Increasing antibiotic resistance is an urgent threat to public health and establishing a review service for antibiotics could alleviate this problem. As use of antibiotics escalates, the risk of resistance becomes increasingly important. Each year, approximately 269 million antibiotics are dispensed and at least 30% are prescribed inappropriately.¹ In addition to inappropriate prescribing, increased antibiotic resistance can be caused by patients not completing an antibiotic course as recommended or inherent bacterial mutations. According to the Centers for Disease Control and Prevention, each year approximately 3 million individuals contract an antibiotic-resistant infection.² By 2050, it is projected that drug-resistant conditions could cause 300 million deaths and might be as disastrous to the economy as the 2008 global financial crisis.³ Ensuring appropriate use of antibiotic therapy through antimicrobial stewardship can help combat this significant public health issue.

Antimicrobial stewardship promotes appropriate use of antimicrobials to improve patient outcomes, reduce health care costs, and decrease antimicrobial resistance. One study found that nearly 50% of patients discharged from the emergency department with antibiotics required therapy modification after culture and susceptibility results were returned.⁴ Both the Infectious Disease Society of America (IDSA) and the Society for Healthcare Epidemiology of America (SHEA) support incorporating a clinical pharmacist into culture reviews.³ Several institutions have implemented a pharmacist-led culture review service to improve antibiotic usage, which has shown positive results. A retrospective case-control study at University of Rochester Medical Center showed reduced time to positive culture review and to patient or health care provider (HCP) notification when emergency medicine pharmacists were involved in culture review.⁵ A retrospective study at Carolinas Medical Center-Northeast showed 12% decreased readmission rate using pharmacist-implemented culture review compared with HCP review.⁶ Results from previous studies showed an overall improvement in patient safety through decreased use of inappropriate agents and reduced time on inappropriate antibiotic therapy.

Establishing a pharmacist-led culture review service at the Carl Vinson Veterans Affairs Medical Center (CVVAMC) in Dublin, Georgia, could decrease the time to review of positive culture results, time to patient or HCP notification, and readmission rates. CVVAMC provides outpatient primary care services to about 30,000 veterans in the central and southern regions of Georgia. Our facility has executed an antimicrobial stewardship program based on guidelines published in 2016 by IDSA and SHEA to guide optimal use of antibiotics. Clinical pharmacists play an active role in antimicrobial stewardship throughout the facility. Clinical responsibilities of the antimicrobial stewardship pharmacist include assessing therapy for inappropriate dual anaerobic coverage, evaluating inpatient culture results within 48 hours, dosing and monitoring antibiotic therapy, including vancomycin and aminoglycosides, and implementing IV to by-mouth conversions for appropriate patients. HCPs involved with antimicrobial stewardship could order an array of tests to assess a veteran’s condition, including cultures, when an infection is suspected.

Culture results take about 3 to 5 days, then HCPs evaluate the result to ensure current antibiotic therapy is appropriate. Patients might not receive timely follow-up because HCPs often have many laboratory alerts to sift through every day, and a protocol is not in place for pharmacists to adjust outpatient antimicrobial regimens based on culture results. Before implementing this project, there was no outpatient service for pharmacists to impact culture and susceptibility review. This project was initiated because a lead physician identified difficulty reviewing culture and susceptibility results. HCPs often work on rotating schedules, and there was a concern about possible delay in follow-up of results if a HCP was not scheduled to work for a period of time.

The purpose of this project was to implement an outpatient, pharmacist-managed culture and susceptibility review service to improve patient outcomes, including decreasing and preventing inappropriate antibiotic use. The primary objective was to design and implement a pharmacist-led review service to intervene in cases of mismatched antibiotic bacteria combinations. Secondary objectives included identifying most common culture types and organisms encountered and intervened on at our facility.

Quality Improvement Project

This quality improvement project was approved by the CVVAMC Pharmacy and Therapeutics Committee. Members of the medical review board signed a care coordination agreement between pharmacy and outpatient HCPs to permit pharmacist interventions involving optimization of antibiotic therapy. This agreement allowed pharmacists to make changes to existing antimicrobial regimens within their scope of practice (SOP) without requiring discussion with HCPs. A protocol was also developed to guide pharmacist modification of antimicrobial therapy based on current antimicrobial guidelines.⁷ This protocol was based on commonly isolated organisms and local resistance patterns and provided guidance for antibiotic treatment based on culture type (ie, skin and soft tissue infection, urine, etc). Computerized Patient Record System (CPRS) note templates were also developed for interventions performed, and patient follow-up after antibiotic regimens were completed (eAppendix 1 

Program Inclusion

Veterans were included in this project if they presented to primary care or urgent care clinics for therapy; had positive culture and sensitivity results; and were prescribed an empiric antibiotic. Veterans were not eligible for this project if they were not receiving antibiotic therapy, with or without pending or resulted culture results shown in CPRS.

Implementation

Data gathered through a CPRS dashboard from August 2019 to February 2020 identified patients with pending or completed culture results in urgent care and primary care settings (eAppendix 4). The dashboard was created specifically for this project to show patient details that included initial antibiotic(s) prescribed and preliminary and final culture results. After a mismatched combination was identified, pharmacists contacted patients and assessed symptoms. If a patient was still symptomatic, the pharmacist changed the antibiotic regimen and educated the patient about this change. The pharmacist documented an intervention note in CPRS and added the HCP as a signer so he or she would be aware of the change. The clinical pharmacist followed up after regimens were complete. At this time, the pharmacist assessed patients to ensure the medication was taken as directed (eg, number of days of therapy, how many tablets per day, etc), to discuss any reported adverse effects, and to assess resolution of symptoms. If a patient still had symptoms, the pharmacist contacted the patient’s primary care provider. If the veteran could not be contacted after 3 consecutive attempts via phone, a certified letter was mailed. If patients were asymptomatic at the time of the call, the pharmacist documented the lack of symptoms and added the HCP as a signer for awareness purposes. HCPs continued to practice as usual while this service was implemented.

Observations

Using the culture and susceptibility dashboard, the pharmacist identified 675 patients as having a pending culture (Table 1). Among these patients, 320 results were positive, and were taking antibiotics empirically. Out of the 320 patients who met inclusion criteria, 10 required pharmacist intervention. After contacting the veterans, 7 required regimen changes because their current antibiotic was not susceptible to the isolated organism. Three additional patients were contacted because of a mismatch between the empiric antibiotic and culture result. Antibiotic therapy was not modified because these patients were asymptomatic at the time the clinical pharmacist contacted them. These patient cases were discussed with the HCP before documenting the intervention to prevent initiation of unwarranted antibiotics.

Most of the modified antimicrobial regimens were found in urine cultures from symptomatic patients (Table 2). Of the 7 patients requiring therapy change because of a mismatch antibiotic–bacteria combination, 4 were empirically prescribed fluoroquinolones, 2 received levofloxacin, and 2 were prescribed ciprofloxacin. According to the most recent antibiogram at our facility, some organisms are resistant to fluoroquinolones, specifically Proteus mirabilis (P mirabilis) and Escherichia coli (E coli). These pathogens were the cause of urinary tract infections in 3 of 4 patients with fluoroquinolone prescriptions.

Through the CPRS dashboard, the pharmacist inadvertently identified 4 patients with positive culture results who were not on antibiotic therapy. These patients were contacted by telephone, and antibiotics were initiated for symptomatic patients after consultation with the HCP. The primary culture type intervened on was urine in 12 of 14 cases (86%). The other 2 culture types included oropharynx culture (7%) positive for an acute bacterial respiratory tract infection caused by group C Streptococcus and a stool culture (7%) positive for Pseudomonas aeruginosa (P aeruginosa). E coli (36%) was isolated in 5 cases and was the most commonly isolated organism. P aeruginosa (29%) was identified in 4 cases. Other organisms included P mirabilis (14%) in 2 patients and streptococcus species (14%) in 2 cases. Enterococcus faecium (7%) was isolated in 1 case.

Discussion

This project was an innovative antimicrobial stewardship endeavor that helped initiate antibiotic interventions quickly and improve patient outcomes. The antimicrobial stewardship pharmacist independently performed interventions for patients without requiring HCP consultation, therefore decreasing HCP burden and possibly reducing time to assessment of culture results.

Limitations

The study results were limited due to its small sample size of antimicrobial interventions. The clinical pharmacist did not contact the patient when the antibiotic prescribed empirically by the HCP was appropriate for the isolated organism. Among the patients contacted, 3 were asymptomatic, did not require further antibiotic therapy, and no intervention was made. Provider education was deemed successful because HCPs did not request further information about the service. However, not all HCPs were provided education because of different shifts and inability to attend educational sessions. Closely working with lead physicians within the facility provided an alternate method for information dissemination.

The care coordination agreement allowed the pharmacist to make changes if patients had a current prescription for an antibiotic. In addition to the changes to antibiotics, this project improved HCP awareness of culture results even in cases of symptomatic patients who were not prescribed therapy. When this occurred, the pharmacist contacted the patient to assess symptoms and then notified the HCP if the patient was symptomatic.

Future Directions

Future endeavors regarding this project include modifying the scope of the service to allow pharmacists to prescribe antibiotics for patients with positive cultures and symptoms without empiric antibiotics in addition to continuing to modify empiric therapy. Additionally, improving dashboard efficiency through changes to include only isolated antibiotic mismatches rather than all antibiotics prescribed and all available cultures would reduce the pharmacists’ time commitment. Expanding to other parts of the medical center, including long-term care facilities and other outpatient clinics, would allow this service to reach more veterans. Integrating this service throughout the medical center will require continued HCP education and modifying care coordination agreements to include these facilities.

On a typical day, 60 to 90 minutes were spent navigating the dashboard and implementing this service. The CPRS dashboard should be modified to streamline patients identified to decrease the daily time commitment. Re-education of HCPs about resistance rates of fluoroquinolones and empirically prescribing these agents also will be completed based on empiric antibiotic interventions made with these agents throughout this project. Discussing HCP viewpoints on this service would be beneficial to ensure HCP satisfaction.

Conclusions

This pharmacy service and antimicrobial stewardship program reduced time patients were on inappropriate antibiotics. Pharmacists reviewed the dashboard daily under the scope of this project, which expedited needed changes and decreased provider burden because pharmacists were able to make changes without interrupting HCPs’ daily tasks, including patient care.

This program may also reduce readmissions. Patients who were still symptomatic were contacted could be given revised medication regimens without the patient returning to the facility for follow-up treatment. An interesting conclusion not included in the current scope of this service was possible reduced time to therapy initiation in cases of positive cultures and symptomatic patients without antibiotic therapy. If this occurred on the dashboard, patient’s symptoms could be assessed, and if symptoms were ongoing, the pharmacist contacted the HCP with a recommended antimicrobial therapy. In these cases, we were able to mail the antibiotic quickly, and many times, on the same day as this intervention through overnight mail. Implementation of a pharmacist-led antimicrobial review service has provided positive results overall for CVVAMC.

Acknowledgment
This material is the result of work supported with resources and the use of the facilities at the Carl Vinson VA Medical Center.

Increasing antibiotic resistance is an urgent threat to public health and establishing a review service for antibiotics could alleviate this problem. As use of antibiotics escalates, the risk of resistance becomes increasingly important. Each year, approximately 269 million antibiotics are dispensed and at least 30% are prescribed inappropriately.¹ In addition to inappropriate prescribing, increased antibiotic resistance can be caused by patients not completing an antibiotic course as recommended or inherent bacterial mutations. According to the Centers for Disease Control and Prevention, each year approximately 3 million individuals contract an antibiotic-resistant infection.² By 2050, it is projected that drug-resistant conditions could cause 300 million deaths and might be as disastrous to the economy as the 2008 global financial crisis.³ Ensuring appropriate use of antibiotic therapy through antimicrobial stewardship can help combat this significant public health issue.

Antimicrobial stewardship promotes appropriate use of antimicrobials to improve patient outcomes, reduce health care costs, and decrease antimicrobial resistance. One study found that nearly 50% of patients discharged from the emergency department with antibiotics required therapy modification after culture and susceptibility results were returned.⁴ Both the Infectious Disease Society of America (IDSA) and the Society for Healthcare Epidemiology of America (SHEA) support incorporating a clinical pharmacist into culture reviews.³ Several institutions have implemented a pharmacist-led culture review service to improve antibiotic usage, which has shown positive results. A retrospective case-control study at University of Rochester Medical Center showed reduced time to positive culture review and to patient or health care provider (HCP) notification when emergency medicine pharmacists were involved in culture review.⁵ A retrospective study at Carolinas Medical Center-Northeast showed 12% decreased readmission rate using pharmacist-implemented culture review compared with HCP review.⁶ Results from previous studies showed an overall improvement in patient safety through decreased use of inappropriate agents and reduced time on inappropriate antibiotic therapy.

Establishing a pharmacist-led culture review service at the Carl Vinson Veterans Affairs Medical Center (CVVAMC) in Dublin, Georgia, could decrease the time to review of positive culture results, time to patient or HCP notification, and readmission rates. CVVAMC provides outpatient primary care services to about 30,000 veterans in the central and southern regions of Georgia. Our facility has executed an antimicrobial stewardship program based on guidelines published in 2016 by IDSA and SHEA to guide optimal use of antibiotics. Clinical pharmacists play an active role in antimicrobial stewardship throughout the facility. Clinical responsibilities of the antimicrobial stewardship pharmacist include assessing therapy for inappropriate dual anaerobic coverage, evaluating inpatient culture results within 48 hours, dosing and monitoring antibiotic therapy, including vancomycin and aminoglycosides, and implementing IV to by-mouth conversions for appropriate patients. HCPs involved with antimicrobial stewardship could order an array of tests to assess a veteran’s condition, including cultures, when an infection is suspected.

Culture results take about 3 to 5 days, then HCPs evaluate the result to ensure current antibiotic therapy is appropriate. Patients might not receive timely follow-up because HCPs often have many laboratory alerts to sift through every day, and a protocol is not in place for pharmacists to adjust outpatient antimicrobial regimens based on culture results. Before implementing this project, there was no outpatient service for pharmacists to impact culture and susceptibility review. This project was initiated because a lead physician identified difficulty reviewing culture and susceptibility results. HCPs often work on rotating schedules, and there was a concern about possible delay in follow-up of results if a HCP was not scheduled to work for a period of time.

The purpose of this project was to implement an outpatient, pharmacist-managed culture and susceptibility review service to improve patient outcomes, including decreasing and preventing inappropriate antibiotic use. The primary objective was to design and implement a pharmacist-led review service to intervene in cases of mismatched antibiotic bacteria combinations. Secondary objectives included identifying most common culture types and organisms encountered and intervened on at our facility.

Quality Improvement Project

This quality improvement project was approved by the CVVAMC Pharmacy and Therapeutics Committee. Members of the medical review board signed a care coordination agreement between pharmacy and outpatient HCPs to permit pharmacist interventions involving optimization of antibiotic therapy. This agreement allowed pharmacists to make changes to existing antimicrobial regimens within their scope of practice (SOP) without requiring discussion with HCPs. A protocol was also developed to guide pharmacist modification of antimicrobial therapy based on current antimicrobial guidelines.⁷ This protocol was based on commonly isolated organisms and local resistance patterns and provided guidance for antibiotic treatment based on culture type (ie, skin and soft tissue infection, urine, etc). Computerized Patient Record System (CPRS) note templates were also developed for interventions performed, and patient follow-up after antibiotic regimens were completed (eAppendix 1 

Program Inclusion

Veterans were included in this project if they presented to primary care or urgent care clinics for therapy; had positive culture and sensitivity results; and were prescribed an empiric antibiotic. Veterans were not eligible for this project if they were not receiving antibiotic therapy, with or without pending or resulted culture results shown in CPRS.

Implementation

Data gathered through a CPRS dashboard from August 2019 to February 2020 identified patients with pending or completed culture results in urgent care and primary care settings (eAppendix 4). The dashboard was created specifically for this project to show patient details that included initial antibiotic(s) prescribed and preliminary and final culture results. After a mismatched combination was identified, pharmacists contacted patients and assessed symptoms. If a patient was still symptomatic, the pharmacist changed the antibiotic regimen and educated the patient about this change. The pharmacist documented an intervention note in CPRS and added the HCP as a signer so he or she would be aware of the change. The clinical pharmacist followed up after regimens were complete. At this time, the pharmacist assessed patients to ensure the medication was taken as directed (eg, number of days of therapy, how many tablets per day, etc), to discuss any reported adverse effects, and to assess resolution of symptoms. If a patient still had symptoms, the pharmacist contacted the patient’s primary care provider. If the veteran could not be contacted after 3 consecutive attempts via phone, a certified letter was mailed. If patients were asymptomatic at the time of the call, the pharmacist documented the lack of symptoms and added the HCP as a signer for awareness purposes. HCPs continued to practice as usual while this service was implemented.

Observations

Using the culture and susceptibility dashboard, the pharmacist identified 675 patients as having a pending culture (Table 1). Among these patients, 320 results were positive, and were taking antibiotics empirically. Out of the 320 patients who met inclusion criteria, 10 required pharmacist intervention. After contacting the veterans, 7 required regimen changes because their current antibiotic was not susceptible to the isolated organism. Three additional patients were contacted because of a mismatch between the empiric antibiotic and culture result. Antibiotic therapy was not modified because these patients were asymptomatic at the time the clinical pharmacist contacted them. These patient cases were discussed with the HCP before documenting the intervention to prevent initiation of unwarranted antibiotics.

Most of the modified antimicrobial regimens were found in urine cultures from symptomatic patients (Table 2). Of the 7 patients requiring therapy change because of a mismatch antibiotic–bacteria combination, 4 were empirically prescribed fluoroquinolones, 2 received levofloxacin, and 2 were prescribed ciprofloxacin. According to the most recent antibiogram at our facility, some organisms are resistant to fluoroquinolones, specifically Proteus mirabilis (P mirabilis) and Escherichia coli (E coli). These pathogens were the cause of urinary tract infections in 3 of 4 patients with fluoroquinolone prescriptions.

Through the CPRS dashboard, the pharmacist inadvertently identified 4 patients with positive culture results who were not on antibiotic therapy. These patients were contacted by telephone, and antibiotics were initiated for symptomatic patients after consultation with the HCP. The primary culture type intervened on was urine in 12 of 14 cases (86%). The other 2 culture types included oropharynx culture (7%) positive for an acute bacterial respiratory tract infection caused by group C Streptococcus and a stool culture (7%) positive for Pseudomonas aeruginosa (P aeruginosa). E coli (36%) was isolated in 5 cases and was the most commonly isolated organism. P aeruginosa (29%) was identified in 4 cases. Other organisms included P mirabilis (14%) in 2 patients and streptococcus species (14%) in 2 cases. Enterococcus faecium (7%) was isolated in 1 case.

Discussion

This project was an innovative antimicrobial stewardship endeavor that helped initiate antibiotic interventions quickly and improve patient outcomes. The antimicrobial stewardship pharmacist independently performed interventions for patients without requiring HCP consultation, therefore decreasing HCP burden and possibly reducing time to assessment of culture results.

Limitations

The study results were limited due to its small sample size of antimicrobial interventions. The clinical pharmacist did not contact the patient when the antibiotic prescribed empirically by the HCP was appropriate for the isolated organism. Among the patients contacted, 3 were asymptomatic, did not require further antibiotic therapy, and no intervention was made. Provider education was deemed successful because HCPs did not request further information about the service. However, not all HCPs were provided education because of different shifts and inability to attend educational sessions. Closely working with lead physicians within the facility provided an alternate method for information dissemination.

The care coordination agreement allowed the pharmacist to make changes if patients had a current prescription for an antibiotic. In addition to the changes to antibiotics, this project improved HCP awareness of culture results even in cases of symptomatic patients who were not prescribed therapy. When this occurred, the pharmacist contacted the patient to assess symptoms and then notified the HCP if the patient was symptomatic.

Future Directions

Future endeavors regarding this project include modifying the scope of the service to allow pharmacists to prescribe antibiotics for patients with positive cultures and symptoms without empiric antibiotics in addition to continuing to modify empiric therapy. Additionally, improving dashboard efficiency through changes to include only isolated antibiotic mismatches rather than all antibiotics prescribed and all available cultures would reduce the pharmacists’ time commitment. Expanding to other parts of the medical center, including long-term care facilities and other outpatient clinics, would allow this service to reach more veterans. Integrating this service throughout the medical center will require continued HCP education and modifying care coordination agreements to include these facilities.

On a typical day, 60 to 90 minutes were spent navigating the dashboard and implementing this service. The CPRS dashboard should be modified to streamline patients identified to decrease the daily time commitment. Re-education of HCPs about resistance rates of fluoroquinolones and empirically prescribing these agents also will be completed based on empiric antibiotic interventions made with these agents throughout this project. Discussing HCP viewpoints on this service would be beneficial to ensure HCP satisfaction.

Conclusions

This pharmacy service and antimicrobial stewardship program reduced time patients were on inappropriate antibiotics. Pharmacists reviewed the dashboard daily under the scope of this project, which expedited needed changes and decreased provider burden because pharmacists were able to make changes without interrupting HCPs’ daily tasks, including patient care.

This program may also reduce readmissions. Patients who were still symptomatic were contacted could be given revised medication regimens without the patient returning to the facility for follow-up treatment. An interesting conclusion not included in the current scope of this service was possible reduced time to therapy initiation in cases of positive cultures and symptomatic patients without antibiotic therapy. If this occurred on the dashboard, patient’s symptoms could be assessed, and if symptoms were ongoing, the pharmacist contacted the HCP with a recommended antimicrobial therapy. In these cases, we were able to mail the antibiotic quickly, and many times, on the same day as this intervention through overnight mail. Implementation of a pharmacist-led antimicrobial review service has provided positive results overall for CVVAMC.

Acknowledgment
This material is the result of work supported with resources and the use of the facilities at the Carl Vinson VA Medical Center.

Increasing antibiotic resistance is an urgent threat to public health and establishing a review service for antibiotics could alleviate this problem. As use of antibiotics escalates, the risk of resistance becomes increasingly important. Each year, approximately 269 million antibiotics are dispensed and at least 30% are prescribed inappropriately.¹ In addition to inappropriate prescribing, increased antibiotic resistance can be caused by patients not completing an antibiotic course as recommended or inherent bacterial mutations. According to the Centers for Disease Control and Prevention, each year approximately 3 million individuals contract an antibiotic-resistant infection.² By 2050, it is projected that drug-resistant conditions could cause 300 million deaths and might be as disastrous to the economy as the 2008 global financial crisis.³ Ensuring appropriate use of antibiotic therapy through antimicrobial stewardship can help combat this significant public health issue.

Antimicrobial stewardship promotes appropriate use of antimicrobials to improve patient outcomes, reduce health care costs, and decrease antimicrobial resistance. One study found that nearly 50% of patients discharged from the emergency department with antibiotics required therapy modification after culture and susceptibility results were returned.⁴ Both the Infectious Disease Society of America (IDSA) and the Society for Healthcare Epidemiology of America (SHEA) support incorporating a clinical pharmacist into culture reviews.³ Several institutions have implemented a pharmacist-led culture review service to improve antibiotic usage, which has shown positive results. A retrospective case-control study at University of Rochester Medical Center showed reduced time to positive culture review and to patient or health care provider (HCP) notification when emergency medicine pharmacists were involved in culture review.⁵ A retrospective study at Carolinas Medical Center-Northeast showed 12% decreased readmission rate using pharmacist-implemented culture review compared with HCP review.⁶ Results from previous studies showed an overall improvement in patient safety through decreased use of inappropriate agents and reduced time on inappropriate antibiotic therapy.

Establishing a pharmacist-led culture review service at the Carl Vinson Veterans Affairs Medical Center (CVVAMC) in Dublin, Georgia, could decrease the time to review of positive culture results, time to patient or HCP notification, and readmission rates. CVVAMC provides outpatient primary care services to about 30,000 veterans in the central and southern regions of Georgia. Our facility has executed an antimicrobial stewardship program based on guidelines published in 2016 by IDSA and SHEA to guide optimal use of antibiotics. Clinical pharmacists play an active role in antimicrobial stewardship throughout the facility. Clinical responsibilities of the antimicrobial stewardship pharmacist include assessing therapy for inappropriate dual anaerobic coverage, evaluating inpatient culture results within 48 hours, dosing and monitoring antibiotic therapy, including vancomycin and aminoglycosides, and implementing IV to by-mouth conversions for appropriate patients. HCPs involved with antimicrobial stewardship could order an array of tests to assess a veteran’s condition, including cultures, when an infection is suspected.

Culture results take about 3 to 5 days, then HCPs evaluate the result to ensure current antibiotic therapy is appropriate. Patients might not receive timely follow-up because HCPs often have many laboratory alerts to sift through every day, and a protocol is not in place for pharmacists to adjust outpatient antimicrobial regimens based on culture results. Before implementing this project, there was no outpatient service for pharmacists to impact culture and susceptibility review. This project was initiated because a lead physician identified difficulty reviewing culture and susceptibility results. HCPs often work on rotating schedules, and there was a concern about possible delay in follow-up of results if a HCP was not scheduled to work for a period of time.

The purpose of this project was to implement an outpatient, pharmacist-managed culture and susceptibility review service to improve patient outcomes, including decreasing and preventing inappropriate antibiotic use. The primary objective was to design and implement a pharmacist-led review service to intervene in cases of mismatched antibiotic bacteria combinations. Secondary objectives included identifying most common culture types and organisms encountered and intervened on at our facility.

Quality Improvement Project

This quality improvement project was approved by the CVVAMC Pharmacy and Therapeutics Committee. Members of the medical review board signed a care coordination agreement between pharmacy and outpatient HCPs to permit pharmacist interventions involving optimization of antibiotic therapy. This agreement allowed pharmacists to make changes to existing antimicrobial regimens within their scope of practice (SOP) without requiring discussion with HCPs. A protocol was also developed to guide pharmacist modification of antimicrobial therapy based on current antimicrobial guidelines.⁷ This protocol was based on commonly isolated organisms and local resistance patterns and provided guidance for antibiotic treatment based on culture type (ie, skin and soft tissue infection, urine, etc). Computerized Patient Record System (CPRS) note templates were also developed for interventions performed, and patient follow-up after antibiotic regimens were completed (eAppendix 1 

Program Inclusion

Veterans were included in this project if they presented to primary care or urgent care clinics for therapy; had positive culture and sensitivity results; and were prescribed an empiric antibiotic. Veterans were not eligible for this project if they were not receiving antibiotic therapy, with or without pending or resulted culture results shown in CPRS.

Implementation

Data gathered through a CPRS dashboard from August 2019 to February 2020 identified patients with pending or completed culture results in urgent care and primary care settings (eAppendix 4). The dashboard was created specifically for this project to show patient details that included initial antibiotic(s) prescribed and preliminary and final culture results. After a mismatched combination was identified, pharmacists contacted patients and assessed symptoms. If a patient was still symptomatic, the pharmacist changed the antibiotic regimen and educated the patient about this change. The pharmacist documented an intervention note in CPRS and added the HCP as a signer so he or she would be aware of the change. The clinical pharmacist followed up after regimens were complete. At this time, the pharmacist assessed patients to ensure the medication was taken as directed (eg, number of days of therapy, how many tablets per day, etc), to discuss any reported adverse effects, and to assess resolution of symptoms. If a patient still had symptoms, the pharmacist contacted the patient’s primary care provider. If the veteran could not be contacted after 3 consecutive attempts via phone, a certified letter was mailed. If patients were asymptomatic at the time of the call, the pharmacist documented the lack of symptoms and added the HCP as a signer for awareness purposes. HCPs continued to practice as usual while this service was implemented.

Observations

Using the culture and susceptibility dashboard, the pharmacist identified 675 patients as having a pending culture (Table 1). Among these patients, 320 results were positive, and were taking antibiotics empirically. Out of the 320 patients who met inclusion criteria, 10 required pharmacist intervention. After contacting the veterans, 7 required regimen changes because their current antibiotic was not susceptible to the isolated organism. Three additional patients were contacted because of a mismatch between the empiric antibiotic and culture result. Antibiotic therapy was not modified because these patients were asymptomatic at the time the clinical pharmacist contacted them. These patient cases were discussed with the HCP before documenting the intervention to prevent initiation of unwarranted antibiotics.

Most of the modified antimicrobial regimens were found in urine cultures from symptomatic patients (Table 2). Of the 7 patients requiring therapy change because of a mismatch antibiotic–bacteria combination, 4 were empirically prescribed fluoroquinolones, 2 received levofloxacin, and 2 were prescribed ciprofloxacin. According to the most recent antibiogram at our facility, some organisms are resistant to fluoroquinolones, specifically Proteus mirabilis (P mirabilis) and Escherichia coli (E coli). These pathogens were the cause of urinary tract infections in 3 of 4 patients with fluoroquinolone prescriptions.

Through the CPRS dashboard, the pharmacist inadvertently identified 4 patients with positive culture results who were not on antibiotic therapy. These patients were contacted by telephone, and antibiotics were initiated for symptomatic patients after consultation with the HCP. The primary culture type intervened on was urine in 12 of 14 cases (86%). The other 2 culture types included oropharynx culture (7%) positive for an acute bacterial respiratory tract infection caused by group C Streptococcus and a stool culture (7%) positive for Pseudomonas aeruginosa (P aeruginosa). E coli (36%) was isolated in 5 cases and was the most commonly isolated organism. P aeruginosa (29%) was identified in 4 cases. Other organisms included P mirabilis (14%) in 2 patients and streptococcus species (14%) in 2 cases. Enterococcus faecium (7%) was isolated in 1 case.

Discussion

This project was an innovative antimicrobial stewardship endeavor that helped initiate antibiotic interventions quickly and improve patient outcomes. The antimicrobial stewardship pharmacist independently performed interventions for patients without requiring HCP consultation, therefore decreasing HCP burden and possibly reducing time to assessment of culture results.

Limitations

The study results were limited due to its small sample size of antimicrobial interventions. The clinical pharmacist did not contact the patient when the antibiotic prescribed empirically by the HCP was appropriate for the isolated organism. Among the patients contacted, 3 were asymptomatic, did not require further antibiotic therapy, and no intervention was made. Provider education was deemed successful because HCPs did not request further information about the service. However, not all HCPs were provided education because of different shifts and inability to attend educational sessions. Closely working with lead physicians within the facility provided an alternate method for information dissemination.

The care coordination agreement allowed the pharmacist to make changes if patients had a current prescription for an antibiotic. In addition to the changes to antibiotics, this project improved HCP awareness of culture results even in cases of symptomatic patients who were not prescribed therapy. When this occurred, the pharmacist contacted the patient to assess symptoms and then notified the HCP if the patient was symptomatic.

Future Directions

Future endeavors regarding this project include modifying the scope of the service to allow pharmacists to prescribe antibiotics for patients with positive cultures and symptoms without empiric antibiotics in addition to continuing to modify empiric therapy. Additionally, improving dashboard efficiency through changes to include only isolated antibiotic mismatches rather than all antibiotics prescribed and all available cultures would reduce the pharmacists’ time commitment. Expanding to other parts of the medical center, including long-term care facilities and other outpatient clinics, would allow this service to reach more veterans. Integrating this service throughout the medical center will require continued HCP education and modifying care coordination agreements to include these facilities.

On a typical day, 60 to 90 minutes were spent navigating the dashboard and implementing this service. The CPRS dashboard should be modified to streamline patients identified to decrease the daily time commitment. Re-education of HCPs about resistance rates of fluoroquinolones and empirically prescribing these agents also will be completed based on empiric antibiotic interventions made with these agents throughout this project. Discussing HCP viewpoints on this service would be beneficial to ensure HCP satisfaction.

Conclusions

This pharmacy service and antimicrobial stewardship program reduced time patients were on inappropriate antibiotics. Pharmacists reviewed the dashboard daily under the scope of this project, which expedited needed changes and decreased provider burden because pharmacists were able to make changes without interrupting HCPs’ daily tasks, including patient care.

This program may also reduce readmissions. Patients who were still symptomatic were contacted could be given revised medication regimens without the patient returning to the facility for follow-up treatment. An interesting conclusion not included in the current scope of this service was possible reduced time to therapy initiation in cases of positive cultures and symptomatic patients without antibiotic therapy. If this occurred on the dashboard, patient’s symptoms could be assessed, and if symptoms were ongoing, the pharmacist contacted the HCP with a recommended antimicrobial therapy. In these cases, we were able to mail the antibiotic quickly, and many times, on the same day as this intervention through overnight mail. Implementation of a pharmacist-led antimicrobial review service has provided positive results overall for CVVAMC.

Acknowledgment
This material is the result of work supported with resources and the use of the facilities at the Carl Vinson VA Medical Center.

“By learning you will teach, by teaching you will learn.”

–Latin proverb

The COVID-19 pandemic thrust hospitalists into uncertain clinical situations where scientific evidence was rapidly changing and expert consensus was not always available. Amidst this, our learners were eager to better understand this new disease and how to properly care for patients. This forced hospitalists, as educators, to face the question: “How do you teach when you don’t have the answers?”

Teaching outside a hospitalist’s expertise existed before the COVID-19 pandemic and will continue to exist. It is a challenge encountered frequently by both junior and senior faculty across all disciplines, yet is rarely discussed.¹ However, great learning can still occur when we teach at the edge of our comfort zones.

You don’t need to be an expert to be a great teacher. Although most educators know this, we often fear that disclosing our knowledge limitations exposes our weaknesses. But a successful start to the learning journey begins with establishing trust and confidence with your learners. Remaining authentic in your knowledge base will inspire more credibility than false pretenses of content mastery. Phrases like, “This topic is new for me as well. Here’s what I do know and what I don’t know” or “What a great question. I wish I had a great answer. Let me get back to you” set a standard for honesty and reduce teaching pressures. In turn, learners will be more comfortable acknowledging their own uncertainties and will be more likely to voice their hesitations or ask questions on rounds.

The field of medicine is steeped in hierarchical structure, where the attending is assumed to have the most knowledge. But this may not always be true, as learners are often more up to date on a subject than the attending. By reexamining traditional hierarchies and instead considering ourselves as part of a learning team, we can promote a more positive educational climate.

When a learner asks a question that you don’t have an answer for, the response “Great question. I can tell you what I think, but I’m interested in first hearing your thoughts” reflects that you respect your learners and their skills and experiences. You can also ask them to do a literature review and report back to you and the team the next morning. By inverting the hierarchy, you are teaching humility, adaptability, and shared responsibility, as well as demonstrating the skills of being a lifelong learner.²

As educators, we often hold ourselves to unrealistic expectations of being omniscient knowledge vessels. In times of crisis or uncertainty, teaching about how to learn and where to learn become just as important as what to learn. Invite learners to observe how you navigate ambiguity. For example, I recently interacted with a colleague on an unfamiliar case. She said, “Dr Wang, I don’t know much about malaria. Can you share with me what made you consider this diagnosis?” Additionally, admitting to learners when you have made an error not only clarifies their learning, but also role models continuous personal improvement.

By modeling humility by acknowledging our own limits, respecting our learners’ knowledge and experiences, and demonstrating how we manage uncertainty, we can enhance the learning environment and inspire our learners.

“By learning you will teach, by teaching you will learn.”

–Latin proverb

The COVID-19 pandemic thrust hospitalists into uncertain clinical situations where scientific evidence was rapidly changing and expert consensus was not always available. Amidst this, our learners were eager to better understand this new disease and how to properly care for patients. This forced hospitalists, as educators, to face the question: “How do you teach when you don’t have the answers?”

Teaching outside a hospitalist’s expertise existed before the COVID-19 pandemic and will continue to exist. It is a challenge encountered frequently by both junior and senior faculty across all disciplines, yet is rarely discussed.¹ However, great learning can still occur when we teach at the edge of our comfort zones.

You don’t need to be an expert to be a great teacher. Although most educators know this, we often fear that disclosing our knowledge limitations exposes our weaknesses. But a successful start to the learning journey begins with establishing trust and confidence with your learners. Remaining authentic in your knowledge base will inspire more credibility than false pretenses of content mastery. Phrases like, “This topic is new for me as well. Here’s what I do know and what I don’t know” or “What a great question. I wish I had a great answer. Let me get back to you” set a standard for honesty and reduce teaching pressures. In turn, learners will be more comfortable acknowledging their own uncertainties and will be more likely to voice their hesitations or ask questions on rounds.

The field of medicine is steeped in hierarchical structure, where the attending is assumed to have the most knowledge. But this may not always be true, as learners are often more up to date on a subject than the attending. By reexamining traditional hierarchies and instead considering ourselves as part of a learning team, we can promote a more positive educational climate.

When a learner asks a question that you don’t have an answer for, the response “Great question. I can tell you what I think, but I’m interested in first hearing your thoughts” reflects that you respect your learners and their skills and experiences. You can also ask them to do a literature review and report back to you and the team the next morning. By inverting the hierarchy, you are teaching humility, adaptability, and shared responsibility, as well as demonstrating the skills of being a lifelong learner.²

As educators, we often hold ourselves to unrealistic expectations of being omniscient knowledge vessels. In times of crisis or uncertainty, teaching about how to learn and where to learn become just as important as what to learn. Invite learners to observe how you navigate ambiguity. For example, I recently interacted with a colleague on an unfamiliar case. She said, “Dr Wang, I don’t know much about malaria. Can you share with me what made you consider this diagnosis?” Additionally, admitting to learners when you have made an error not only clarifies their learning, but also role models continuous personal improvement.

By modeling humility by acknowledging our own limits, respecting our learners’ knowledge and experiences, and demonstrating how we manage uncertainty, we can enhance the learning environment and inspire our learners.

“By learning you will teach, by teaching you will learn.”

–Latin proverb

The COVID-19 pandemic thrust hospitalists into uncertain clinical situations where scientific evidence was rapidly changing and expert consensus was not always available. Amidst this, our learners were eager to better understand this new disease and how to properly care for patients. This forced hospitalists, as educators, to face the question: “How do you teach when you don’t have the answers?”

Teaching outside a hospitalist’s expertise existed before the COVID-19 pandemic and will continue to exist. It is a challenge encountered frequently by both junior and senior faculty across all disciplines, yet is rarely discussed.¹ However, great learning can still occur when we teach at the edge of our comfort zones.

You don’t need to be an expert to be a great teacher. Although most educators know this, we often fear that disclosing our knowledge limitations exposes our weaknesses. But a successful start to the learning journey begins with establishing trust and confidence with your learners. Remaining authentic in your knowledge base will inspire more credibility than false pretenses of content mastery. Phrases like, “This topic is new for me as well. Here’s what I do know and what I don’t know” or “What a great question. I wish I had a great answer. Let me get back to you” set a standard for honesty and reduce teaching pressures. In turn, learners will be more comfortable acknowledging their own uncertainties and will be more likely to voice their hesitations or ask questions on rounds.

The field of medicine is steeped in hierarchical structure, where the attending is assumed to have the most knowledge. But this may not always be true, as learners are often more up to date on a subject than the attending. By reexamining traditional hierarchies and instead considering ourselves as part of a learning team, we can promote a more positive educational climate.

When a learner asks a question that you don’t have an answer for, the response “Great question. I can tell you what I think, but I’m interested in first hearing your thoughts” reflects that you respect your learners and their skills and experiences. You can also ask them to do a literature review and report back to you and the team the next morning. By inverting the hierarchy, you are teaching humility, adaptability, and shared responsibility, as well as demonstrating the skills of being a lifelong learner.²

As educators, we often hold ourselves to unrealistic expectations of being omniscient knowledge vessels. In times of crisis or uncertainty, teaching about how to learn and where to learn become just as important as what to learn. Invite learners to observe how you navigate ambiguity. For example, I recently interacted with a colleague on an unfamiliar case. She said, “Dr Wang, I don’t know much about malaria. Can you share with me what made you consider this diagnosis?” Additionally, admitting to learners when you have made an error not only clarifies their learning, but also role models continuous personal improvement.

By modeling humility by acknowledging our own limits, respecting our learners’ knowledge and experiences, and demonstrating how we manage uncertainty, we can enhance the learning environment and inspire our learners.

What has been the scholarly output of academic hospital medicine faculty (AHMF) and what academic rank have they achieved at US academic medical centers (AMCs)? Sumarsono et al¹ address these questions and add to the growing body of literature exposing the limited academic footprint of hospitalists.

The authors performed a cross-sectional analysis of AHMF affiliated with the top 25 internal medicine training programs (as determined by the physician networking service doximity.com) and used Scopus to determine number of publications, citations, and H-index (a metric of productivity) for each faculty member. They also evaluated predictors for promotion. In contrast, most prior research on this topic relies on data obtained by survey methodology.^2-5

Among 1554 AHMF from 22 AMCs, 42 (2.7%) were full professors and 140 (9.0%) were associate professors. The number of publications per AHMF was noticeably low, with a mean of 6.3 and median of 0 (interquartile range, 0-4). The authors found that H-index, completion of chief residency, and graduation from a top 25 medical school were independently associated with promotion.

The authors only evaluated AHMF among the most academically rigorous AMCs, an approach that likely overestimates scholarly output of hospitalists across all US AMCs. Conversely, if we presume that promotion is more difficult at these major AMCs, the results may underestimate academic rank of AHMF nationally. Additionally, the authors did not distinguish faculty by tracks (eg, clinician-investigators, clinician-educators), which often have different criteria for academic promotion.

These findings are worrisomely consistent with prior reports, despite the tremendous expansion of the field.^2-4 A 2008 survey of academic hospitalists found that 4% of respondents were full professors and 9% were associate professors, values nearly identical to the results in this current analysis,⁴ suggesting enduring barriers to academic advancement.

We are left with the following questions provoked by this body of literature: How can hospitalists increase their scholarly output and climb the promotional ladder? And how can we increase the academic footprint of hospital medicine? We recently proposed the following strategies based on a survey of academic groups participating in the Hospital Medicine Reengineering Network (HOMERuN) survey⁵: (1) expand hospital medicine research fellowships, which will provide graduates with research skills to justify dedicated time for research and aid their ability to obtain independent funding; (2) formalize mentorship between research faculty in hospital medicine and other internal medicine disciplines with robust track records for research; (3) invest in research infrastructure and data access within and between institutions; and (4) encourage hospital medicine group leaders to foster academic growth by incentivizing faculty to perform research, present their work at national conferences, and publish manuscripts with their findings.

Although an increase in scholarly output should contribute to higher academic rank, hospitalists routinely make other invaluable contributions beyond clinical care to AMCs, including medical education, hospital leadership, quality improvement, clinical innovation, and social justice advocacy. Also, hospitalists are increasingly disseminating their contributions via newer mediums (eg, social media, podcasts) that arguably have greater reach than traditional scholarship outlets. We believe that promotion committees should update their criteria to reflect the evolution of academic contribution and integrate these within traditional promotion pathways.

Finally, we must address federal funding mechanisms, which currently favor specialty-specific funding over funding that would be more applicable to hospital medicine researchers. Funding agencies are largely specialty- or disease-specific, with limited options for broader-based research.⁶ Additionally, grant-review committees are largely comprised of specialists, with few generalists and fewer hospitalists. These limitations make it difficult to “argue” the necessity of hospital medicine research. One concrete step would be for the National Institutes of Health (NIH) to create an Office for Hospital Medicine Research, analogous to the Office of Emergency Care Research, which works across NIH institutes and centers to foster research and research training for the emergency setting.

With these strategies, we are hopeful that hospital medicine will continue to expand its academic footprint and be recognized for its ever-growing contributions to the practice of medicine.

What has been the scholarly output of academic hospital medicine faculty (AHMF) and what academic rank have they achieved at US academic medical centers (AMCs)? Sumarsono et al¹ address these questions and add to the growing body of literature exposing the limited academic footprint of hospitalists.

The authors performed a cross-sectional analysis of AHMF affiliated with the top 25 internal medicine training programs (as determined by the physician networking service doximity.com) and used Scopus to determine number of publications, citations, and H-index (a metric of productivity) for each faculty member. They also evaluated predictors for promotion. In contrast, most prior research on this topic relies on data obtained by survey methodology.^2-5

Among 1554 AHMF from 22 AMCs, 42 (2.7%) were full professors and 140 (9.0%) were associate professors. The number of publications per AHMF was noticeably low, with a mean of 6.3 and median of 0 (interquartile range, 0-4). The authors found that H-index, completion of chief residency, and graduation from a top 25 medical school were independently associated with promotion.

The authors only evaluated AHMF among the most academically rigorous AMCs, an approach that likely overestimates scholarly output of hospitalists across all US AMCs. Conversely, if we presume that promotion is more difficult at these major AMCs, the results may underestimate academic rank of AHMF nationally. Additionally, the authors did not distinguish faculty by tracks (eg, clinician-investigators, clinician-educators), which often have different criteria for academic promotion.

These findings are worrisomely consistent with prior reports, despite the tremendous expansion of the field.^2-4 A 2008 survey of academic hospitalists found that 4% of respondents were full professors and 9% were associate professors, values nearly identical to the results in this current analysis,⁴ suggesting enduring barriers to academic advancement.

We are left with the following questions provoked by this body of literature: How can hospitalists increase their scholarly output and climb the promotional ladder? And how can we increase the academic footprint of hospital medicine? We recently proposed the following strategies based on a survey of academic groups participating in the Hospital Medicine Reengineering Network (HOMERuN) survey⁵: (1) expand hospital medicine research fellowships, which will provide graduates with research skills to justify dedicated time for research and aid their ability to obtain independent funding; (2) formalize mentorship between research faculty in hospital medicine and other internal medicine disciplines with robust track records for research; (3) invest in research infrastructure and data access within and between institutions; and (4) encourage hospital medicine group leaders to foster academic growth by incentivizing faculty to perform research, present their work at national conferences, and publish manuscripts with their findings.

Although an increase in scholarly output should contribute to higher academic rank, hospitalists routinely make other invaluable contributions beyond clinical care to AMCs, including medical education, hospital leadership, quality improvement, clinical innovation, and social justice advocacy. Also, hospitalists are increasingly disseminating their contributions via newer mediums (eg, social media, podcasts) that arguably have greater reach than traditional scholarship outlets. We believe that promotion committees should update their criteria to reflect the evolution of academic contribution and integrate these within traditional promotion pathways.

Finally, we must address federal funding mechanisms, which currently favor specialty-specific funding over funding that would be more applicable to hospital medicine researchers. Funding agencies are largely specialty- or disease-specific, with limited options for broader-based research.⁶ Additionally, grant-review committees are largely comprised of specialists, with few generalists and fewer hospitalists. These limitations make it difficult to “argue” the necessity of hospital medicine research. One concrete step would be for the National Institutes of Health (NIH) to create an Office for Hospital Medicine Research, analogous to the Office of Emergency Care Research, which works across NIH institutes and centers to foster research and research training for the emergency setting.

With these strategies, we are hopeful that hospital medicine will continue to expand its academic footprint and be recognized for its ever-growing contributions to the practice of medicine.

What has been the scholarly output of academic hospital medicine faculty (AHMF) and what academic rank have they achieved at US academic medical centers (AMCs)? Sumarsono et al¹ address these questions and add to the growing body of literature exposing the limited academic footprint of hospitalists.

The authors performed a cross-sectional analysis of AHMF affiliated with the top 25 internal medicine training programs (as determined by the physician networking service doximity.com) and used Scopus to determine number of publications, citations, and H-index (a metric of productivity) for each faculty member. They also evaluated predictors for promotion. In contrast, most prior research on this topic relies on data obtained by survey methodology.^2-5

Among 1554 AHMF from 22 AMCs, 42 (2.7%) were full professors and 140 (9.0%) were associate professors. The number of publications per AHMF was noticeably low, with a mean of 6.3 and median of 0 (interquartile range, 0-4). The authors found that H-index, completion of chief residency, and graduation from a top 25 medical school were independently associated with promotion.

The authors only evaluated AHMF among the most academically rigorous AMCs, an approach that likely overestimates scholarly output of hospitalists across all US AMCs. Conversely, if we presume that promotion is more difficult at these major AMCs, the results may underestimate academic rank of AHMF nationally. Additionally, the authors did not distinguish faculty by tracks (eg, clinician-investigators, clinician-educators), which often have different criteria for academic promotion.

These findings are worrisomely consistent with prior reports, despite the tremendous expansion of the field.^2-4 A 2008 survey of academic hospitalists found that 4% of respondents were full professors and 9% were associate professors, values nearly identical to the results in this current analysis,⁴ suggesting enduring barriers to academic advancement.

We are left with the following questions provoked by this body of literature: How can hospitalists increase their scholarly output and climb the promotional ladder? And how can we increase the academic footprint of hospital medicine? We recently proposed the following strategies based on a survey of academic groups participating in the Hospital Medicine Reengineering Network (HOMERuN) survey⁵: (1) expand hospital medicine research fellowships, which will provide graduates with research skills to justify dedicated time for research and aid their ability to obtain independent funding; (2) formalize mentorship between research faculty in hospital medicine and other internal medicine disciplines with robust track records for research; (3) invest in research infrastructure and data access within and between institutions; and (4) encourage hospital medicine group leaders to foster academic growth by incentivizing faculty to perform research, present their work at national conferences, and publish manuscripts with their findings.

Although an increase in scholarly output should contribute to higher academic rank, hospitalists routinely make other invaluable contributions beyond clinical care to AMCs, including medical education, hospital leadership, quality improvement, clinical innovation, and social justice advocacy. Also, hospitalists are increasingly disseminating their contributions via newer mediums (eg, social media, podcasts) that arguably have greater reach than traditional scholarship outlets. We believe that promotion committees should update their criteria to reflect the evolution of academic contribution and integrate these within traditional promotion pathways.

Finally, we must address federal funding mechanisms, which currently favor specialty-specific funding over funding that would be more applicable to hospital medicine researchers. Funding agencies are largely specialty- or disease-specific, with limited options for broader-based research.⁶ Additionally, grant-review committees are largely comprised of specialists, with few generalists and fewer hospitalists. These limitations make it difficult to “argue” the necessity of hospital medicine research. One concrete step would be for the National Institutes of Health (NIH) to create an Office for Hospital Medicine Research, analogous to the Office of Emergency Care Research, which works across NIH institutes and centers to foster research and research training for the emergency setting.

With these strategies, we are hopeful that hospital medicine will continue to expand its academic footprint and be recognized for its ever-growing contributions to the practice of medicine.

As our understanding of SARS-CoV-2 has progressed, researchers, clinicians, and patients have learned that recovery from COVID-19 can last well beyond the acute phase of the illness. As we see fewer fatal cases and more survivors, studies that characterize the postacute sequelae of COVID-19 (PASC) are increasingly important for understanding how to help patients return to their normal lives, especially after hospitalization. Critical to investigating this is knowing patients’ burden of symptoms and disabilities prior to infection. In this issue, a study by Iwashyna et al¹ helps us understand patients’ lives after COVID compared to their lives before COVID.

The study analyzed patients with SARS-CoV-2 infection admitted during the third wave of the pandemic to assess for new cardiopulmonary symptoms, new disability, and financial toxicity of hospitalization 1 month after discharge.¹ Many patients had new cardiopulmonary symptoms and oxygen use, and a much larger number had new limitations in activities of daily living (ADLs) or instrumental activities of daily living (iADLs). The majority were discharged home without home care services, and new limitations in ADLs or iADLs were common in these cases. Most patients reported not having returned to their cardiopulmonary or functional baseline; however, new cough, shortness of breath, or oxygen use usually did not explain their new disabilities. Financial toxicity was also common, reflecting the effects of COVID-19 on both employment and family finances.

These results complement those of Chopra et al,² who examined 60-day outcomes for patients hospitalized during the first wave of the pandemic. At 2 months from discharge, many patients had ongoing cough, shortness of breath, oxygen use, and disability, but at lower rates. This likely reflects continuing recovery during the extra 30 days, but other potential explanations deserve consideration. One possibility is improving survival over the course of the pandemic. Many patients who may have passed away earlier in the pandemic now survive to return home, albeit with a heavy burden of symptomatology. This raises the possibility that symptoms among survivors may continue to increase as survival of COVID-19 improves. However, it should be noted that neither study is representative of the national patterns of hospitalization by race or ethnicity.³ Iwashyna et al¹ underrepresented Black patients, while Chopra et al² underrepresented Hispanic patients. Given what we know about outcomes for these populations and their underrepresentation in PASC literature, the impact of COVID-19 for them is likely underestimated. As data from 3, 6, or 12 months become available, we may also see the effect sizes described in this early literature become even larger.

Consistent with the findings of Chopra et al,² financial toxicity after COVID-19 hospitalization was high. The longer-term financial burden of COVID-19 will likely exceed what is described here, particularly for Black and Hispanic patients, who experienced a disproportionate drain on their savings. These populations are also more likely to be negatively impacted by the COVID economy⁴ and thus may suffer a “double hit” financially if hospitalized.

Iwashyna et al¹ underscore the urgent need for progress in understanding COVID “long-haulers”⁵ and helping patients with physical and financial recovery. Whether the spectacular innovations identified by the medical community in COVID-19 prevention and treatment of acute illness can be found for long COVID remains to be seen. The fact that so many patients studied by Iwashyna et al did not receive home care services and experienced financial toxicity shows the importance of broader implementation of systems and services to support survivors of COVID-19 hospitalization. Developers of this support must emphasize the importance of physical and cardiopulmonary rehabilitation as well as financial relief, particularly for minorities. For our patients and their families, this may be the best strategy to get “back to normal.”

The authors thank Dr Vineet Arora for reviewing and advising on this manuscript.

As our understanding of SARS-CoV-2 has progressed, researchers, clinicians, and patients have learned that recovery from COVID-19 can last well beyond the acute phase of the illness. As we see fewer fatal cases and more survivors, studies that characterize the postacute sequelae of COVID-19 (PASC) are increasingly important for understanding how to help patients return to their normal lives, especially after hospitalization. Critical to investigating this is knowing patients’ burden of symptoms and disabilities prior to infection. In this issue, a study by Iwashyna et al¹ helps us understand patients’ lives after COVID compared to their lives before COVID.

The study analyzed patients with SARS-CoV-2 infection admitted during the third wave of the pandemic to assess for new cardiopulmonary symptoms, new disability, and financial toxicity of hospitalization 1 month after discharge.¹ Many patients had new cardiopulmonary symptoms and oxygen use, and a much larger number had new limitations in activities of daily living (ADLs) or instrumental activities of daily living (iADLs). The majority were discharged home without home care services, and new limitations in ADLs or iADLs were common in these cases. Most patients reported not having returned to their cardiopulmonary or functional baseline; however, new cough, shortness of breath, or oxygen use usually did not explain their new disabilities. Financial toxicity was also common, reflecting the effects of COVID-19 on both employment and family finances.

These results complement those of Chopra et al,² who examined 60-day outcomes for patients hospitalized during the first wave of the pandemic. At 2 months from discharge, many patients had ongoing cough, shortness of breath, oxygen use, and disability, but at lower rates. This likely reflects continuing recovery during the extra 30 days, but other potential explanations deserve consideration. One possibility is improving survival over the course of the pandemic. Many patients who may have passed away earlier in the pandemic now survive to return home, albeit with a heavy burden of symptomatology. This raises the possibility that symptoms among survivors may continue to increase as survival of COVID-19 improves. However, it should be noted that neither study is representative of the national patterns of hospitalization by race or ethnicity.³ Iwashyna et al¹ underrepresented Black patients, while Chopra et al² underrepresented Hispanic patients. Given what we know about outcomes for these populations and their underrepresentation in PASC literature, the impact of COVID-19 for them is likely underestimated. As data from 3, 6, or 12 months become available, we may also see the effect sizes described in this early literature become even larger.

Consistent with the findings of Chopra et al,² financial toxicity after COVID-19 hospitalization was high. The longer-term financial burden of COVID-19 will likely exceed what is described here, particularly for Black and Hispanic patients, who experienced a disproportionate drain on their savings. These populations are also more likely to be negatively impacted by the COVID economy⁴ and thus may suffer a “double hit” financially if hospitalized.

Iwashyna et al¹ underscore the urgent need for progress in understanding COVID “long-haulers”⁵ and helping patients with physical and financial recovery. Whether the spectacular innovations identified by the medical community in COVID-19 prevention and treatment of acute illness can be found for long COVID remains to be seen. The fact that so many patients studied by Iwashyna et al did not receive home care services and experienced financial toxicity shows the importance of broader implementation of systems and services to support survivors of COVID-19 hospitalization. Developers of this support must emphasize the importance of physical and cardiopulmonary rehabilitation as well as financial relief, particularly for minorities. For our patients and their families, this may be the best strategy to get “back to normal.”

The authors thank Dr Vineet Arora for reviewing and advising on this manuscript.

As our understanding of SARS-CoV-2 has progressed, researchers, clinicians, and patients have learned that recovery from COVID-19 can last well beyond the acute phase of the illness. As we see fewer fatal cases and more survivors, studies that characterize the postacute sequelae of COVID-19 (PASC) are increasingly important for understanding how to help patients return to their normal lives, especially after hospitalization. Critical to investigating this is knowing patients’ burden of symptoms and disabilities prior to infection. In this issue, a study by Iwashyna et al¹ helps us understand patients’ lives after COVID compared to their lives before COVID.

The study analyzed patients with SARS-CoV-2 infection admitted during the third wave of the pandemic to assess for new cardiopulmonary symptoms, new disability, and financial toxicity of hospitalization 1 month after discharge.¹ Many patients had new cardiopulmonary symptoms and oxygen use, and a much larger number had new limitations in activities of daily living (ADLs) or instrumental activities of daily living (iADLs). The majority were discharged home without home care services, and new limitations in ADLs or iADLs were common in these cases. Most patients reported not having returned to their cardiopulmonary or functional baseline; however, new cough, shortness of breath, or oxygen use usually did not explain their new disabilities. Financial toxicity was also common, reflecting the effects of COVID-19 on both employment and family finances.

These results complement those of Chopra et al,² who examined 60-day outcomes for patients hospitalized during the first wave of the pandemic. At 2 months from discharge, many patients had ongoing cough, shortness of breath, oxygen use, and disability, but at lower rates. This likely reflects continuing recovery during the extra 30 days, but other potential explanations deserve consideration. One possibility is improving survival over the course of the pandemic. Many patients who may have passed away earlier in the pandemic now survive to return home, albeit with a heavy burden of symptomatology. This raises the possibility that symptoms among survivors may continue to increase as survival of COVID-19 improves. However, it should be noted that neither study is representative of the national patterns of hospitalization by race or ethnicity.³ Iwashyna et al¹ underrepresented Black patients, while Chopra et al² underrepresented Hispanic patients. Given what we know about outcomes for these populations and their underrepresentation in PASC literature, the impact of COVID-19 for them is likely underestimated. As data from 3, 6, or 12 months become available, we may also see the effect sizes described in this early literature become even larger.

Consistent with the findings of Chopra et al,² financial toxicity after COVID-19 hospitalization was high. The longer-term financial burden of COVID-19 will likely exceed what is described here, particularly for Black and Hispanic patients, who experienced a disproportionate drain on their savings. These populations are also more likely to be negatively impacted by the COVID economy⁴ and thus may suffer a “double hit” financially if hospitalized.

Iwashyna et al¹ underscore the urgent need for progress in understanding COVID “long-haulers”⁵ and helping patients with physical and financial recovery. Whether the spectacular innovations identified by the medical community in COVID-19 prevention and treatment of acute illness can be found for long COVID remains to be seen. The fact that so many patients studied by Iwashyna et al did not receive home care services and experienced financial toxicity shows the importance of broader implementation of systems and services to support survivors of COVID-19 hospitalization. Developers of this support must emphasize the importance of physical and cardiopulmonary rehabilitation as well as financial relief, particularly for minorities. For our patients and their families, this may be the best strategy to get “back to normal.”

The authors thank Dr Vineet Arora for reviewing and advising on this manuscript.

Is this patient a good candidate? In medicine, we subconsciously answer this question for every clinical decision we make. Occasionally, though, a clinical scenario is so complex that it cannot or should not be answered by a single individual. One example is the decision on whether a patient should receive an organ transplant. In this situation, a multidisciplinary committee weighs the complex ethical, clinical, and financial implications of the decision before coming to a verdict. Together, team members discuss the risks and benefits of each patient’s candidacy and, in a united fashion, decide the best course of care. For hospitalists, a far more common question occurs every day and is similarly fraught with multifaceted implications: Is my patient a good candidate for a skilled nursing facility (SNF)? We often rely on a single individual to make the final call, but should we instead be leveraging the expertise of other care team members to assist with this decision?

In this issue, Boyle et al¹ describe the implementation of a multidisciplinary team consisting of physicians, case managers, social workers, physical and occupational therapists, and home-health representatives that reviewed all patients with an expected discharge to a SNF. Case managers or social workers began the process by referring eligible patients to the committee for review. If deemed appropriate, the committee discussed each case and reached a consensus recommendation as to whether a SNF was an appropriate discharge destination. The investigators used a matched, preintervention sample as a comparison group, with a primary outcome of total discharges to SNFs, and secondary outcomes consisting of readmissions, time to readmission, and median length of stay. The authors observed a 49.7% relative reduction in total SNF discharges (25.5% of preintervention patients discharged to a SNF vs 12.8% postintervention), as well as a 66.9% relative reduction in new SNF discharges. Despite the significant reduction in SNF utilization, no differences were noted in readmissions, time to readmission, or readmission length of stay.

While this study was performed during the COVID-19 pandemic, several characteristics make its findings applicable beyond this period. First, the structure and workflow of the team are extensively detailed and make the intervention easily generalizable to most hospitals. Second, while not specifically examined, the outcome of SNF reduction likely corresponds to an increase in the patient’s time at home—an important patient-centered target for most posthospitalization plans.² Finally, the intervention used existing infrastructure and individuals, and did not require new resources to improve patient care, which increases the feasibility of implementation at other institutions.

These findings also reveal potential overutilization of SNFs in the discharge process. On average, a typical SNF stay costs the health system more than $11,000.³ A simple intervention could lead to substantial savings for individuals and the healthcare system. With a nearly 50% reduction in SNF use, understanding why patients who were eligible to go home were ultimately discharged to a SNF will be a crucial question to answer. Are there barriers to patient or family education? Is there a perceived safety difference between a SNF and home for nonskilled nursing needs? Additionally, care should be taken to ensure that decreases in SNF utilization do not disproportionately affect certain populations. Further work should assess the performance of similar models in a non-COVID era and among multiple institutions to verify potential scalability and generalizability.

Like organ transplant committees, Boyle et al’s multidisciplinary approach to reduce SNF discharges had to include thoughtful and intentional decisions. Perhaps it is time we use this same model to transplant patients back into their homes as safely and efficiently as possible.

Is this patient a good candidate? In medicine, we subconsciously answer this question for every clinical decision we make. Occasionally, though, a clinical scenario is so complex that it cannot or should not be answered by a single individual. One example is the decision on whether a patient should receive an organ transplant. In this situation, a multidisciplinary committee weighs the complex ethical, clinical, and financial implications of the decision before coming to a verdict. Together, team members discuss the risks and benefits of each patient’s candidacy and, in a united fashion, decide the best course of care. For hospitalists, a far more common question occurs every day and is similarly fraught with multifaceted implications: Is my patient a good candidate for a skilled nursing facility (SNF)? We often rely on a single individual to make the final call, but should we instead be leveraging the expertise of other care team members to assist with this decision?

In this issue, Boyle et al¹ describe the implementation of a multidisciplinary team consisting of physicians, case managers, social workers, physical and occupational therapists, and home-health representatives that reviewed all patients with an expected discharge to a SNF. Case managers or social workers began the process by referring eligible patients to the committee for review. If deemed appropriate, the committee discussed each case and reached a consensus recommendation as to whether a SNF was an appropriate discharge destination. The investigators used a matched, preintervention sample as a comparison group, with a primary outcome of total discharges to SNFs, and secondary outcomes consisting of readmissions, time to readmission, and median length of stay. The authors observed a 49.7% relative reduction in total SNF discharges (25.5% of preintervention patients discharged to a SNF vs 12.8% postintervention), as well as a 66.9% relative reduction in new SNF discharges. Despite the significant reduction in SNF utilization, no differences were noted in readmissions, time to readmission, or readmission length of stay.

While this study was performed during the COVID-19 pandemic, several characteristics make its findings applicable beyond this period. First, the structure and workflow of the team are extensively detailed and make the intervention easily generalizable to most hospitals. Second, while not specifically examined, the outcome of SNF reduction likely corresponds to an increase in the patient’s time at home—an important patient-centered target for most posthospitalization plans.² Finally, the intervention used existing infrastructure and individuals, and did not require new resources to improve patient care, which increases the feasibility of implementation at other institutions.

These findings also reveal potential overutilization of SNFs in the discharge process. On average, a typical SNF stay costs the health system more than $11,000.³ A simple intervention could lead to substantial savings for individuals and the healthcare system. With a nearly 50% reduction in SNF use, understanding why patients who were eligible to go home were ultimately discharged to a SNF will be a crucial question to answer. Are there barriers to patient or family education? Is there a perceived safety difference between a SNF and home for nonskilled nursing needs? Additionally, care should be taken to ensure that decreases in SNF utilization do not disproportionately affect certain populations. Further work should assess the performance of similar models in a non-COVID era and among multiple institutions to verify potential scalability and generalizability.

Like organ transplant committees, Boyle et al’s multidisciplinary approach to reduce SNF discharges had to include thoughtful and intentional decisions. Perhaps it is time we use this same model to transplant patients back into their homes as safely and efficiently as possible.

Is this patient a good candidate? In medicine, we subconsciously answer this question for every clinical decision we make. Occasionally, though, a clinical scenario is so complex that it cannot or should not be answered by a single individual. One example is the decision on whether a patient should receive an organ transplant. In this situation, a multidisciplinary committee weighs the complex ethical, clinical, and financial implications of the decision before coming to a verdict. Together, team members discuss the risks and benefits of each patient’s candidacy and, in a united fashion, decide the best course of care. For hospitalists, a far more common question occurs every day and is similarly fraught with multifaceted implications: Is my patient a good candidate for a skilled nursing facility (SNF)? We often rely on a single individual to make the final call, but should we instead be leveraging the expertise of other care team members to assist with this decision?

In this issue, Boyle et al¹ describe the implementation of a multidisciplinary team consisting of physicians, case managers, social workers, physical and occupational therapists, and home-health representatives that reviewed all patients with an expected discharge to a SNF. Case managers or social workers began the process by referring eligible patients to the committee for review. If deemed appropriate, the committee discussed each case and reached a consensus recommendation as to whether a SNF was an appropriate discharge destination. The investigators used a matched, preintervention sample as a comparison group, with a primary outcome of total discharges to SNFs, and secondary outcomes consisting of readmissions, time to readmission, and median length of stay. The authors observed a 49.7% relative reduction in total SNF discharges (25.5% of preintervention patients discharged to a SNF vs 12.8% postintervention), as well as a 66.9% relative reduction in new SNF discharges. Despite the significant reduction in SNF utilization, no differences were noted in readmissions, time to readmission, or readmission length of stay.

While this study was performed during the COVID-19 pandemic, several characteristics make its findings applicable beyond this period. First, the structure and workflow of the team are extensively detailed and make the intervention easily generalizable to most hospitals. Second, while not specifically examined, the outcome of SNF reduction likely corresponds to an increase in the patient’s time at home—an important patient-centered target for most posthospitalization plans.² Finally, the intervention used existing infrastructure and individuals, and did not require new resources to improve patient care, which increases the feasibility of implementation at other institutions.

These findings also reveal potential overutilization of SNFs in the discharge process. On average, a typical SNF stay costs the health system more than $11,000.³ A simple intervention could lead to substantial savings for individuals and the healthcare system. With a nearly 50% reduction in SNF use, understanding why patients who were eligible to go home were ultimately discharged to a SNF will be a crucial question to answer. Are there barriers to patient or family education? Is there a perceived safety difference between a SNF and home for nonskilled nursing needs? Additionally, care should be taken to ensure that decreases in SNF utilization do not disproportionately affect certain populations. Further work should assess the performance of similar models in a non-COVID era and among multiple institutions to verify potential scalability and generalizability.

Like organ transplant committees, Boyle et al’s multidisciplinary approach to reduce SNF discharges had to include thoughtful and intentional decisions. Perhaps it is time we use this same model to transplant patients back into their homes as safely and efficiently as possible.

Earlier this year, the Journal of Hospital Medicine updated its author guidelines to include recommendations on addressing race and racism.¹ These recommendations include explicitly naming racism (rather than race) as a determinant of health. Operationalizing these recommendations into manuscripts represents a fundamental shift in how we ask research questions, structure analyses, and interpret results.

In this issue, Maxwell et al² illustrate how to disseminate research through this lens in their retrospective cohort study of children with type 1 diabetes hospitalized with diabetic ketoacidosis (DKA). Using 6 years of data from a major academic pediatric medical center, the authors examine the association between risk for DKA admission and three factors: neighborhood poverty level, race, and type of insurance (public or private). Secondary outcomes include DKA severity and length of stay. In their unadjusted model, poverty, race, and insurance were all associated with increased hospitalizations. However, following adjustment, the association between race and hospitalizations disappeared.In line with the journal’s new guidelines, the authors point out that the statistically significant associations of poverty and insurance type with clinical outcomes suggest that racism, rather than race, is a social factor at work in their population. The authors provide further context regarding structural racism in the United States and the history of redlining, which has helped shape a society in which Black individuals are more likely to live in areas of concentrated poverty and be publicly insured.

Two other findings related to the impact of racism are notable. First, in both their univariate and multivariate models, the authors found significant A1c differences between Black and White children—higher than those of previous reports.³ These findings suggest the existence of structural factors at work in the health of their patients. Second, Black patients had longer lengths of stay when compared to White patients with the same severity of DKA. Neither poverty level nor insurance status were significantly associated with length of stay. While the analysis was limited to detecting this difference, rather than identifying its causes, the authors suggest factors at both individual and structural levels that may be impacting outcomes. Specifically, care team bias may impact discharge decisions, and factors such as less flexible times to complete diabetes education, transportation barriers, and childcare challenges could also impact discharge timing.

This work provides a template for how to address the impact of racism on health with intentionality. Moreover, individuals’ lived environments should be considered through alternative economic measurements and neighborhood definitions. The proportion of people within a census tract living below the federal poverty line is just one measure of the complex dynamics that contribute to an individual’s socioeconomic status. An alternative measure is the area deprivation index, which incorporates 17 indicators at the more granular census block group level to describe an individual’s environment⁴ and could be useful in this area of research.

Perhaps most relevant is the use of public insurance as a marker of socioeconomic status. Medicaid, although not without its flaws, provides fairly comprehensive coverage. However, many Americans have incomes too high to qualify for public insurance but too low to afford adequate insurance coverage. Theoretically, these individuals qualify for subsidies through the Affordable Care Act, yet underinsurance remains a significant issue.⁵ Future analyses to further understand and describe clinical outcomes could include this population of underinsured children as a distinct at-risk group. Maxwell et al² provide an excellent example of how we should address race and racism in disseminated literature. Although initially challenging, writing with intentionality regarding this fundamental determinant of health can provide rich and actionable information for practitioners and policy-makers.

Earlier this year, the Journal of Hospital Medicine updated its author guidelines to include recommendations on addressing race and racism.¹ These recommendations include explicitly naming racism (rather than race) as a determinant of health. Operationalizing these recommendations into manuscripts represents a fundamental shift in how we ask research questions, structure analyses, and interpret results.

In this issue, Maxwell et al² illustrate how to disseminate research through this lens in their retrospective cohort study of children with type 1 diabetes hospitalized with diabetic ketoacidosis (DKA). Using 6 years of data from a major academic pediatric medical center, the authors examine the association between risk for DKA admission and three factors: neighborhood poverty level, race, and type of insurance (public or private). Secondary outcomes include DKA severity and length of stay. In their unadjusted model, poverty, race, and insurance were all associated with increased hospitalizations. However, following adjustment, the association between race and hospitalizations disappeared.In line with the journal’s new guidelines, the authors point out that the statistically significant associations of poverty and insurance type with clinical outcomes suggest that racism, rather than race, is a social factor at work in their population. The authors provide further context regarding structural racism in the United States and the history of redlining, which has helped shape a society in which Black individuals are more likely to live in areas of concentrated poverty and be publicly insured.

Two other findings related to the impact of racism are notable. First, in both their univariate and multivariate models, the authors found significant A1c differences between Black and White children—higher than those of previous reports.³ These findings suggest the existence of structural factors at work in the health of their patients. Second, Black patients had longer lengths of stay when compared to White patients with the same severity of DKA. Neither poverty level nor insurance status were significantly associated with length of stay. While the analysis was limited to detecting this difference, rather than identifying its causes, the authors suggest factors at both individual and structural levels that may be impacting outcomes. Specifically, care team bias may impact discharge decisions, and factors such as less flexible times to complete diabetes education, transportation barriers, and childcare challenges could also impact discharge timing.

This work provides a template for how to address the impact of racism on health with intentionality. Moreover, individuals’ lived environments should be considered through alternative economic measurements and neighborhood definitions. The proportion of people within a census tract living below the federal poverty line is just one measure of the complex dynamics that contribute to an individual’s socioeconomic status. An alternative measure is the area deprivation index, which incorporates 17 indicators at the more granular census block group level to describe an individual’s environment⁴ and could be useful in this area of research.

Perhaps most relevant is the use of public insurance as a marker of socioeconomic status. Medicaid, although not without its flaws, provides fairly comprehensive coverage. However, many Americans have incomes too high to qualify for public insurance but too low to afford adequate insurance coverage. Theoretically, these individuals qualify for subsidies through the Affordable Care Act, yet underinsurance remains a significant issue.⁵ Future analyses to further understand and describe clinical outcomes could include this population of underinsured children as a distinct at-risk group. Maxwell et al² provide an excellent example of how we should address race and racism in disseminated literature. Although initially challenging, writing with intentionality regarding this fundamental determinant of health can provide rich and actionable information for practitioners and policy-makers.

Earlier this year, the Journal of Hospital Medicine updated its author guidelines to include recommendations on addressing race and racism.¹ These recommendations include explicitly naming racism (rather than race) as a determinant of health. Operationalizing these recommendations into manuscripts represents a fundamental shift in how we ask research questions, structure analyses, and interpret results.

In this issue, Maxwell et al² illustrate how to disseminate research through this lens in their retrospective cohort study of children with type 1 diabetes hospitalized with diabetic ketoacidosis (DKA). Using 6 years of data from a major academic pediatric medical center, the authors examine the association between risk for DKA admission and three factors: neighborhood poverty level, race, and type of insurance (public or private). Secondary outcomes include DKA severity and length of stay. In their unadjusted model, poverty, race, and insurance were all associated with increased hospitalizations. However, following adjustment, the association between race and hospitalizations disappeared.In line with the journal’s new guidelines, the authors point out that the statistically significant associations of poverty and insurance type with clinical outcomes suggest that racism, rather than race, is a social factor at work in their population. The authors provide further context regarding structural racism in the United States and the history of redlining, which has helped shape a society in which Black individuals are more likely to live in areas of concentrated poverty and be publicly insured.

Two other findings related to the impact of racism are notable. First, in both their univariate and multivariate models, the authors found significant A1c differences between Black and White children—higher than those of previous reports.³ These findings suggest the existence of structural factors at work in the health of their patients. Second, Black patients had longer lengths of stay when compared to White patients with the same severity of DKA. Neither poverty level nor insurance status were significantly associated with length of stay. While the analysis was limited to detecting this difference, rather than identifying its causes, the authors suggest factors at both individual and structural levels that may be impacting outcomes. Specifically, care team bias may impact discharge decisions, and factors such as less flexible times to complete diabetes education, transportation barriers, and childcare challenges could also impact discharge timing.

This work provides a template for how to address the impact of racism on health with intentionality. Moreover, individuals’ lived environments should be considered through alternative economic measurements and neighborhood definitions. The proportion of people within a census tract living below the federal poverty line is just one measure of the complex dynamics that contribute to an individual’s socioeconomic status. An alternative measure is the area deprivation index, which incorporates 17 indicators at the more granular census block group level to describe an individual’s environment⁴ and could be useful in this area of research.

Perhaps most relevant is the use of public insurance as a marker of socioeconomic status. Medicaid, although not without its flaws, provides fairly comprehensive coverage. However, many Americans have incomes too high to qualify for public insurance but too low to afford adequate insurance coverage. Theoretically, these individuals qualify for subsidies through the Affordable Care Act, yet underinsurance remains a significant issue.⁵ Future analyses to further understand and describe clinical outcomes could include this population of underinsured children as a distinct at-risk group. Maxwell et al² provide an excellent example of how we should address race and racism in disseminated literature. Although initially challenging, writing with intentionality regarding this fundamental determinant of health can provide rich and actionable information for practitioners and policy-makers.

Veterans living with cancer need comprehensive assessment that includes supportive psychosocial care. The National Comprehensive Cancer Network (NCCN) and American College of Surgeons Commission on Cancer require accredited cancer centers to evaluate psychosocial distress and provide appropriate triage and treatment for all patients.^1-3 Implementing psychosocial distress screening can be difficult because of procedural barriers and time constraints, clinic and supportive care resources, and lack of knowledge about how to access supportive services.

Distress screening protocols must be designed to address the specific needs of each population. To improve screening for cancer-related distress, deliver effective supportive services, and gain agreement on distress screening standards of care, the Coleman Foundation supported development of the Coleman Supportive Oncology Collaborative (CSOC), a project of 135 interdisciplinary health care professionals from 25 Chicago-area cancer care institutions.⁴

The Jesse Brown US Department of Veterans Affairs (VA) Medical Center (JBVAMC) was chosen to assess cancer-related concerns among veterans using the CSOC screening tool and to improve access to supportive oncology. JBVAMC provides care to approximately 49,000 veterans in Chicago, Illinois, and northwestern Indiana. The JBVAMC patient population includes a large number of veterans with dual diagnoses (co-occurring substance use and mental health disorders) and veterans experiencing homelessness.

Delivering integrated screening and oncologic care that is culture and age appropriate is particularly important for veterans given their unique risk factors. The veteran population is considered vulnerable in terms of health status, psychological functioning, and social context. Veterans who use the VA health system as a principal source of care have poorer health, greater comorbid medical conditions, and an increased risk of mortality and suicide compared with the general population.^5,6 Poorer health status in veterans also may relate to old age, low income, poor education, psychological health, and minority race.^7-9

Past studies point to unique risk factors for cancer and poor cancer adjustment among veterans, which may complicate cancer treatment and end-of-life/survivorship care. Veteran-specific risk factors include military-related exposures, particularly Agent Orange and morbidity/mortality secondary to comorbid medical and psychiatric conditions (eg, chronic obstructive pulmonary disease, diabetes mellitus, and posttraumatic stress disorder [PTSD]).^10-12 Moreover, the geriatric veteran population continues to grow,with increasing rates of cancer that require unique considerations for effective cancer care.^13,14 Despite this, there are minimal data to inform best practices and supportive care approaches for veterans with cancer. Lack of guidelines specific to veterans and other populations with increased psychosocial challenges may impede successful cancer care, making distress screening procedures particularly important. This is especially the case for the JBVAMC, which serves primarily African American urban-dwelling veterans who experience high rates of cancer disparities, including increased rates of mortality and increased levels of psychosocial distress.^15,16

The goals of this program were to (1) examine levels of psychological, physical, financial, and treatment-related distress in a large sample of urban-dwelling veterans; (2) create a streamlined, sustainable process to screen a large number of veterans receiving cancer care in the outpatient setting and connect them with available supportive services; and (3) educate oncology physicians, nurses, and other staff about cancer-related distress and concerns using in-service trainings and interpersonal interactions to improve patient care. Our program was based on a Primary Care Mental Health Integration (PCMHI) model that embeds health psychologists in general medical clinics to better reach veterans dealing with mental health issues. We tailored for palliative care involvement.

Studies of this model have shown that mental health integration improves access to mental health services and mental health treatment outcomes and has higher patient and provider satisfaction.¹⁷ We were also influenced by the construct of the patient aligned care team (PACT) social worker who, in this veteran-centered approach, often functions as a care coordinator. Social work responsibilities include assessment of patients’ stressors including adjusting to the medical conditions, identifying untreated or undertreated mental health or substance abuse issues, economic instability, legal problems, and inadequate housing and transportation, which can often be exacerbated during cancer treatment.¹⁸

Methods

This institutional review board at JBVAMC reviewed and exempted this quality improvement program using the SQUIRE framework.¹⁹ It was led by a group of palliative care clinicians, psychologists, and administrators who have worked with the oncology service for many years, primarily in the care of hospitalized patients. Common palliative care services include providing care for patients with serious illness diagnosis through the illness trajectory.

Setting

At the start of this program, we assessed the current clinic workflow to determine how to best screen and assist veterans experiencing distress. We met with team members individually to identify the best method of clinic integration, including attending medical oncologists, medical oncology fellows, psychology interns, oncology nursing staff, the oncology nurse coordinator, and clinic clerks.

The JBVAMC provides cancer care through 4 half-day medical hematology-oncology clinics that serve about 50 patients per half-day clinic. The clinics are staffed by hematology-oncology fellows supervised by hematology-oncology attending physicians, who are affiliated with 2 academic medical centers. These clinics are staffed by 3 registered nurses (RNs) and a licensed practical nurse (LPN) and are adjacent to a chemotherapy infusion clinic with unique nursing staff. The JBVAMC also provides a variety of supportive care services, including extensive mental health and substance use treatment, physical and occupational therapy, acupuncture, nutrition, social work, and housing services. Following our assessment, it was evident that there were a low number of referrals from oncology clinics to supportive care services, mostly due to lack of knowledge of resources and unclear referral procedures.

Based on clinical volume, we determined that our screening program could best be implemented through a stepped approach beginning in one clinic and expanding thereafter. We began by having a palliative care physician and health psychology intern embedded in 1 weekly half-day clinic and a health psychology intern embedded in a second weekly half-day clinic. Our program included 2 health psychology interns (for each academic year of the program) who were supervised by a JBVA health psychologist.

About 15 months after successful integration within the first 2 half-day clinics, we expanded the screening program to staff an additional half-day medical oncology clinic with a palliative care APRN. This allowed us to expand the screening tool distribution and collection to 3 of 4 of the weekly half-day oncology clinics as well as to meet individually with veterans experiencing high levels of distress. Veterans were flagged as having high distress levels by either the results of their completed screening tool or by referral from a medical oncology physician. We initially established screening in clinics that were sufficiently staffed to ensure that screens were appropriately distributed and reviewed. Patients seen in nonparticipating clinics were referred to outpatient social work, mental health and/or outpatient palliative care according to oncology fellows’ clinical assessments of the patient. All oncology fellows received education about distress screening and methods for referring to supportive care. Our clinic screening program extended from February 2017 through January 2020.

Screening

Program staff screened patients with new cancer diagnoses, then identified patients for follow-up screens. This tracking allowed staff to identify patients with oncology appointments that day and cross-reference patients needing a follow-up screen.

Following feedback from the clinic nurses, we determined that nurses would provide the distress tool to patients in paper form after they completed their assessment of vitals and waited to be seen by their medical oncologist. The patient would then deliver their completed form to the nurse who would combine it with the patient’s clinic notes for the oncologist to review. Veterans who reported elevated 4-question Patient Health Questionnaire (PHQ-4) scores ≥ 6 were seen immediately by program staff. Veterans were referred to social work or psychiatry services for a same day visit if they endorsed a high level of psychological distress during clinical examination. They were referred for other supportive care services if they were determined to have practical, family, or nutrition unmet distress needs by either the program staff or oncology fellows. Program staff provided guidance to medical oncology fellows for needed referrals including social work, mental health, and palliative care follow ups (eAppendix A available at doi:10.12788/fp.0158).

Veterans referred for supportive care services were contacted by the relevant clinical administrator by phone to schedule an intake; for social work referrals, patients were either seen in a walk-in office located in a colocated building or contacted by a social worker by phone.

Our screening tool was the Coleman Foundation Supportive Oncology Collaborative Screening Tool, compiled from validated instruments. Patients completed this screening tool, which includes the PHQ-4, NCCN problem list concerns, adapted Mini Nutrition Assessment and PROMIS Pain and Fatigue measure (eAppendix B available at doi:10.12788/fp.0158).^20-22

We also worked with the VA Computerized Patient Record System (CPRS) to create an electronic template for the screening tool. Completed screening tools were manually entered by the physician, psychologists, or APRN into the CPRS chart.

We analyzed the different supportive care services available at the JBVAMC and noticed that many supportive services were available, yet these services were often separated. Therefore, we created a consult flowsheet to assist oncologists in placing referrals. These supportive care services include mental health services, a cancer support group, home health care, social services, nutrition, physical medicine and rehabilitation, and other specialty services.

Patient Education

The psychology and nursing staff created a patient information bulletin board where patients could access information about supportive services available at JBVAMC. This board required frequent replenishment of handouts because patients consulted the board regularly. Handouts and folders about common clinical issues also were placed in the clinic treatment rooms. We partnered with 2 local cancer support centers, Gilda’s Club and the Cancer Support Center, to make referrals for family members and/or caregivers who would benefit from additional support.

We provided in-service trainings for oncology fellows, including trainings on PTSD and substance abuse and their relationship to cancer care at the VA. These topics were chosen based on the feedback program staff received about perceived knowledge gaps from the oncology fellows. This program allowed for multiple informal conversations between that program staff and oncology fellows about overall patient care. We held trainings with the cancer coordinator and clinical nursing staff on strategies to identify and follow-up on cancer-related distress, and with oncology fellows to review the importance of distress screening and to instruct fellows on instructions for the consult flowsheet.

Funding

This program was funded by the Chicago-based Coleman Foundation as part of the CSOC. Funding was used to support a portion of time for administrative and clinical work of program staff, as well as data collection and analysis.

Results

We established 3 half-day integrated clinics where patients were screened and referred for services based on supportive oncology needs. In addition to our primary activities to screen and refer veterans, we held multiple educational sessions for colleagues, developed a workflow template, and integrated patient education materials into the clinics.

Screening

Veterans completed 1010 distress screens in 3 of 4 half-day oncology clinics over the 2.5-year project period. Veterans were screened at initial diagnosis and every 3 months, or during changes in their clinical care or disease status. As a result, 579 patients completed screening, with some patients doing several follow-up screens during their care. Integration of palliative care providers and health psychologists was instrumental in facilitating screening in these busy general medical oncology clinics. Most veterans were receptive to completing surveys with few refusing to fill out the survey.²³ Medical oncology fellows often used the completed screener to inform their review of systems (by reviewing the Coleman screener Physical and Other Concerns section) and connect with the supportive care staff present in clinic for patient’s identifying severe needs (ie, mental health distress or complex psychosocial needs). Veterans’ rates of distress needs and successfuloutcomes of integration with mental health and social work services have been reported elsewhere.²³

The mean (SD) age for veterans in this cohort was 72 (9.5) years. Participants were primarily African American veterans (70%), with mostly advanced disease (Table 1). Participants endorsed elevated distress needs compared with other patient populations screened in Chicago through the CSOC for depressed mood, pain, housing, transportation, and physical, nutrition, and treatment concerns.²³ Elevated presence of needs was especially prominent for food, housing and insurance/medical needs; physical concerns; nutrition, and treatment- or care-related concerns. Veterans in this cohort reported extensive financial and housing concerns: 10.4% reported food and housing concerns, 18.6% reported transportation concerns, and 9.0% reported issues paying for medical care or medications (Table 2).²⁰ Anecdotally, many experienced job loss or strain with their cancer diagnosis or were living at the poverty level before their diagnosis.

Discussion

This project created a successful program to screen veterans for psychosocial distress and triage them to appropriate services. During the project, patients in VA-outpatient oncology clinics reported significant cancer-related distress due to baseline psychosocial needs, changes in emotional and physical functioning, logistical and financial challenges of receiving cancer care, and lack of instrumental support.²³

Staff education supported successful buy-in, development and implementation of supportive oncology programs. We used a combination of in-service trainings, online trainings, and handouts to provide evidence for distress screening.²⁴ Highlighting the evidence-base that demonstrates how cancer-related distress screening improves cancer and quality of life outcomes helped to address physician reluctance to accept the additional requirements needed to address veterans’ psychosocial needs and care concerns. To increase buy-in and collaboration among team members and foster heightened understanding between providers and patients, we recommend creating accessible education for all staff levels.

One specific area of education we focused on was primary palliative care, which includes the core competencies of communication and symptom management recommended for generalists and specialists of all disciplines.²⁵ Program staff supported oncology fellows in developing their primary palliative care skills by being available to discuss basic symptom management and communication issues. VA cancer care programs could benefit from ongoing palliative care education of oncology staff to facilitate primary palliative care as well as earlier integration of secondary palliative care when needed.²⁶ Secondary palliative care or care provided directly by the palliative care team assists with complex symptom management or communication issues. For these needs, oncology fellows were encouraged to refer to either the palliative care staff available in one of the half-day clinics or to the outpatient palliative care clinic. As a unique strength, the VA allows veterans to receive concurrent cancer-directed therapy and hospice care, which enables earlier referrals to hospice care and higher quality end-of-life care and emphasizes the need for primary palliative care in oncology.^27,28

Integrating supportive oncology team members, such as licensed clinical social worker and psychology interns, was successful. This was modeled on the VA PACT, which focuses on prevention, health promotion, coordination and chronic disease management.²⁹ Social determinants of health have a major impact on health outcomes especially in veteran-specific and African American populations, making screening for distress critical.^30-32 The VA Office of Health Equity actively addresses health inequities by supporting initiation of screening programs for social determinants of health, including education, employment, exposure to abuse and violence, food insecurity, housing instability, legal needs, social isolation, transportation needs, and utility needs. This is especially needed for African-American individuals who are not only more likely to experience cancer, but also more likely to be negatively impacted by the consequences of cancer diagnosis/treatment, such as complications related to one’s job security, access to care, adverse effects, and other highly distressing needs.^33,34

Our program found that veterans with cancer often had concerns associated with food and housing insecurity, transportation and paying for medication or medical care, and screening allowed health care providers to detect and address these social determinants of health through referrals to VA and community-specific programs. Social workers integrated into VA cancer clinics are uniquely equipped to coordinate distress screening and support continuity of care by virtue of their training, connections to preexisting VA supportive services, and knowledge of community resources. This model could be used in other VA specialty clinics serving veterans with chronic illness and those with high levels of physical frailty.³⁵

Our ability to roll out distress screening was scaffolded by technological integration into existing VA systems (eg, screening results in CPRS and electronic referrals). Screening procedures could have been even more efficient with improved technology (Table 3). For example, technological limitations made it challenging to easily identify patients due for screening, requiring a cumbersome process of tracking, collecting and entering patients’ paper forms. Health care providers seeking to develop a distress screening program should consider investing in technology that allows for identification of patients requiring screening at a predetermined interval, completion of screening via tablet or personal device, integration of screening responses into the electronic health record, and automatic generation of notifications to the treating physician and appropriate support services.

Conclusions

Veterans with cancer benefited from enhanced screening and psychosocial service availability, similar to a PCMHI model. Robust screening programs helped advocate for veterans dealing with the effects of poverty through identification of need and referral to existing VA programs and services quickly and efficiently. Providing comprehensive care within ambulatory cancer clinics can address cancer-related distress and any potential barriers to care in real time. VA hospitals typically offer an array of supportive services to address veterans’ psychosocial needs, yet these services tend to be siloed. Integrated referrals can help to resolve such access barriers. Since many veterans with burdensome cancers are not able to see their VA primary care physician regularly, offering comprehensive care within medical oncology ensures complete and integrated care that includes psychosocial screening.

We believe that this program is an example of a mechanism for oncologists and palliative care clinicians to integrate their care in a way that identifies needs and triages services for vulnerable veterans. As colleagues have written, “it is fundamental to our commitment to veterans that we ensure comparable, high quality care regardless of a veteran’s gender, race, or where they live.^”34 Health care providers may underestimate the extensive change a cancer diagnosis can have on a patient’s quality of life. Cancer diagnosis and treatment have a large impact on all individuals, but this impact may be greater for individuals in poverty due to inability to work from home, inflexible work hours, and limited support structures. By creating screening programs with psychosocial integration in oncology clinics such as we have described, we hope to improve access to more equitable care for vulnerable veterans.

Veterans living with cancer need comprehensive assessment that includes supportive psychosocial care. The National Comprehensive Cancer Network (NCCN) and American College of Surgeons Commission on Cancer require accredited cancer centers to evaluate psychosocial distress and provide appropriate triage and treatment for all patients.^1-3 Implementing psychosocial distress screening can be difficult because of procedural barriers and time constraints, clinic and supportive care resources, and lack of knowledge about how to access supportive services.

Distress screening protocols must be designed to address the specific needs of each population. To improve screening for cancer-related distress, deliver effective supportive services, and gain agreement on distress screening standards of care, the Coleman Foundation supported development of the Coleman Supportive Oncology Collaborative (CSOC), a project of 135 interdisciplinary health care professionals from 25 Chicago-area cancer care institutions.⁴

The Jesse Brown US Department of Veterans Affairs (VA) Medical Center (JBVAMC) was chosen to assess cancer-related concerns among veterans using the CSOC screening tool and to improve access to supportive oncology. JBVAMC provides care to approximately 49,000 veterans in Chicago, Illinois, and northwestern Indiana. The JBVAMC patient population includes a large number of veterans with dual diagnoses (co-occurring substance use and mental health disorders) and veterans experiencing homelessness.

Delivering integrated screening and oncologic care that is culture and age appropriate is particularly important for veterans given their unique risk factors. The veteran population is considered vulnerable in terms of health status, psychological functioning, and social context. Veterans who use the VA health system as a principal source of care have poorer health, greater comorbid medical conditions, and an increased risk of mortality and suicide compared with the general population.^5,6 Poorer health status in veterans also may relate to old age, low income, poor education, psychological health, and minority race.^7-9

Past studies point to unique risk factors for cancer and poor cancer adjustment among veterans, which may complicate cancer treatment and end-of-life/survivorship care. Veteran-specific risk factors include military-related exposures, particularly Agent Orange and morbidity/mortality secondary to comorbid medical and psychiatric conditions (eg, chronic obstructive pulmonary disease, diabetes mellitus, and posttraumatic stress disorder [PTSD]).^10-12 Moreover, the geriatric veteran population continues to grow,with increasing rates of cancer that require unique considerations for effective cancer care.^13,14 Despite this, there are minimal data to inform best practices and supportive care approaches for veterans with cancer. Lack of guidelines specific to veterans and other populations with increased psychosocial challenges may impede successful cancer care, making distress screening procedures particularly important. This is especially the case for the JBVAMC, which serves primarily African American urban-dwelling veterans who experience high rates of cancer disparities, including increased rates of mortality and increased levels of psychosocial distress.^15,16

The goals of this program were to (1) examine levels of psychological, physical, financial, and treatment-related distress in a large sample of urban-dwelling veterans; (2) create a streamlined, sustainable process to screen a large number of veterans receiving cancer care in the outpatient setting and connect them with available supportive services; and (3) educate oncology physicians, nurses, and other staff about cancer-related distress and concerns using in-service trainings and interpersonal interactions to improve patient care. Our program was based on a Primary Care Mental Health Integration (PCMHI) model that embeds health psychologists in general medical clinics to better reach veterans dealing with mental health issues. We tailored for palliative care involvement.

Studies of this model have shown that mental health integration improves access to mental health services and mental health treatment outcomes and has higher patient and provider satisfaction.¹⁷ We were also influenced by the construct of the patient aligned care team (PACT) social worker who, in this veteran-centered approach, often functions as a care coordinator. Social work responsibilities include assessment of patients’ stressors including adjusting to the medical conditions, identifying untreated or undertreated mental health or substance abuse issues, economic instability, legal problems, and inadequate housing and transportation, which can often be exacerbated during cancer treatment.¹⁸

Methods

This institutional review board at JBVAMC reviewed and exempted this quality improvement program using the SQUIRE framework.¹⁹ It was led by a group of palliative care clinicians, psychologists, and administrators who have worked with the oncology service for many years, primarily in the care of hospitalized patients. Common palliative care services include providing care for patients with serious illness diagnosis through the illness trajectory.

Setting

At the start of this program, we assessed the current clinic workflow to determine how to best screen and assist veterans experiencing distress. We met with team members individually to identify the best method of clinic integration, including attending medical oncologists, medical oncology fellows, psychology interns, oncology nursing staff, the oncology nurse coordinator, and clinic clerks.

The JBVAMC provides cancer care through 4 half-day medical hematology-oncology clinics that serve about 50 patients per half-day clinic. The clinics are staffed by hematology-oncology fellows supervised by hematology-oncology attending physicians, who are affiliated with 2 academic medical centers. These clinics are staffed by 3 registered nurses (RNs) and a licensed practical nurse (LPN) and are adjacent to a chemotherapy infusion clinic with unique nursing staff. The JBVAMC also provides a variety of supportive care services, including extensive mental health and substance use treatment, physical and occupational therapy, acupuncture, nutrition, social work, and housing services. Following our assessment, it was evident that there were a low number of referrals from oncology clinics to supportive care services, mostly due to lack of knowledge of resources and unclear referral procedures.

Based on clinical volume, we determined that our screening program could best be implemented through a stepped approach beginning in one clinic and expanding thereafter. We began by having a palliative care physician and health psychology intern embedded in 1 weekly half-day clinic and a health psychology intern embedded in a second weekly half-day clinic. Our program included 2 health psychology interns (for each academic year of the program) who were supervised by a JBVA health psychologist.

About 15 months after successful integration within the first 2 half-day clinics, we expanded the screening program to staff an additional half-day medical oncology clinic with a palliative care APRN. This allowed us to expand the screening tool distribution and collection to 3 of 4 of the weekly half-day oncology clinics as well as to meet individually with veterans experiencing high levels of distress. Veterans were flagged as having high distress levels by either the results of their completed screening tool or by referral from a medical oncology physician. We initially established screening in clinics that were sufficiently staffed to ensure that screens were appropriately distributed and reviewed. Patients seen in nonparticipating clinics were referred to outpatient social work, mental health and/or outpatient palliative care according to oncology fellows’ clinical assessments of the patient. All oncology fellows received education about distress screening and methods for referring to supportive care. Our clinic screening program extended from February 2017 through January 2020.

Screening

Program staff screened patients with new cancer diagnoses, then identified patients for follow-up screens. This tracking allowed staff to identify patients with oncology appointments that day and cross-reference patients needing a follow-up screen.

Following feedback from the clinic nurses, we determined that nurses would provide the distress tool to patients in paper form after they completed their assessment of vitals and waited to be seen by their medical oncologist. The patient would then deliver their completed form to the nurse who would combine it with the patient’s clinic notes for the oncologist to review. Veterans who reported elevated 4-question Patient Health Questionnaire (PHQ-4) scores ≥ 6 were seen immediately by program staff. Veterans were referred to social work or psychiatry services for a same day visit if they endorsed a high level of psychological distress during clinical examination. They were referred for other supportive care services if they were determined to have practical, family, or nutrition unmet distress needs by either the program staff or oncology fellows. Program staff provided guidance to medical oncology fellows for needed referrals including social work, mental health, and palliative care follow ups (eAppendix A available at doi:10.12788/fp.0158).

Veterans referred for supportive care services were contacted by the relevant clinical administrator by phone to schedule an intake; for social work referrals, patients were either seen in a walk-in office located in a colocated building or contacted by a social worker by phone.

Our screening tool was the Coleman Foundation Supportive Oncology Collaborative Screening Tool, compiled from validated instruments. Patients completed this screening tool, which includes the PHQ-4, NCCN problem list concerns, adapted Mini Nutrition Assessment and PROMIS Pain and Fatigue measure (eAppendix B available at doi:10.12788/fp.0158).^20-22

We also worked with the VA Computerized Patient Record System (CPRS) to create an electronic template for the screening tool. Completed screening tools were manually entered by the physician, psychologists, or APRN into the CPRS chart.

We analyzed the different supportive care services available at the JBVAMC and noticed that many supportive services were available, yet these services were often separated. Therefore, we created a consult flowsheet to assist oncologists in placing referrals. These supportive care services include mental health services, a cancer support group, home health care, social services, nutrition, physical medicine and rehabilitation, and other specialty services.

Patient Education

The psychology and nursing staff created a patient information bulletin board where patients could access information about supportive services available at JBVAMC. This board required frequent replenishment of handouts because patients consulted the board regularly. Handouts and folders about common clinical issues also were placed in the clinic treatment rooms. We partnered with 2 local cancer support centers, Gilda’s Club and the Cancer Support Center, to make referrals for family members and/or caregivers who would benefit from additional support.

We provided in-service trainings for oncology fellows, including trainings on PTSD and substance abuse and their relationship to cancer care at the VA. These topics were chosen based on the feedback program staff received about perceived knowledge gaps from the oncology fellows. This program allowed for multiple informal conversations between that program staff and oncology fellows about overall patient care. We held trainings with the cancer coordinator and clinical nursing staff on strategies to identify and follow-up on cancer-related distress, and with oncology fellows to review the importance of distress screening and to instruct fellows on instructions for the consult flowsheet.

Funding

This program was funded by the Chicago-based Coleman Foundation as part of the CSOC. Funding was used to support a portion of time for administrative and clinical work of program staff, as well as data collection and analysis.

Results

We established 3 half-day integrated clinics where patients were screened and referred for services based on supportive oncology needs. In addition to our primary activities to screen and refer veterans, we held multiple educational sessions for colleagues, developed a workflow template, and integrated patient education materials into the clinics.

Screening

Veterans completed 1010 distress screens in 3 of 4 half-day oncology clinics over the 2.5-year project period. Veterans were screened at initial diagnosis and every 3 months, or during changes in their clinical care or disease status. As a result, 579 patients completed screening, with some patients doing several follow-up screens during their care. Integration of palliative care providers and health psychologists was instrumental in facilitating screening in these busy general medical oncology clinics. Most veterans were receptive to completing surveys with few refusing to fill out the survey.²³ Medical oncology fellows often used the completed screener to inform their review of systems (by reviewing the Coleman screener Physical and Other Concerns section) and connect with the supportive care staff present in clinic for patient’s identifying severe needs (ie, mental health distress or complex psychosocial needs). Veterans’ rates of distress needs and successfuloutcomes of integration with mental health and social work services have been reported elsewhere.²³

The mean (SD) age for veterans in this cohort was 72 (9.5) years. Participants were primarily African American veterans (70%), with mostly advanced disease (Table 1). Participants endorsed elevated distress needs compared with other patient populations screened in Chicago through the CSOC for depressed mood, pain, housing, transportation, and physical, nutrition, and treatment concerns.²³ Elevated presence of needs was especially prominent for food, housing and insurance/medical needs; physical concerns; nutrition, and treatment- or care-related concerns. Veterans in this cohort reported extensive financial and housing concerns: 10.4% reported food and housing concerns, 18.6% reported transportation concerns, and 9.0% reported issues paying for medical care or medications (Table 2).²⁰ Anecdotally, many experienced job loss or strain with their cancer diagnosis or were living at the poverty level before their diagnosis.

Discussion

This project created a successful program to screen veterans for psychosocial distress and triage them to appropriate services. During the project, patients in VA-outpatient oncology clinics reported significant cancer-related distress due to baseline psychosocial needs, changes in emotional and physical functioning, logistical and financial challenges of receiving cancer care, and lack of instrumental support.²³

Staff education supported successful buy-in, development and implementation of supportive oncology programs. We used a combination of in-service trainings, online trainings, and handouts to provide evidence for distress screening.²⁴ Highlighting the evidence-base that demonstrates how cancer-related distress screening improves cancer and quality of life outcomes helped to address physician reluctance to accept the additional requirements needed to address veterans’ psychosocial needs and care concerns. To increase buy-in and collaboration among team members and foster heightened understanding between providers and patients, we recommend creating accessible education for all staff levels.

One specific area of education we focused on was primary palliative care, which includes the core competencies of communication and symptom management recommended for generalists and specialists of all disciplines.²⁵ Program staff supported oncology fellows in developing their primary palliative care skills by being available to discuss basic symptom management and communication issues. VA cancer care programs could benefit from ongoing palliative care education of oncology staff to facilitate primary palliative care as well as earlier integration of secondary palliative care when needed.²⁶ Secondary palliative care or care provided directly by the palliative care team assists with complex symptom management or communication issues. For these needs, oncology fellows were encouraged to refer to either the palliative care staff available in one of the half-day clinics or to the outpatient palliative care clinic. As a unique strength, the VA allows veterans to receive concurrent cancer-directed therapy and hospice care, which enables earlier referrals to hospice care and higher quality end-of-life care and emphasizes the need for primary palliative care in oncology.^27,28

Integrating supportive oncology team members, such as licensed clinical social worker and psychology interns, was successful. This was modeled on the VA PACT, which focuses on prevention, health promotion, coordination and chronic disease management.²⁹ Social determinants of health have a major impact on health outcomes especially in veteran-specific and African American populations, making screening for distress critical.^30-32 The VA Office of Health Equity actively addresses health inequities by supporting initiation of screening programs for social determinants of health, including education, employment, exposure to abuse and violence, food insecurity, housing instability, legal needs, social isolation, transportation needs, and utility needs. This is especially needed for African-American individuals who are not only more likely to experience cancer, but also more likely to be negatively impacted by the consequences of cancer diagnosis/treatment, such as complications related to one’s job security, access to care, adverse effects, and other highly distressing needs.^33,34

Our program found that veterans with cancer often had concerns associated with food and housing insecurity, transportation and paying for medication or medical care, and screening allowed health care providers to detect and address these social determinants of health through referrals to VA and community-specific programs. Social workers integrated into VA cancer clinics are uniquely equipped to coordinate distress screening and support continuity of care by virtue of their training, connections to preexisting VA supportive services, and knowledge of community resources. This model could be used in other VA specialty clinics serving veterans with chronic illness and those with high levels of physical frailty.³⁵

Our ability to roll out distress screening was scaffolded by technological integration into existing VA systems (eg, screening results in CPRS and electronic referrals). Screening procedures could have been even more efficient with improved technology (Table 3). For example, technological limitations made it challenging to easily identify patients due for screening, requiring a cumbersome process of tracking, collecting and entering patients’ paper forms. Health care providers seeking to develop a distress screening program should consider investing in technology that allows for identification of patients requiring screening at a predetermined interval, completion of screening via tablet or personal device, integration of screening responses into the electronic health record, and automatic generation of notifications to the treating physician and appropriate support services.

Conclusions

Veterans with cancer benefited from enhanced screening and psychosocial service availability, similar to a PCMHI model. Robust screening programs helped advocate for veterans dealing with the effects of poverty through identification of need and referral to existing VA programs and services quickly and efficiently. Providing comprehensive care within ambulatory cancer clinics can address cancer-related distress and any potential barriers to care in real time. VA hospitals typically offer an array of supportive services to address veterans’ psychosocial needs, yet these services tend to be siloed. Integrated referrals can help to resolve such access barriers. Since many veterans with burdensome cancers are not able to see their VA primary care physician regularly, offering comprehensive care within medical oncology ensures complete and integrated care that includes psychosocial screening.

We believe that this program is an example of a mechanism for oncologists and palliative care clinicians to integrate their care in a way that identifies needs and triages services for vulnerable veterans. As colleagues have written, “it is fundamental to our commitment to veterans that we ensure comparable, high quality care regardless of a veteran’s gender, race, or where they live.^”34 Health care providers may underestimate the extensive change a cancer diagnosis can have on a patient’s quality of life. Cancer diagnosis and treatment have a large impact on all individuals, but this impact may be greater for individuals in poverty due to inability to work from home, inflexible work hours, and limited support structures. By creating screening programs with psychosocial integration in oncology clinics such as we have described, we hope to improve access to more equitable care for vulnerable veterans.

Veterans living with cancer need comprehensive assessment that includes supportive psychosocial care. The National Comprehensive Cancer Network (NCCN) and American College of Surgeons Commission on Cancer require accredited cancer centers to evaluate psychosocial distress and provide appropriate triage and treatment for all patients.^1-3 Implementing psychosocial distress screening can be difficult because of procedural barriers and time constraints, clinic and supportive care resources, and lack of knowledge about how to access supportive services.

Distress screening protocols must be designed to address the specific needs of each population. To improve screening for cancer-related distress, deliver effective supportive services, and gain agreement on distress screening standards of care, the Coleman Foundation supported development of the Coleman Supportive Oncology Collaborative (CSOC), a project of 135 interdisciplinary health care professionals from 25 Chicago-area cancer care institutions.⁴

The Jesse Brown US Department of Veterans Affairs (VA) Medical Center (JBVAMC) was chosen to assess cancer-related concerns among veterans using the CSOC screening tool and to improve access to supportive oncology. JBVAMC provides care to approximately 49,000 veterans in Chicago, Illinois, and northwestern Indiana. The JBVAMC patient population includes a large number of veterans with dual diagnoses (co-occurring substance use and mental health disorders) and veterans experiencing homelessness.

Delivering integrated screening and oncologic care that is culture and age appropriate is particularly important for veterans given their unique risk factors. The veteran population is considered vulnerable in terms of health status, psychological functioning, and social context. Veterans who use the VA health system as a principal source of care have poorer health, greater comorbid medical conditions, and an increased risk of mortality and suicide compared with the general population.^5,6 Poorer health status in veterans also may relate to old age, low income, poor education, psychological health, and minority race.^7-9

Past studies point to unique risk factors for cancer and poor cancer adjustment among veterans, which may complicate cancer treatment and end-of-life/survivorship care. Veteran-specific risk factors include military-related exposures, particularly Agent Orange and morbidity/mortality secondary to comorbid medical and psychiatric conditions (eg, chronic obstructive pulmonary disease, diabetes mellitus, and posttraumatic stress disorder [PTSD]).^10-12 Moreover, the geriatric veteran population continues to grow,with increasing rates of cancer that require unique considerations for effective cancer care.^13,14 Despite this, there are minimal data to inform best practices and supportive care approaches for veterans with cancer. Lack of guidelines specific to veterans and other populations with increased psychosocial challenges may impede successful cancer care, making distress screening procedures particularly important. This is especially the case for the JBVAMC, which serves primarily African American urban-dwelling veterans who experience high rates of cancer disparities, including increased rates of mortality and increased levels of psychosocial distress.^15,16

The goals of this program were to (1) examine levels of psychological, physical, financial, and treatment-related distress in a large sample of urban-dwelling veterans; (2) create a streamlined, sustainable process to screen a large number of veterans receiving cancer care in the outpatient setting and connect them with available supportive services; and (3) educate oncology physicians, nurses, and other staff about cancer-related distress and concerns using in-service trainings and interpersonal interactions to improve patient care. Our program was based on a Primary Care Mental Health Integration (PCMHI) model that embeds health psychologists in general medical clinics to better reach veterans dealing with mental health issues. We tailored for palliative care involvement.

Studies of this model have shown that mental health integration improves access to mental health services and mental health treatment outcomes and has higher patient and provider satisfaction.¹⁷ We were also influenced by the construct of the patient aligned care team (PACT) social worker who, in this veteran-centered approach, often functions as a care coordinator. Social work responsibilities include assessment of patients’ stressors including adjusting to the medical conditions, identifying untreated or undertreated mental health or substance abuse issues, economic instability, legal problems, and inadequate housing and transportation, which can often be exacerbated during cancer treatment.¹⁸

Methods

This institutional review board at JBVAMC reviewed and exempted this quality improvement program using the SQUIRE framework.¹⁹ It was led by a group of palliative care clinicians, psychologists, and administrators who have worked with the oncology service for many years, primarily in the care of hospitalized patients. Common palliative care services include providing care for patients with serious illness diagnosis through the illness trajectory.

Setting

At the start of this program, we assessed the current clinic workflow to determine how to best screen and assist veterans experiencing distress. We met with team members individually to identify the best method of clinic integration, including attending medical oncologists, medical oncology fellows, psychology interns, oncology nursing staff, the oncology nurse coordinator, and clinic clerks.

The JBVAMC provides cancer care through 4 half-day medical hematology-oncology clinics that serve about 50 patients per half-day clinic. The clinics are staffed by hematology-oncology fellows supervised by hematology-oncology attending physicians, who are affiliated with 2 academic medical centers. These clinics are staffed by 3 registered nurses (RNs) and a licensed practical nurse (LPN) and are adjacent to a chemotherapy infusion clinic with unique nursing staff. The JBVAMC also provides a variety of supportive care services, including extensive mental health and substance use treatment, physical and occupational therapy, acupuncture, nutrition, social work, and housing services. Following our assessment, it was evident that there were a low number of referrals from oncology clinics to supportive care services, mostly due to lack of knowledge of resources and unclear referral procedures.

Based on clinical volume, we determined that our screening program could best be implemented through a stepped approach beginning in one clinic and expanding thereafter. We began by having a palliative care physician and health psychology intern embedded in 1 weekly half-day clinic and a health psychology intern embedded in a second weekly half-day clinic. Our program included 2 health psychology interns (for each academic year of the program) who were supervised by a JBVA health psychologist.

About 15 months after successful integration within the first 2 half-day clinics, we expanded the screening program to staff an additional half-day medical oncology clinic with a palliative care APRN. This allowed us to expand the screening tool distribution and collection to 3 of 4 of the weekly half-day oncology clinics as well as to meet individually with veterans experiencing high levels of distress. Veterans were flagged as having high distress levels by either the results of their completed screening tool or by referral from a medical oncology physician. We initially established screening in clinics that were sufficiently staffed to ensure that screens were appropriately distributed and reviewed. Patients seen in nonparticipating clinics were referred to outpatient social work, mental health and/or outpatient palliative care according to oncology fellows’ clinical assessments of the patient. All oncology fellows received education about distress screening and methods for referring to supportive care. Our clinic screening program extended from February 2017 through January 2020.

Screening

Program staff screened patients with new cancer diagnoses, then identified patients for follow-up screens. This tracking allowed staff to identify patients with oncology appointments that day and cross-reference patients needing a follow-up screen.

Following feedback from the clinic nurses, we determined that nurses would provide the distress tool to patients in paper form after they completed their assessment of vitals and waited to be seen by their medical oncologist. The patient would then deliver their completed form to the nurse who would combine it with the patient’s clinic notes for the oncologist to review. Veterans who reported elevated 4-question Patient Health Questionnaire (PHQ-4) scores ≥ 6 were seen immediately by program staff. Veterans were referred to social work or psychiatry services for a same day visit if they endorsed a high level of psychological distress during clinical examination. They were referred for other supportive care services if they were determined to have practical, family, or nutrition unmet distress needs by either the program staff or oncology fellows. Program staff provided guidance to medical oncology fellows for needed referrals including social work, mental health, and palliative care follow ups (eAppendix A available at doi:10.12788/fp.0158).

Veterans referred for supportive care services were contacted by the relevant clinical administrator by phone to schedule an intake; for social work referrals, patients were either seen in a walk-in office located in a colocated building or contacted by a social worker by phone.

Our screening tool was the Coleman Foundation Supportive Oncology Collaborative Screening Tool, compiled from validated instruments. Patients completed this screening tool, which includes the PHQ-4, NCCN problem list concerns, adapted Mini Nutrition Assessment and PROMIS Pain and Fatigue measure (eAppendix B available at doi:10.12788/fp.0158).^20-22

We also worked with the VA Computerized Patient Record System (CPRS) to create an electronic template for the screening tool. Completed screening tools were manually entered by the physician, psychologists, or APRN into the CPRS chart.

We analyzed the different supportive care services available at the JBVAMC and noticed that many supportive services were available, yet these services were often separated. Therefore, we created a consult flowsheet to assist oncologists in placing referrals. These supportive care services include mental health services, a cancer support group, home health care, social services, nutrition, physical medicine and rehabilitation, and other specialty services.

Patient Education

The psychology and nursing staff created a patient information bulletin board where patients could access information about supportive services available at JBVAMC. This board required frequent replenishment of handouts because patients consulted the board regularly. Handouts and folders about common clinical issues also were placed in the clinic treatment rooms. We partnered with 2 local cancer support centers, Gilda’s Club and the Cancer Support Center, to make referrals for family members and/or caregivers who would benefit from additional support.

We provided in-service trainings for oncology fellows, including trainings on PTSD and substance abuse and their relationship to cancer care at the VA. These topics were chosen based on the feedback program staff received about perceived knowledge gaps from the oncology fellows. This program allowed for multiple informal conversations between that program staff and oncology fellows about overall patient care. We held trainings with the cancer coordinator and clinical nursing staff on strategies to identify and follow-up on cancer-related distress, and with oncology fellows to review the importance of distress screening and to instruct fellows on instructions for the consult flowsheet.

Funding

This program was funded by the Chicago-based Coleman Foundation as part of the CSOC. Funding was used to support a portion of time for administrative and clinical work of program staff, as well as data collection and analysis.

Results

We established 3 half-day integrated clinics where patients were screened and referred for services based on supportive oncology needs. In addition to our primary activities to screen and refer veterans, we held multiple educational sessions for colleagues, developed a workflow template, and integrated patient education materials into the clinics.

Screening

Veterans completed 1010 distress screens in 3 of 4 half-day oncology clinics over the 2.5-year project period. Veterans were screened at initial diagnosis and every 3 months, or during changes in their clinical care or disease status. As a result, 579 patients completed screening, with some patients doing several follow-up screens during their care. Integration of palliative care providers and health psychologists was instrumental in facilitating screening in these busy general medical oncology clinics. Most veterans were receptive to completing surveys with few refusing to fill out the survey.²³ Medical oncology fellows often used the completed screener to inform their review of systems (by reviewing the Coleman screener Physical and Other Concerns section) and connect with the supportive care staff present in clinic for patient’s identifying severe needs (ie, mental health distress or complex psychosocial needs). Veterans’ rates of distress needs and successfuloutcomes of integration with mental health and social work services have been reported elsewhere.²³

The mean (SD) age for veterans in this cohort was 72 (9.5) years. Participants were primarily African American veterans (70%), with mostly advanced disease (Table 1). Participants endorsed elevated distress needs compared with other patient populations screened in Chicago through the CSOC for depressed mood, pain, housing, transportation, and physical, nutrition, and treatment concerns.²³ Elevated presence of needs was especially prominent for food, housing and insurance/medical needs; physical concerns; nutrition, and treatment- or care-related concerns. Veterans in this cohort reported extensive financial and housing concerns: 10.4% reported food and housing concerns, 18.6% reported transportation concerns, and 9.0% reported issues paying for medical care or medications (Table 2).²⁰ Anecdotally, many experienced job loss or strain with their cancer diagnosis or were living at the poverty level before their diagnosis.

Discussion

This project created a successful program to screen veterans for psychosocial distress and triage them to appropriate services. During the project, patients in VA-outpatient oncology clinics reported significant cancer-related distress due to baseline psychosocial needs, changes in emotional and physical functioning, logistical and financial challenges of receiving cancer care, and lack of instrumental support.²³

Staff education supported successful buy-in, development and implementation of supportive oncology programs. We used a combination of in-service trainings, online trainings, and handouts to provide evidence for distress screening.²⁴ Highlighting the evidence-base that demonstrates how cancer-related distress screening improves cancer and quality of life outcomes helped to address physician reluctance to accept the additional requirements needed to address veterans’ psychosocial needs and care concerns. To increase buy-in and collaboration among team members and foster heightened understanding between providers and patients, we recommend creating accessible education for all staff levels.

One specific area of education we focused on was primary palliative care, which includes the core competencies of communication and symptom management recommended for generalists and specialists of all disciplines.²⁵ Program staff supported oncology fellows in developing their primary palliative care skills by being available to discuss basic symptom management and communication issues. VA cancer care programs could benefit from ongoing palliative care education of oncology staff to facilitate primary palliative care as well as earlier integration of secondary palliative care when needed.²⁶ Secondary palliative care or care provided directly by the palliative care team assists with complex symptom management or communication issues. For these needs, oncology fellows were encouraged to refer to either the palliative care staff available in one of the half-day clinics or to the outpatient palliative care clinic. As a unique strength, the VA allows veterans to receive concurrent cancer-directed therapy and hospice care, which enables earlier referrals to hospice care and higher quality end-of-life care and emphasizes the need for primary palliative care in oncology.^27,28

Integrating supportive oncology team members, such as licensed clinical social worker and psychology interns, was successful. This was modeled on the VA PACT, which focuses on prevention, health promotion, coordination and chronic disease management.²⁹ Social determinants of health have a major impact on health outcomes especially in veteran-specific and African American populations, making screening for distress critical.^30-32 The VA Office of Health Equity actively addresses health inequities by supporting initiation of screening programs for social determinants of health, including education, employment, exposure to abuse and violence, food insecurity, housing instability, legal needs, social isolation, transportation needs, and utility needs. This is especially needed for African-American individuals who are not only more likely to experience cancer, but also more likely to be negatively impacted by the consequences of cancer diagnosis/treatment, such as complications related to one’s job security, access to care, adverse effects, and other highly distressing needs.^33,34

Our program found that veterans with cancer often had concerns associated with food and housing insecurity, transportation and paying for medication or medical care, and screening allowed health care providers to detect and address these social determinants of health through referrals to VA and community-specific programs. Social workers integrated into VA cancer clinics are uniquely equipped to coordinate distress screening and support continuity of care by virtue of their training, connections to preexisting VA supportive services, and knowledge of community resources. This model could be used in other VA specialty clinics serving veterans with chronic illness and those with high levels of physical frailty.³⁵

Our ability to roll out distress screening was scaffolded by technological integration into existing VA systems (eg, screening results in CPRS and electronic referrals). Screening procedures could have been even more efficient with improved technology (Table 3). For example, technological limitations made it challenging to easily identify patients due for screening, requiring a cumbersome process of tracking, collecting and entering patients’ paper forms. Health care providers seeking to develop a distress screening program should consider investing in technology that allows for identification of patients requiring screening at a predetermined interval, completion of screening via tablet or personal device, integration of screening responses into the electronic health record, and automatic generation of notifications to the treating physician and appropriate support services.

Conclusions

Veterans with cancer benefited from enhanced screening and psychosocial service availability, similar to a PCMHI model. Robust screening programs helped advocate for veterans dealing with the effects of poverty through identification of need and referral to existing VA programs and services quickly and efficiently. Providing comprehensive care within ambulatory cancer clinics can address cancer-related distress and any potential barriers to care in real time. VA hospitals typically offer an array of supportive services to address veterans’ psychosocial needs, yet these services tend to be siloed. Integrated referrals can help to resolve such access barriers. Since many veterans with burdensome cancers are not able to see their VA primary care physician regularly, offering comprehensive care within medical oncology ensures complete and integrated care that includes psychosocial screening.

We believe that this program is an example of a mechanism for oncologists and palliative care clinicians to integrate their care in a way that identifies needs and triages services for vulnerable veterans. As colleagues have written, “it is fundamental to our commitment to veterans that we ensure comparable, high quality care regardless of a veteran’s gender, race, or where they live.^”34 Health care providers may underestimate the extensive change a cancer diagnosis can have on a patient’s quality of life. Cancer diagnosis and treatment have a large impact on all individuals, but this impact may be greater for individuals in poverty due to inability to work from home, inflexible work hours, and limited support structures. By creating screening programs with psychosocial integration in oncology clinics such as we have described, we hope to improve access to more equitable care for vulnerable veterans.

The COVID-19 outbreak in February 2020 at a congregate living facility near Seattle, Washington, signaled the beginning of the pandemic in the United States. In that facility, infected residents had a 54.5% hospitalization rate and 33.7% case-fatality rate.¹ Similar to the experience in Washington, all congregate living facilities have proved particularly vulnerable to the effects of COVID-19,^2-7 with residents at increased risk for disease severity and mortality.^2-7

Due to the COVID-19 emergency, NorthShore University HealthSystem (NUHS), a multihospital, integrated health system in northern Illinois, established a best practice for appropriate use of congregate living facilities after hospitalization. This focused on the safety of discharged patients and mitigation of COVID-19 by putting in place a referral process to a newly established congregate living review committee (CLRC) for review prior to discharge. Although all discharges to congregate living settings are at high risk,² new placements to skilled nursing facilities (SNFs) were the primary focus of the committee and the sole focus of this study. In this study, we sought to determine whether establishment of the CLRC was associated with a reduction in SNF utilization, whether this was safe and efficient, and whether it was associated with a reduction in COVID-19 incidence in the 30 days following discharge.

Setting and Case Review Intervention

We conducted a retrospective cohort study for patients hospitalized within NUHS from March 19, 2019 to July 16, 2020, designed as an interrupted time series. The study was approved by the NUHS Institutional Review Board (EH21-022).

The study exposure was creation of a referral and review process for all patients with expected discharge to a SNF and was implemented as part of usual discharge planning during the COVID-19 pandemic. The key intervention was to establish a multidisciplinary committee, the CLRC, to review all potential discharges to SNFs. The CLRC had dual goals of preventing COVID-19 spread in facilities by limiting placement of new residents and protecting a vulnerable population from a setting that conferred a higher risk of acquiring COVID-19. The CLRC was organized as a multidisciplinary committee with physicians, case managers, social workers, physical therapists, occupational therapists, and the director of NUHS home health agency. Physician members were evenly split as half hospitalists and half ambulatory physicians. The CLRC review was initiated by a patient’s assigned case manager or social worker by consult through a referral in the electronic medical record (EMR). Each case was summarized and then presented to the full CLRC. The CLRC met for 1 hour per day, 6 days per week, to review all planned discharges that met criteria for review. A committee physician chaired each meeting. Three other members were needed for a quorum, with one other member with a title of director or higher. Time required was the 1-hour daily meeting, as well as one full-time position for case review, preparation, and program administration. The case presentation included a clinical summary of the hospitalization as well as COVID-19 status and testing history, previous living situation, level of home support, functional level, psychosocial needs, barrier(s) to discharging home, and long-term residential plans. A structured assessment was then made by each CLRC member in accordance with their professional expertise. Unanimous consensus would be reached before finalizing any recommended adjustments to the discharge, which would be communicated to the inpatient care team via a structured note within the EMR, along with direct communication to the assigned case manager or social worker. When the CLRC suggested adjustments to the discharge, they would work with the assigned case manager or social worker to communicate an appropriate post–acute care plan with the patient or appropriate representative. If there was disagreement or the recommendations could not be followed, the case manager or social worker would place a new referral with additional information for reconsideration. Following a recommendation for SNF, verification would be completed by the CLRC prior to discharge. This process is detailed in Figure 1.

Patient Population

Inclusion criteria for the study were: (1) inpatient hospitalization and (2) eligibility for risk scoring via the organization’s clinical analytics prediction engine (CAPE).⁸ CAPE is a validated predictive model that includes risk of readmission, in-hospital mortality, and out-of-hospital mortality,⁸ with extensive adoption at NUHS. CAPE score eligibility was used as an inclusion criterion so that CAPE could be applied for derivation of a matched control. CAPE eligibility criteria include admission age of at least 18 years and that hospitalization is not psychiatric, rehabilitative, or obstetric. Patients must not be enrolled in hospice and must be discharged alive.

Exclusions were patients who tested positive for SARS-CoV-2 prior to or during index hospitalization. Excluding COVID-19 patients from the analysis eliminated a confounder not present in the preintervention group.

For patients with multiple inpatient admissions, the first admission was the only admission used for analysis. Additionally, if a patient had an admission that occurred in both the preintervention and postintervention periods, they were included only in the postintervention period. This was done to avoid any within-subject correlation and ensure unique patients in each group. Confounding from this approach was mitigated through the process of deriving a matched control.

Outcomes Measurement

The primary outcome of interest was total discharges to SNF across NUHS facilities after hospital admission. Patients were identified as discharging to a SNF if discharge destination codes 03, 64, or 83 appeared on the hospital bill. Additionally, new discharges to SNFs were assessed and identified if documentation indicated that the patient’s living arrangement prior to admission was not a SNF but discharge billing destination codes 03, 64, or 83 appeared on the hospital bill.

Secondary outcomes were measurement of readmissions, days to readmission, and median length of stay (LOS). Readmissions and LOS were balancing measures for the primary outcome, with readmissions measured to evaluate the safety of the CLRC process and LOS measured to evaluate its efficiency. A readmission was any patient who had an unplanned inpatient admission at an NUHS facility within 30 days after an index admission. LOS was measured in days from arrival on a hospital unit to time of discharge.

Additional analysis was done to estimate the effect of the intervention on the incidence of COVID-19 in the 30 days following discharge by comparing the observed to expected incidence of COVID-19 by discharge destination. The expected values were derived by estimating COVID-19 cases that would have been expected to occur with rates of preintervention SNF utilization. This was accomplished by multiplying the observed incidence of COVID-19 in the 30 days following discharge by the number of patients who were discharged to SNFs or home/other in the preintervention period. This expected value was then compared with the observed values to estimate the effect size of the intervention on COVID-19 incidence following discharge. This method of deriving an expected value from the observed incidence was utilized because the preintervention period was before COVID-19 was widespread in the community. It was therefore not possible to directly measure COVID-19 incidence in the preintervention period.

Data Source

Data were retrieved from the NUHS Enterprise Data Warehouse, NUHS’s central data repository, which contains a nightly upload of clinical and financial data from the EMR. Data were collected between March 19, 2019, and July 16, 2020.

The preintervention period was defined as March 19, 2019, to March 18, 2020. Data from that interval were compared with the postintervention period, which was from March 19, 2020, to July 16, 2020. The preintervention period, 1 year immediately prior to the intervention, was chosen to limit any effect of temporal trends while also providing a large sample size. The postintervention period began on the first day NUHS implemented the revised approach to SNF use and ended on the last day before the review process was modified.

Data Analysis

An interrupted time series was used to measure the impact of adoption of the CLRC protocol. A matched control was derived from the preintervention population. To derive this matched control, there was an assessment of covariates in the preintervention and postintervention groups using a standardized mean difference (SMD)⁹ that indicated an imbalance (SMD ≥ 0.1) in some covariates. A propensity score–matching technique¹⁰ was applied to address this imbalance and lack of randomization.

The candidate variables for propensity matching were chosen if they had an association with 30-day readmission. Readmission was chosen to find candidate variables because, of the possible outcomes, this was the only one that was not directly impacted by any CLRC decision. Each covariate was assessed using a logistic regression model while controlling for the postintervention group. If there was an association between a covariate and the outcome, it was chosen for propensity matching. Propensity scores were calculated using a logistic regression model with the treatment (1/0) variable as the dependent variable and the chosen covariates as predictors.

There were no indications of strong multicollinearity. The propensity scores generated were then used to derive a matched control using paired matching. MatchIt package in R (R Foundation for Statistical Computing) was used to create a matched dataset with a logit distance and standard caliper of 0.2 times the standard deviations of the logit of the propensity score. If a match was not found within the caliper, the nearest available match was used.

Regression adjustment¹¹ was then performed using multivariate linear/logistic regression with LOS, readmission rate, days to readmission, total SNF discharges, and new SNF discharges as the outcomes. Treatment (1/0) variable and propensity score were used as the predictors. The adjusted coefficients or odds ratios (ORs) of the intervention variable were thus derived, and their associated P values were used to assess the impact of the intervention on the respective outcomes.

The unmatched preintervention population included 14,468 patients, with 4424 patients in the postintervention population. A matched population was derived and, after matching, the population sizes for pre and post intervention were 4424 each. In the matched population, all measured preintervention characteristics had SMDs and P values that were statistically equivalent. Patient characteristics for the unmatched and matched populations are detailed in Table 1.

During the preintervention period, 1130 (25.5%) patients were discharged to a SNF, with 776 (17.5%) patients being new SNF discharges. In the postintervention period, 568 (12.8%) patients were discharged to a SNF, with 257 (5.8%) patients being new SNF discharges. Total SNF discharges postintervention saw a 49.7% relative reduction (OR, 0.42; 95% CI, 0.38-0.47), while new SNF discharges saw a 66.9% relative reduction (OR, 0.29; 95% CI, 0.25-0.34). These results for both total and new SNF discharges were statistically significant, with P values of <.001, respectively.

Readmissions in the preintervention period were 529 (12.0%) patients, compared with 559 (12.6%) patients in the postintervention period (OR, 1.06; 95% CI, 0.93-1.20; P =.406). An OR was also calculated for readmissions, adjusting for discharge disposition, to account for changes observed in SNF use in the postintervention period. This OR was 1.11 (95% CI, 0.97-1.26; P = .131). Days to readmission in the preintervention and postintervention groups were 11.0 days and 12.0 days, respectively (OR, 0.41; 95% CI, –0.61 to 1.43; P = .429).

LOS was 3.61 days in the preintervention group and 3.64 days in the postintervention group, with an interquartile range (IQR) of 2.14 to 5.69 days in the preintervention group and 2.08 to 5.95 in the postintervention group (OR, 0.09; 95% CI, –0.09 to 0.27; P =.316). These results are summarized in Table 2.

A COVID-19 outbreak in a SNF presents a grave risk to residents and patients discharged to these facilities. It is critical for healthcare systems to do the utmost to protect the health of this vulnerable population and the public in efforts to limit COVID-19 within SNFs.^12-14

In this study, we observed that at NUHS, establishing a multidisciplinary review committee, the CLRC, to assess the appropriateness of discharge to a SNF after hospitalization resulted in a nearly 50% reduction in total SNF discharges and a greater than two-thirds reduction in new SNF discharges, without any increase in LOS or readmissions. Additionally, it was observed that discharging to settings other than a SNF greatly reduced a patient’s risk of being diagnosed with COVID-19 within 30 days, a result that reached statistical significance. Based on the observed 37.2% relative reduction in COVID-19 cases, we estimate that there may have been one COVID-19 infection prevented every 5.6 days from this intervention. Based on published COVID-19 mortality rates for SNF residents,¹ the intervention may have prevented one death every 2.6 weeks. Beyond the risk of COVID-19, other benefits of reducing SNF use are patient and family well-being. Although not measured in this study, others have published about the significant psychological burdens placed on SNF residents, who were at high risk for social isolation, anxiety, and depression during the COVID-19 pandemic^2,15-19 Family members also may have had increased stress, as they were deprived of the opportunity to visit loved ones, advocate for them, and help maintain their identity, humanity, and quality of life.²⁰

Although other hospitals have established a structured approach to reduce COVID-19 in SNFs,²¹ to the best of the authors’ knowledge, the approach described in this article is a unique response to the COVID-19 pandemic. As we have demonstrated, it is highly effective and safe and likely prevented many COVID-19 cases and deaths.

Furthermore, a review committee, such as the one we have described, has value well beyond the COVID-19 pandemic. The health and affordability of care for patients, provider success in value-based care models, and the long-term sustainability of the US healthcare system require close attention to appropriate use of expensive services and to ensuring that their use creates high value. SNF use after a hospitalization is one such service that is frequently targeted and thought to contribute to a substantial portion of wasteful medical spending.^22,23 Additionally, SNFs are known to be high risk for communicable disease outbreaks other than COVID-19,^24,25 as well as a high-risk environment for many other preventable adverse events.^25,26 This review committee ultimately serves to help determine the most appropriate postacute setting for patients being discharged with a determination made through considerations for patient safety, rehabilitation potential, and mental and physical well-being. From a population health perspective, this can lead to better outcomes and lower costs.^22,23 Therefore, although the risks of COVID-19 infection in SNFs are expected to subside, the work of evaluating appropriate use of SNFs after hospitalization at our institution continues. The broader focus now extends beyond postacute level of service toward ensuring a high-value discharge that results in both appropriate resource use and safe patient care transitions.

Limitations of this study include its retrospective nature, results from a single center, and a number of potentially unmeasured confounders that the COVID-19 pandemic created. One possible confounder is that the reduction in SNF use we observed was a temporal trend related to changing preferences. In addressing this, we reviewed Medicare claims data from the US Department of Health and Human Services in April 2020 and July 2020 compared with the same period in 2019. These data demonstrated only a modest reduction in spending on SNFs in April 2020 that was smaller than the reduction seen in Part A inpatient hospital spending during that same month.²⁷ By July 2020, the spending from Medicare on SNFs exceeded the levels seen in 2019,²⁷ suggesting that the percentage of acute care admissions discharging to SNFs was no lower for Medicare patients in response to COVD-19. We also considered more stringent SNF admission standards as another potential confounder; however, this was not seen at the SNFs in the NUHS geography, where the referral process became less stringent because of COVID-19 waivers for a qualifying stay or skilled need from the Centers for Medicare and Medicaid Services. We were also not able to account for readmissions outside of NUHS, and therefore there may have been differences in the readmission rate that were unmeasured. To address this limitation, we reviewed a data extract from the Illinois Health and Hospital Association and found that the percentage of patients who returned for readmission to a NUHS facility in the year prior to the intervention and during the intervention period were 92.8% and 95.3%, respectively. From this we concluded the unmeasured readmission rate appears to be low, stable, and unlikely to have altered the results of this study. Additionally, when calculating potential COVID-19 cases avoided, the expected number was, by necessity, derived from the observed outcome, given the absence of COVID-19 in the preintervention population. This may have introduced unmeasured confounders, limiting the ability to precisely measure the effect size or draw conclusions on causation. Finally, there may be limitations to the generalizability of these results based on the payor mix of the population at NUHS, which is predominantly insured through Medicare or commercial payors.

We believe this model is replicable and the results generalizable and could serve as both a template for reducing the risks of COVID-19 in SNFs and as part of a larger infection-control strategy to mitigate disease spread in vulnerable populations. It could also be applied as a component of value-improvement programs to foster appropriate use of postacute services after an acute care hospitalization, ensuring safe transitions of care through promotion of high-value care practices.

The authors thank Wei Ning Chi for editorial assistance.

The COVID-19 outbreak in February 2020 at a congregate living facility near Seattle, Washington, signaled the beginning of the pandemic in the United States. In that facility, infected residents had a 54.5% hospitalization rate and 33.7% case-fatality rate.¹ Similar to the experience in Washington, all congregate living facilities have proved particularly vulnerable to the effects of COVID-19,^2-7 with residents at increased risk for disease severity and mortality.^2-7

Due to the COVID-19 emergency, NorthShore University HealthSystem (NUHS), a multihospital, integrated health system in northern Illinois, established a best practice for appropriate use of congregate living facilities after hospitalization. This focused on the safety of discharged patients and mitigation of COVID-19 by putting in place a referral process to a newly established congregate living review committee (CLRC) for review prior to discharge. Although all discharges to congregate living settings are at high risk,² new placements to skilled nursing facilities (SNFs) were the primary focus of the committee and the sole focus of this study. In this study, we sought to determine whether establishment of the CLRC was associated with a reduction in SNF utilization, whether this was safe and efficient, and whether it was associated with a reduction in COVID-19 incidence in the 30 days following discharge.

Setting and Case Review Intervention

We conducted a retrospective cohort study for patients hospitalized within NUHS from March 19, 2019 to July 16, 2020, designed as an interrupted time series. The study was approved by the NUHS Institutional Review Board (EH21-022).

The study exposure was creation of a referral and review process for all patients with expected discharge to a SNF and was implemented as part of usual discharge planning during the COVID-19 pandemic. The key intervention was to establish a multidisciplinary committee, the CLRC, to review all potential discharges to SNFs. The CLRC had dual goals of preventing COVID-19 spread in facilities by limiting placement of new residents and protecting a vulnerable population from a setting that conferred a higher risk of acquiring COVID-19. The CLRC was organized as a multidisciplinary committee with physicians, case managers, social workers, physical therapists, occupational therapists, and the director of NUHS home health agency. Physician members were evenly split as half hospitalists and half ambulatory physicians. The CLRC review was initiated by a patient’s assigned case manager or social worker by consult through a referral in the electronic medical record (EMR). Each case was summarized and then presented to the full CLRC. The CLRC met for 1 hour per day, 6 days per week, to review all planned discharges that met criteria for review. A committee physician chaired each meeting. Three other members were needed for a quorum, with one other member with a title of director or higher. Time required was the 1-hour daily meeting, as well as one full-time position for case review, preparation, and program administration. The case presentation included a clinical summary of the hospitalization as well as COVID-19 status and testing history, previous living situation, level of home support, functional level, psychosocial needs, barrier(s) to discharging home, and long-term residential plans. A structured assessment was then made by each CLRC member in accordance with their professional expertise. Unanimous consensus would be reached before finalizing any recommended adjustments to the discharge, which would be communicated to the inpatient care team via a structured note within the EMR, along with direct communication to the assigned case manager or social worker. When the CLRC suggested adjustments to the discharge, they would work with the assigned case manager or social worker to communicate an appropriate post–acute care plan with the patient or appropriate representative. If there was disagreement or the recommendations could not be followed, the case manager or social worker would place a new referral with additional information for reconsideration. Following a recommendation for SNF, verification would be completed by the CLRC prior to discharge. This process is detailed in Figure 1.

Patient Population

Inclusion criteria for the study were: (1) inpatient hospitalization and (2) eligibility for risk scoring via the organization’s clinical analytics prediction engine (CAPE).⁸ CAPE is a validated predictive model that includes risk of readmission, in-hospital mortality, and out-of-hospital mortality,⁸ with extensive adoption at NUHS. CAPE score eligibility was used as an inclusion criterion so that CAPE could be applied for derivation of a matched control. CAPE eligibility criteria include admission age of at least 18 years and that hospitalization is not psychiatric, rehabilitative, or obstetric. Patients must not be enrolled in hospice and must be discharged alive.

Exclusions were patients who tested positive for SARS-CoV-2 prior to or during index hospitalization. Excluding COVID-19 patients from the analysis eliminated a confounder not present in the preintervention group.

For patients with multiple inpatient admissions, the first admission was the only admission used for analysis. Additionally, if a patient had an admission that occurred in both the preintervention and postintervention periods, they were included only in the postintervention period. This was done to avoid any within-subject correlation and ensure unique patients in each group. Confounding from this approach was mitigated through the process of deriving a matched control.

Outcomes Measurement

The primary outcome of interest was total discharges to SNF across NUHS facilities after hospital admission. Patients were identified as discharging to a SNF if discharge destination codes 03, 64, or 83 appeared on the hospital bill. Additionally, new discharges to SNFs were assessed and identified if documentation indicated that the patient’s living arrangement prior to admission was not a SNF but discharge billing destination codes 03, 64, or 83 appeared on the hospital bill.

Secondary outcomes were measurement of readmissions, days to readmission, and median length of stay (LOS). Readmissions and LOS were balancing measures for the primary outcome, with readmissions measured to evaluate the safety of the CLRC process and LOS measured to evaluate its efficiency. A readmission was any patient who had an unplanned inpatient admission at an NUHS facility within 30 days after an index admission. LOS was measured in days from arrival on a hospital unit to time of discharge.

Additional analysis was done to estimate the effect of the intervention on the incidence of COVID-19 in the 30 days following discharge by comparing the observed to expected incidence of COVID-19 by discharge destination. The expected values were derived by estimating COVID-19 cases that would have been expected to occur with rates of preintervention SNF utilization. This was accomplished by multiplying the observed incidence of COVID-19 in the 30 days following discharge by the number of patients who were discharged to SNFs or home/other in the preintervention period. This expected value was then compared with the observed values to estimate the effect size of the intervention on COVID-19 incidence following discharge. This method of deriving an expected value from the observed incidence was utilized because the preintervention period was before COVID-19 was widespread in the community. It was therefore not possible to directly measure COVID-19 incidence in the preintervention period.

Data Source

Data were retrieved from the NUHS Enterprise Data Warehouse, NUHS’s central data repository, which contains a nightly upload of clinical and financial data from the EMR. Data were collected between March 19, 2019, and July 16, 2020.

The preintervention period was defined as March 19, 2019, to March 18, 2020. Data from that interval were compared with the postintervention period, which was from March 19, 2020, to July 16, 2020. The preintervention period, 1 year immediately prior to the intervention, was chosen to limit any effect of temporal trends while also providing a large sample size. The postintervention period began on the first day NUHS implemented the revised approach to SNF use and ended on the last day before the review process was modified.

Data Analysis

An interrupted time series was used to measure the impact of adoption of the CLRC protocol. A matched control was derived from the preintervention population. To derive this matched control, there was an assessment of covariates in the preintervention and postintervention groups using a standardized mean difference (SMD)⁹ that indicated an imbalance (SMD ≥ 0.1) in some covariates. A propensity score–matching technique¹⁰ was applied to address this imbalance and lack of randomization.

The candidate variables for propensity matching were chosen if they had an association with 30-day readmission. Readmission was chosen to find candidate variables because, of the possible outcomes, this was the only one that was not directly impacted by any CLRC decision. Each covariate was assessed using a logistic regression model while controlling for the postintervention group. If there was an association between a covariate and the outcome, it was chosen for propensity matching. Propensity scores were calculated using a logistic regression model with the treatment (1/0) variable as the dependent variable and the chosen covariates as predictors.

There were no indications of strong multicollinearity. The propensity scores generated were then used to derive a matched control using paired matching. MatchIt package in R (R Foundation for Statistical Computing) was used to create a matched dataset with a logit distance and standard caliper of 0.2 times the standard deviations of the logit of the propensity score. If a match was not found within the caliper, the nearest available match was used.

Regression adjustment¹¹ was then performed using multivariate linear/logistic regression with LOS, readmission rate, days to readmission, total SNF discharges, and new SNF discharges as the outcomes. Treatment (1/0) variable and propensity score were used as the predictors. The adjusted coefficients or odds ratios (ORs) of the intervention variable were thus derived, and their associated P values were used to assess the impact of the intervention on the respective outcomes.

The unmatched preintervention population included 14,468 patients, with 4424 patients in the postintervention population. A matched population was derived and, after matching, the population sizes for pre and post intervention were 4424 each. In the matched population, all measured preintervention characteristics had SMDs and P values that were statistically equivalent. Patient characteristics for the unmatched and matched populations are detailed in Table 1.

During the preintervention period, 1130 (25.5%) patients were discharged to a SNF, with 776 (17.5%) patients being new SNF discharges. In the postintervention period, 568 (12.8%) patients were discharged to a SNF, with 257 (5.8%) patients being new SNF discharges. Total SNF discharges postintervention saw a 49.7% relative reduction (OR, 0.42; 95% CI, 0.38-0.47), while new SNF discharges saw a 66.9% relative reduction (OR, 0.29; 95% CI, 0.25-0.34). These results for both total and new SNF discharges were statistically significant, with P values of <.001, respectively.

Readmissions in the preintervention period were 529 (12.0%) patients, compared with 559 (12.6%) patients in the postintervention period (OR, 1.06; 95% CI, 0.93-1.20; P =.406). An OR was also calculated for readmissions, adjusting for discharge disposition, to account for changes observed in SNF use in the postintervention period. This OR was 1.11 (95% CI, 0.97-1.26; P = .131). Days to readmission in the preintervention and postintervention groups were 11.0 days and 12.0 days, respectively (OR, 0.41; 95% CI, –0.61 to 1.43; P = .429).

LOS was 3.61 days in the preintervention group and 3.64 days in the postintervention group, with an interquartile range (IQR) of 2.14 to 5.69 days in the preintervention group and 2.08 to 5.95 in the postintervention group (OR, 0.09; 95% CI, –0.09 to 0.27; P =.316). These results are summarized in Table 2.

A COVID-19 outbreak in a SNF presents a grave risk to residents and patients discharged to these facilities. It is critical for healthcare systems to do the utmost to protect the health of this vulnerable population and the public in efforts to limit COVID-19 within SNFs.^12-14

In this study, we observed that at NUHS, establishing a multidisciplinary review committee, the CLRC, to assess the appropriateness of discharge to a SNF after hospitalization resulted in a nearly 50% reduction in total SNF discharges and a greater than two-thirds reduction in new SNF discharges, without any increase in LOS or readmissions. Additionally, it was observed that discharging to settings other than a SNF greatly reduced a patient’s risk of being diagnosed with COVID-19 within 30 days, a result that reached statistical significance. Based on the observed 37.2% relative reduction in COVID-19 cases, we estimate that there may have been one COVID-19 infection prevented every 5.6 days from this intervention. Based on published COVID-19 mortality rates for SNF residents,¹ the intervention may have prevented one death every 2.6 weeks. Beyond the risk of COVID-19, other benefits of reducing SNF use are patient and family well-being. Although not measured in this study, others have published about the significant psychological burdens placed on SNF residents, who were at high risk for social isolation, anxiety, and depression during the COVID-19 pandemic^2,15-19 Family members also may have had increased stress, as they were deprived of the opportunity to visit loved ones, advocate for them, and help maintain their identity, humanity, and quality of life.²⁰

Although other hospitals have established a structured approach to reduce COVID-19 in SNFs,²¹ to the best of the authors’ knowledge, the approach described in this article is a unique response to the COVID-19 pandemic. As we have demonstrated, it is highly effective and safe and likely prevented many COVID-19 cases and deaths.

Furthermore, a review committee, such as the one we have described, has value well beyond the COVID-19 pandemic. The health and affordability of care for patients, provider success in value-based care models, and the long-term sustainability of the US healthcare system require close attention to appropriate use of expensive services and to ensuring that their use creates high value. SNF use after a hospitalization is one such service that is frequently targeted and thought to contribute to a substantial portion of wasteful medical spending.^22,23 Additionally, SNFs are known to be high risk for communicable disease outbreaks other than COVID-19,^24,25 as well as a high-risk environment for many other preventable adverse events.^25,26 This review committee ultimately serves to help determine the most appropriate postacute setting for patients being discharged with a determination made through considerations for patient safety, rehabilitation potential, and mental and physical well-being. From a population health perspective, this can lead to better outcomes and lower costs.^22,23 Therefore, although the risks of COVID-19 infection in SNFs are expected to subside, the work of evaluating appropriate use of SNFs after hospitalization at our institution continues. The broader focus now extends beyond postacute level of service toward ensuring a high-value discharge that results in both appropriate resource use and safe patient care transitions.

Limitations of this study include its retrospective nature, results from a single center, and a number of potentially unmeasured confounders that the COVID-19 pandemic created. One possible confounder is that the reduction in SNF use we observed was a temporal trend related to changing preferences. In addressing this, we reviewed Medicare claims data from the US Department of Health and Human Services in April 2020 and July 2020 compared with the same period in 2019. These data demonstrated only a modest reduction in spending on SNFs in April 2020 that was smaller than the reduction seen in Part A inpatient hospital spending during that same month.²⁷ By July 2020, the spending from Medicare on SNFs exceeded the levels seen in 2019,²⁷ suggesting that the percentage of acute care admissions discharging to SNFs was no lower for Medicare patients in response to COVD-19. We also considered more stringent SNF admission standards as another potential confounder; however, this was not seen at the SNFs in the NUHS geography, where the referral process became less stringent because of COVID-19 waivers for a qualifying stay or skilled need from the Centers for Medicare and Medicaid Services. We were also not able to account for readmissions outside of NUHS, and therefore there may have been differences in the readmission rate that were unmeasured. To address this limitation, we reviewed a data extract from the Illinois Health and Hospital Association and found that the percentage of patients who returned for readmission to a NUHS facility in the year prior to the intervention and during the intervention period were 92.8% and 95.3%, respectively. From this we concluded the unmeasured readmission rate appears to be low, stable, and unlikely to have altered the results of this study. Additionally, when calculating potential COVID-19 cases avoided, the expected number was, by necessity, derived from the observed outcome, given the absence of COVID-19 in the preintervention population. This may have introduced unmeasured confounders, limiting the ability to precisely measure the effect size or draw conclusions on causation. Finally, there may be limitations to the generalizability of these results based on the payor mix of the population at NUHS, which is predominantly insured through Medicare or commercial payors.

We believe this model is replicable and the results generalizable and could serve as both a template for reducing the risks of COVID-19 in SNFs and as part of a larger infection-control strategy to mitigate disease spread in vulnerable populations. It could also be applied as a component of value-improvement programs to foster appropriate use of postacute services after an acute care hospitalization, ensuring safe transitions of care through promotion of high-value care practices.

The authors thank Wei Ning Chi for editorial assistance.

The COVID-19 outbreak in February 2020 at a congregate living facility near Seattle, Washington, signaled the beginning of the pandemic in the United States. In that facility, infected residents had a 54.5% hospitalization rate and 33.7% case-fatality rate.¹ Similar to the experience in Washington, all congregate living facilities have proved particularly vulnerable to the effects of COVID-19,^2-7 with residents at increased risk for disease severity and mortality.^2-7

Due to the COVID-19 emergency, NorthShore University HealthSystem (NUHS), a multihospital, integrated health system in northern Illinois, established a best practice for appropriate use of congregate living facilities after hospitalization. This focused on the safety of discharged patients and mitigation of COVID-19 by putting in place a referral process to a newly established congregate living review committee (CLRC) for review prior to discharge. Although all discharges to congregate living settings are at high risk,² new placements to skilled nursing facilities (SNFs) were the primary focus of the committee and the sole focus of this study. In this study, we sought to determine whether establishment of the CLRC was associated with a reduction in SNF utilization, whether this was safe and efficient, and whether it was associated with a reduction in COVID-19 incidence in the 30 days following discharge.

Setting and Case Review Intervention

We conducted a retrospective cohort study for patients hospitalized within NUHS from March 19, 2019 to July 16, 2020, designed as an interrupted time series. The study was approved by the NUHS Institutional Review Board (EH21-022).

The study exposure was creation of a referral and review process for all patients with expected discharge to a SNF and was implemented as part of usual discharge planning during the COVID-19 pandemic. The key intervention was to establish a multidisciplinary committee, the CLRC, to review all potential discharges to SNFs. The CLRC had dual goals of preventing COVID-19 spread in facilities by limiting placement of new residents and protecting a vulnerable population from a setting that conferred a higher risk of acquiring COVID-19. The CLRC was organized as a multidisciplinary committee with physicians, case managers, social workers, physical therapists, occupational therapists, and the director of NUHS home health agency. Physician members were evenly split as half hospitalists and half ambulatory physicians. The CLRC review was initiated by a patient’s assigned case manager or social worker by consult through a referral in the electronic medical record (EMR). Each case was summarized and then presented to the full CLRC. The CLRC met for 1 hour per day, 6 days per week, to review all planned discharges that met criteria for review. A committee physician chaired each meeting. Three other members were needed for a quorum, with one other member with a title of director or higher. Time required was the 1-hour daily meeting, as well as one full-time position for case review, preparation, and program administration. The case presentation included a clinical summary of the hospitalization as well as COVID-19 status and testing history, previous living situation, level of home support, functional level, psychosocial needs, barrier(s) to discharging home, and long-term residential plans. A structured assessment was then made by each CLRC member in accordance with their professional expertise. Unanimous consensus would be reached before finalizing any recommended adjustments to the discharge, which would be communicated to the inpatient care team via a structured note within the EMR, along with direct communication to the assigned case manager or social worker. When the CLRC suggested adjustments to the discharge, they would work with the assigned case manager or social worker to communicate an appropriate post–acute care plan with the patient or appropriate representative. If there was disagreement or the recommendations could not be followed, the case manager or social worker would place a new referral with additional information for reconsideration. Following a recommendation for SNF, verification would be completed by the CLRC prior to discharge. This process is detailed in Figure 1.

Patient Population

Inclusion criteria for the study were: (1) inpatient hospitalization and (2) eligibility for risk scoring via the organization’s clinical analytics prediction engine (CAPE).⁸ CAPE is a validated predictive model that includes risk of readmission, in-hospital mortality, and out-of-hospital mortality,⁸ with extensive adoption at NUHS. CAPE score eligibility was used as an inclusion criterion so that CAPE could be applied for derivation of a matched control. CAPE eligibility criteria include admission age of at least 18 years and that hospitalization is not psychiatric, rehabilitative, or obstetric. Patients must not be enrolled in hospice and must be discharged alive.

Exclusions were patients who tested positive for SARS-CoV-2 prior to or during index hospitalization. Excluding COVID-19 patients from the analysis eliminated a confounder not present in the preintervention group.

For patients with multiple inpatient admissions, the first admission was the only admission used for analysis. Additionally, if a patient had an admission that occurred in both the preintervention and postintervention periods, they were included only in the postintervention period. This was done to avoid any within-subject correlation and ensure unique patients in each group. Confounding from this approach was mitigated through the process of deriving a matched control.

Outcomes Measurement

The primary outcome of interest was total discharges to SNF across NUHS facilities after hospital admission. Patients were identified as discharging to a SNF if discharge destination codes 03, 64, or 83 appeared on the hospital bill. Additionally, new discharges to SNFs were assessed and identified if documentation indicated that the patient’s living arrangement prior to admission was not a SNF but discharge billing destination codes 03, 64, or 83 appeared on the hospital bill.

Secondary outcomes were measurement of readmissions, days to readmission, and median length of stay (LOS). Readmissions and LOS were balancing measures for the primary outcome, with readmissions measured to evaluate the safety of the CLRC process and LOS measured to evaluate its efficiency. A readmission was any patient who had an unplanned inpatient admission at an NUHS facility within 30 days after an index admission. LOS was measured in days from arrival on a hospital unit to time of discharge.

Additional analysis was done to estimate the effect of the intervention on the incidence of COVID-19 in the 30 days following discharge by comparing the observed to expected incidence of COVID-19 by discharge destination. The expected values were derived by estimating COVID-19 cases that would have been expected to occur with rates of preintervention SNF utilization. This was accomplished by multiplying the observed incidence of COVID-19 in the 30 days following discharge by the number of patients who were discharged to SNFs or home/other in the preintervention period. This expected value was then compared with the observed values to estimate the effect size of the intervention on COVID-19 incidence following discharge. This method of deriving an expected value from the observed incidence was utilized because the preintervention period was before COVID-19 was widespread in the community. It was therefore not possible to directly measure COVID-19 incidence in the preintervention period.

Data Source

Data were retrieved from the NUHS Enterprise Data Warehouse, NUHS’s central data repository, which contains a nightly upload of clinical and financial data from the EMR. Data were collected between March 19, 2019, and July 16, 2020.

The preintervention period was defined as March 19, 2019, to March 18, 2020. Data from that interval were compared with the postintervention period, which was from March 19, 2020, to July 16, 2020. The preintervention period, 1 year immediately prior to the intervention, was chosen to limit any effect of temporal trends while also providing a large sample size. The postintervention period began on the first day NUHS implemented the revised approach to SNF use and ended on the last day before the review process was modified.

Data Analysis

An interrupted time series was used to measure the impact of adoption of the CLRC protocol. A matched control was derived from the preintervention population. To derive this matched control, there was an assessment of covariates in the preintervention and postintervention groups using a standardized mean difference (SMD)⁹ that indicated an imbalance (SMD ≥ 0.1) in some covariates. A propensity score–matching technique¹⁰ was applied to address this imbalance and lack of randomization.

The candidate variables for propensity matching were chosen if they had an association with 30-day readmission. Readmission was chosen to find candidate variables because, of the possible outcomes, this was the only one that was not directly impacted by any CLRC decision. Each covariate was assessed using a logistic regression model while controlling for the postintervention group. If there was an association between a covariate and the outcome, it was chosen for propensity matching. Propensity scores were calculated using a logistic regression model with the treatment (1/0) variable as the dependent variable and the chosen covariates as predictors.

There were no indications of strong multicollinearity. The propensity scores generated were then used to derive a matched control using paired matching. MatchIt package in R (R Foundation for Statistical Computing) was used to create a matched dataset with a logit distance and standard caliper of 0.2 times the standard deviations of the logit of the propensity score. If a match was not found within the caliper, the nearest available match was used.

Regression adjustment¹¹ was then performed using multivariate linear/logistic regression with LOS, readmission rate, days to readmission, total SNF discharges, and new SNF discharges as the outcomes. Treatment (1/0) variable and propensity score were used as the predictors. The adjusted coefficients or odds ratios (ORs) of the intervention variable were thus derived, and their associated P values were used to assess the impact of the intervention on the respective outcomes.

The unmatched preintervention population included 14,468 patients, with 4424 patients in the postintervention population. A matched population was derived and, after matching, the population sizes for pre and post intervention were 4424 each. In the matched population, all measured preintervention characteristics had SMDs and P values that were statistically equivalent. Patient characteristics for the unmatched and matched populations are detailed in Table 1.

During the preintervention period, 1130 (25.5%) patients were discharged to a SNF, with 776 (17.5%) patients being new SNF discharges. In the postintervention period, 568 (12.8%) patients were discharged to a SNF, with 257 (5.8%) patients being new SNF discharges. Total SNF discharges postintervention saw a 49.7% relative reduction (OR, 0.42; 95% CI, 0.38-0.47), while new SNF discharges saw a 66.9% relative reduction (OR, 0.29; 95% CI, 0.25-0.34). These results for both total and new SNF discharges were statistically significant, with P values of <.001, respectively.

Readmissions in the preintervention period were 529 (12.0%) patients, compared with 559 (12.6%) patients in the postintervention period (OR, 1.06; 95% CI, 0.93-1.20; P =.406). An OR was also calculated for readmissions, adjusting for discharge disposition, to account for changes observed in SNF use in the postintervention period. This OR was 1.11 (95% CI, 0.97-1.26; P = .131). Days to readmission in the preintervention and postintervention groups were 11.0 days and 12.0 days, respectively (OR, 0.41; 95% CI, –0.61 to 1.43; P = .429).

LOS was 3.61 days in the preintervention group and 3.64 days in the postintervention group, with an interquartile range (IQR) of 2.14 to 5.69 days in the preintervention group and 2.08 to 5.95 in the postintervention group (OR, 0.09; 95% CI, –0.09 to 0.27; P =.316). These results are summarized in Table 2.

A COVID-19 outbreak in a SNF presents a grave risk to residents and patients discharged to these facilities. It is critical for healthcare systems to do the utmost to protect the health of this vulnerable population and the public in efforts to limit COVID-19 within SNFs.^12-14

In this study, we observed that at NUHS, establishing a multidisciplinary review committee, the CLRC, to assess the appropriateness of discharge to a SNF after hospitalization resulted in a nearly 50% reduction in total SNF discharges and a greater than two-thirds reduction in new SNF discharges, without any increase in LOS or readmissions. Additionally, it was observed that discharging to settings other than a SNF greatly reduced a patient’s risk of being diagnosed with COVID-19 within 30 days, a result that reached statistical significance. Based on the observed 37.2% relative reduction in COVID-19 cases, we estimate that there may have been one COVID-19 infection prevented every 5.6 days from this intervention. Based on published COVID-19 mortality rates for SNF residents,¹ the intervention may have prevented one death every 2.6 weeks. Beyond the risk of COVID-19, other benefits of reducing SNF use are patient and family well-being. Although not measured in this study, others have published about the significant psychological burdens placed on SNF residents, who were at high risk for social isolation, anxiety, and depression during the COVID-19 pandemic^2,15-19 Family members also may have had increased stress, as they were deprived of the opportunity to visit loved ones, advocate for them, and help maintain their identity, humanity, and quality of life.²⁰

Although other hospitals have established a structured approach to reduce COVID-19 in SNFs,²¹ to the best of the authors’ knowledge, the approach described in this article is a unique response to the COVID-19 pandemic. As we have demonstrated, it is highly effective and safe and likely prevented many COVID-19 cases and deaths.

Furthermore, a review committee, such as the one we have described, has value well beyond the COVID-19 pandemic. The health and affordability of care for patients, provider success in value-based care models, and the long-term sustainability of the US healthcare system require close attention to appropriate use of expensive services and to ensuring that their use creates high value. SNF use after a hospitalization is one such service that is frequently targeted and thought to contribute to a substantial portion of wasteful medical spending.^22,23 Additionally, SNFs are known to be high risk for communicable disease outbreaks other than COVID-19,^24,25 as well as a high-risk environment for many other preventable adverse events.^25,26 This review committee ultimately serves to help determine the most appropriate postacute setting for patients being discharged with a determination made through considerations for patient safety, rehabilitation potential, and mental and physical well-being. From a population health perspective, this can lead to better outcomes and lower costs.^22,23 Therefore, although the risks of COVID-19 infection in SNFs are expected to subside, the work of evaluating appropriate use of SNFs after hospitalization at our institution continues. The broader focus now extends beyond postacute level of service toward ensuring a high-value discharge that results in both appropriate resource use and safe patient care transitions.

Limitations of this study include its retrospective nature, results from a single center, and a number of potentially unmeasured confounders that the COVID-19 pandemic created. One possible confounder is that the reduction in SNF use we observed was a temporal trend related to changing preferences. In addressing this, we reviewed Medicare claims data from the US Department of Health and Human Services in April 2020 and July 2020 compared with the same period in 2019. These data demonstrated only a modest reduction in spending on SNFs in April 2020 that was smaller than the reduction seen in Part A inpatient hospital spending during that same month.²⁷ By July 2020, the spending from Medicare on SNFs exceeded the levels seen in 2019,²⁷ suggesting that the percentage of acute care admissions discharging to SNFs was no lower for Medicare patients in response to COVD-19. We also considered more stringent SNF admission standards as another potential confounder; however, this was not seen at the SNFs in the NUHS geography, where the referral process became less stringent because of COVID-19 waivers for a qualifying stay or skilled need from the Centers for Medicare and Medicaid Services. We were also not able to account for readmissions outside of NUHS, and therefore there may have been differences in the readmission rate that were unmeasured. To address this limitation, we reviewed a data extract from the Illinois Health and Hospital Association and found that the percentage of patients who returned for readmission to a NUHS facility in the year prior to the intervention and during the intervention period were 92.8% and 95.3%, respectively. From this we concluded the unmeasured readmission rate appears to be low, stable, and unlikely to have altered the results of this study. Additionally, when calculating potential COVID-19 cases avoided, the expected number was, by necessity, derived from the observed outcome, given the absence of COVID-19 in the preintervention population. This may have introduced unmeasured confounders, limiting the ability to precisely measure the effect size or draw conclusions on causation. Finally, there may be limitations to the generalizability of these results based on the payor mix of the population at NUHS, which is predominantly insured through Medicare or commercial payors.

We believe this model is replicable and the results generalizable and could serve as both a template for reducing the risks of COVID-19 in SNFs and as part of a larger infection-control strategy to mitigate disease spread in vulnerable populations. It could also be applied as a component of value-improvement programs to foster appropriate use of postacute services after an acute care hospitalization, ensuring safe transitions of care through promotion of high-value care practices.

The authors thank Wei Ning Chi for editorial assistance.