Hospital Medicine

As the population of the United States continues to age, hospitals are seeing an increasing number of older patients with significant medical and social complexity. Medicare data have shown that an increasing number require post–acute care after a hospitalization.¹ Discharges to post–acute care settings are often longer and more costly compared with discharges to other settings, which suggests that targeting quality improvement efforts at this transition period may improve the value of care.²

The transition from the hospital setting to a post–acute care facility can be dangerous and complicated due to lapses in communication, medication errors, and the complexity of medical treatment plans. Suboptimal transitions in care can result in adverse events for the patient, as well as confusion in medication regimens or incomplete plans for follow-up care.³

The Project ECHO (Extension for Community Healthcare Outcomes) model was first developed and launched by Sanjeev Arora, MD, in New Mexico in 2003 to expand access to subspecialist care using videoconferencing.⁴ We first applied this model in 2013 to evaluate the impact of this interdisciplinary videoconferencing tool on the care of patients discharged to post–acute settings.⁵ We found that patients participating in the Extension for Community Healthcare Outcomes–Care Transitions (ECHO-CT) model experienced decreased risk of rehospitalization, decreased skilled nursing facility (SNF) length of stay, and reduced 30-day healthcare costs, compared with those patients not enrolled in this program; these outcomes were likely due to identification and correction of medication-related errors, improved care coordination, improved disease management, and clarification of goals of care.⁶ Though these investigations did identify some issues arising during the care transition process, they did not fully describe the types of problems uncovered. We sought to better characterize the clinical and operational issues identified through the ECHO-CT conference, hereafter known as transition-of-care events (TCEs). These issues may include new or evolving medical concerns, an adverse event, or a “near miss.” Identification and classification of TCEs that may contribute to unsafe or fractured care transitions are critical in developing systematic solutions to improve transitions of care, which can ultimately improve patient safety and potentially avoid preventable errors.

ECHO-CT Multidisciplinary Video Conference

We conducted ECHO-CT at a large, tertiary care academic medical center. The project design for the ECHO-CT program has been previously described.⁵ In brief, the program is a weekly, multidisciplinary videoconference between a hospital-based team and post–acute care providers to discuss patients discharged from inpatient services to post–acute care sites, including SNFs and long-term acute care hospitals (LTACHs), during the preceding week. All patients discharged from the tertiary care inpatient site to one of the eight participating SNFs or LTACHs, from either a medical or surgical service, are eligible to be discussed at this weekly interdisciplinary conference. Long-term care facilities were not included in this study. The ECHO-CT program used HIPAA (Health Insurance Portability and Accountability Act)-compliant videoconferencing technology to connect hospital and post–acute care providers.

During the videoconferences, each patient’s hospital course and discharge documentation are reviewed by a hospitalist, and a pharmacist performs a medication reconciliation of each patient’s admission, discharge, and post–acute care medication list. The discharging attending, primary care providers, residents, other trainees, and subspecialist providers are invited to attend. Typically, the interdisciplinary team at the post–acute care sites includes physicians, nurse practitioners, physical therapists, social workers, and case managers. Between 10 and 20 patients are discussed in a case-based format, which includes a summary of the patient’s hospital course, an update from the post–acute care team on the patient’s care, and an opportunity for a discussion regarding any concerns or questions raised by the post–acute care or inpatient care teams. The content and duration of discussion typically lasts approximately 3 to 10 minutes, depending on the needs of the patient and the care team. Each of the eight post–acute care sites participating in the project are assigned a 10- to 15-minute block. A copy of the ECHO-CT session process document is included in the Appendix.

Data Collection

At each interdisciplinary patient review, TCEs were identified and recorded. These events were categorized in real time by the ECHO-CT data collection team into the following categories: medication related, medical, discharge communication/coordination, or other, and recorded in a secured, deidentified database. For individuals whose TCEs could represent more than one category, authors reviewed the available information about the TCEs and determined the most appropriate category; if more than one category was felt to be applicable to a patient’s situation, the events were reclassified into all applicable categories. Data about individual patients, including gender, age at the time of discharge, and other demographic information, were obtained from hospital databases. Number of diagnoses included any diagnosis billed during the patient’s hospital stay, and these data were obtained from a hospital billing database. Average number of medications at discharge was obtained from a hospital pharmacy database.

A total of 675 patients (experiencing 743 hospitalizations) were discharged from a medical or surgical service to one of the participating post–acute care sites from January 2016 to October 2018, and were discussed at the interdisciplinary conference. During that time, 139 TCEs were recorded for review, involving 132 patients (Table 1). Patients who experienced TCEs were noted to have a slightly higher average number of diagnoses than did those in the non-TCE group (21 vs 18, respectively) and number of medications (18 vs 15).

Representative examples of TCEs are provided in Table 2. Fifty-eight issues were identified as discharge communication or coordination issues (eg, discharge summary was late or missing at time of discharge to facility, transitional issues were unclear, follow-up appointments were not appropriately scheduled or documented). An additional 52 TCEs were identified as pharmacy or medication issues (eg, medications were inadvertently omitted from discharge medication list, prehospital medication list was incorrect). Medical issues accounted for an additional 27 concerns (eg, patient was hypoglycemic on arrival, inadequate pain control, discovery of new acute medical issues or medical diagnoses that were not clearly documented or communicated by the inpatient team). “Other” issues (two) included unaddressed social concerns, such as insurance issues.

The ECHO-CT model unites hospital and post–acute care providers to improve transitions of care and is unique in its focus on the transition from hospital to post–acute care rather than to home care. In 2 years of data collection, we identified several TCEs encompassing a range of concerns. Of the 675 patients discussed, 132 (20%) were noted to have a TCE. When these percentages are applied to the 140 million Medicare hospital discharges that took place during 2000 to 2015, we would estimate nearly 5.5 million TCEs, or 375,000 TCEs per year, that may have affected this population.

The majority of TCEs were communication and coordination errors. Missing or incomplete discharge paperwork, inadequate documentation of inpatient care, and confusion about medical devices or postoperative needs (eg, slings, braces, wound care, drains) were commonly reported. Follow-up appointments with specialists were often not appropriately scheduled or communicated. This may have resulted from unstandardized discharge documentation and a lower priority given to documentation in the setting of multiple clinical demands (eg, direct patient care, complex care coordination, and clinical paperwork and charting). Studies have demonstrated that fewer than one-third of discharge summaries are received by outpatient providers before postdischarge follow-up, and additionally that nearly 40% of patients did not undergo recommended workups for medical issues identified during their hospital stay.^7,8 All of this is problematic because appropriate documentation in discharge summaries is associated with a decreased risk of hospital readmission.⁹

Pharmacy issues were the second most common TCE identified. One member of the post–acute care team noted that “omissions, additions, and replacements” relating to medications were common occurrences. Additionally, it was noted that medications were inadvertently continued for longer than planned or not adjusted appropriately with changing clinical parameters, such as renal function. The results of our analysis are consistent with current literature, which suggests that up to 60% of all medication errors occur during the period surrounding transitions of care.¹⁰

There were several limitations to this investigation. Though recording of identified TCEs occurred in real time, analysis of these identified events occurred retrospectively; therefore, investigators had limited ability to retroactively review or recategorize recorded issues, which potentially could have resulted in misclassification or misinterpretation. Additionally, the data were intended to be descriptive; therefore, outcomes such as hospital readmission and patient harm could not be linked to specific TCEs. Furthermore, it is possible that events were not detected by either the postdischarge team or the hospital-based team and, therefore, not captured in this analysis. Further work would be helpful to determine the root causes underlying the identified issues in care transitions, with the goal of improving patient safety and avoiding preventable errors during transitions of care. Although there is comprehensive literature related to errors and medication-related adverse events,¹¹ there is not a consensus of how to classify and report, in a standardized fashion, events arising during the transition period. A validated structure for systematically identifying, monitoring, recording, and reporting issues arising during care transitions will be critical in preventing errors and ensuring patient safety during this high-risk period.

Our model is a unique intervention that uses the expertise and engagement of an interdisciplinary team and seeks to identify and remedy issues arising during transitions of care—in real time—to prevent direct harm to vulnerable patients. We have already implemented interventions to improve care based on our experiences with this videoconference-based program. For example, direct feedback was given to discharging teams to improve the discharge summary and associated documentation, and changes to the medication-ordering system were implemented to address specific medication errors discovered. The TCEs identified in this investigation highlight specific areas for improvement with the goal of providing high-quality care for patients and seamless transitions to post–acute care. As health systems and hospitals face new challenges in communication and care coordination, especially due to the recent COVID-19 pandemic, the technology and communication methods used in the ECHO-CT model may become even more relevant for promoting clear communication and patient safety during transitions of care.

The ECHO CT team thanks Sabrina Carretie for her contributions in data collection and analysis.

As the population of the United States continues to age, hospitals are seeing an increasing number of older patients with significant medical and social complexity. Medicare data have shown that an increasing number require post–acute care after a hospitalization.¹ Discharges to post–acute care settings are often longer and more costly compared with discharges to other settings, which suggests that targeting quality improvement efforts at this transition period may improve the value of care.²

The transition from the hospital setting to a post–acute care facility can be dangerous and complicated due to lapses in communication, medication errors, and the complexity of medical treatment plans. Suboptimal transitions in care can result in adverse events for the patient, as well as confusion in medication regimens or incomplete plans for follow-up care.³

The Project ECHO (Extension for Community Healthcare Outcomes) model was first developed and launched by Sanjeev Arora, MD, in New Mexico in 2003 to expand access to subspecialist care using videoconferencing.⁴ We first applied this model in 2013 to evaluate the impact of this interdisciplinary videoconferencing tool on the care of patients discharged to post–acute settings.⁵ We found that patients participating in the Extension for Community Healthcare Outcomes–Care Transitions (ECHO-CT) model experienced decreased risk of rehospitalization, decreased skilled nursing facility (SNF) length of stay, and reduced 30-day healthcare costs, compared with those patients not enrolled in this program; these outcomes were likely due to identification and correction of medication-related errors, improved care coordination, improved disease management, and clarification of goals of care.⁶ Though these investigations did identify some issues arising during the care transition process, they did not fully describe the types of problems uncovered. We sought to better characterize the clinical and operational issues identified through the ECHO-CT conference, hereafter known as transition-of-care events (TCEs). These issues may include new or evolving medical concerns, an adverse event, or a “near miss.” Identification and classification of TCEs that may contribute to unsafe or fractured care transitions are critical in developing systematic solutions to improve transitions of care, which can ultimately improve patient safety and potentially avoid preventable errors.

ECHO-CT Multidisciplinary Video Conference

We conducted ECHO-CT at a large, tertiary care academic medical center. The project design for the ECHO-CT program has been previously described.⁵ In brief, the program is a weekly, multidisciplinary videoconference between a hospital-based team and post–acute care providers to discuss patients discharged from inpatient services to post–acute care sites, including SNFs and long-term acute care hospitals (LTACHs), during the preceding week. All patients discharged from the tertiary care inpatient site to one of the eight participating SNFs or LTACHs, from either a medical or surgical service, are eligible to be discussed at this weekly interdisciplinary conference. Long-term care facilities were not included in this study. The ECHO-CT program used HIPAA (Health Insurance Portability and Accountability Act)-compliant videoconferencing technology to connect hospital and post–acute care providers.

During the videoconferences, each patient’s hospital course and discharge documentation are reviewed by a hospitalist, and a pharmacist performs a medication reconciliation of each patient’s admission, discharge, and post–acute care medication list. The discharging attending, primary care providers, residents, other trainees, and subspecialist providers are invited to attend. Typically, the interdisciplinary team at the post–acute care sites includes physicians, nurse practitioners, physical therapists, social workers, and case managers. Between 10 and 20 patients are discussed in a case-based format, which includes a summary of the patient’s hospital course, an update from the post–acute care team on the patient’s care, and an opportunity for a discussion regarding any concerns or questions raised by the post–acute care or inpatient care teams. The content and duration of discussion typically lasts approximately 3 to 10 minutes, depending on the needs of the patient and the care team. Each of the eight post–acute care sites participating in the project are assigned a 10- to 15-minute block. A copy of the ECHO-CT session process document is included in the Appendix.

Data Collection

At each interdisciplinary patient review, TCEs were identified and recorded. These events were categorized in real time by the ECHO-CT data collection team into the following categories: medication related, medical, discharge communication/coordination, or other, and recorded in a secured, deidentified database. For individuals whose TCEs could represent more than one category, authors reviewed the available information about the TCEs and determined the most appropriate category; if more than one category was felt to be applicable to a patient’s situation, the events were reclassified into all applicable categories. Data about individual patients, including gender, age at the time of discharge, and other demographic information, were obtained from hospital databases. Number of diagnoses included any diagnosis billed during the patient’s hospital stay, and these data were obtained from a hospital billing database. Average number of medications at discharge was obtained from a hospital pharmacy database.

A total of 675 patients (experiencing 743 hospitalizations) were discharged from a medical or surgical service to one of the participating post–acute care sites from January 2016 to October 2018, and were discussed at the interdisciplinary conference. During that time, 139 TCEs were recorded for review, involving 132 patients (Table 1). Patients who experienced TCEs were noted to have a slightly higher average number of diagnoses than did those in the non-TCE group (21 vs 18, respectively) and number of medications (18 vs 15).

Representative examples of TCEs are provided in Table 2. Fifty-eight issues were identified as discharge communication or coordination issues (eg, discharge summary was late or missing at time of discharge to facility, transitional issues were unclear, follow-up appointments were not appropriately scheduled or documented). An additional 52 TCEs were identified as pharmacy or medication issues (eg, medications were inadvertently omitted from discharge medication list, prehospital medication list was incorrect). Medical issues accounted for an additional 27 concerns (eg, patient was hypoglycemic on arrival, inadequate pain control, discovery of new acute medical issues or medical diagnoses that were not clearly documented or communicated by the inpatient team). “Other” issues (two) included unaddressed social concerns, such as insurance issues.

The ECHO-CT model unites hospital and post–acute care providers to improve transitions of care and is unique in its focus on the transition from hospital to post–acute care rather than to home care. In 2 years of data collection, we identified several TCEs encompassing a range of concerns. Of the 675 patients discussed, 132 (20%) were noted to have a TCE. When these percentages are applied to the 140 million Medicare hospital discharges that took place during 2000 to 2015, we would estimate nearly 5.5 million TCEs, or 375,000 TCEs per year, that may have affected this population.

The majority of TCEs were communication and coordination errors. Missing or incomplete discharge paperwork, inadequate documentation of inpatient care, and confusion about medical devices or postoperative needs (eg, slings, braces, wound care, drains) were commonly reported. Follow-up appointments with specialists were often not appropriately scheduled or communicated. This may have resulted from unstandardized discharge documentation and a lower priority given to documentation in the setting of multiple clinical demands (eg, direct patient care, complex care coordination, and clinical paperwork and charting). Studies have demonstrated that fewer than one-third of discharge summaries are received by outpatient providers before postdischarge follow-up, and additionally that nearly 40% of patients did not undergo recommended workups for medical issues identified during their hospital stay.^7,8 All of this is problematic because appropriate documentation in discharge summaries is associated with a decreased risk of hospital readmission.⁹

Pharmacy issues were the second most common TCE identified. One member of the post–acute care team noted that “omissions, additions, and replacements” relating to medications were common occurrences. Additionally, it was noted that medications were inadvertently continued for longer than planned or not adjusted appropriately with changing clinical parameters, such as renal function. The results of our analysis are consistent with current literature, which suggests that up to 60% of all medication errors occur during the period surrounding transitions of care.¹⁰

There were several limitations to this investigation. Though recording of identified TCEs occurred in real time, analysis of these identified events occurred retrospectively; therefore, investigators had limited ability to retroactively review or recategorize recorded issues, which potentially could have resulted in misclassification or misinterpretation. Additionally, the data were intended to be descriptive; therefore, outcomes such as hospital readmission and patient harm could not be linked to specific TCEs. Furthermore, it is possible that events were not detected by either the postdischarge team or the hospital-based team and, therefore, not captured in this analysis. Further work would be helpful to determine the root causes underlying the identified issues in care transitions, with the goal of improving patient safety and avoiding preventable errors during transitions of care. Although there is comprehensive literature related to errors and medication-related adverse events,¹¹ there is not a consensus of how to classify and report, in a standardized fashion, events arising during the transition period. A validated structure for systematically identifying, monitoring, recording, and reporting issues arising during care transitions will be critical in preventing errors and ensuring patient safety during this high-risk period.

Our model is a unique intervention that uses the expertise and engagement of an interdisciplinary team and seeks to identify and remedy issues arising during transitions of care—in real time—to prevent direct harm to vulnerable patients. We have already implemented interventions to improve care based on our experiences with this videoconference-based program. For example, direct feedback was given to discharging teams to improve the discharge summary and associated documentation, and changes to the medication-ordering system were implemented to address specific medication errors discovered. The TCEs identified in this investigation highlight specific areas for improvement with the goal of providing high-quality care for patients and seamless transitions to post–acute care. As health systems and hospitals face new challenges in communication and care coordination, especially due to the recent COVID-19 pandemic, the technology and communication methods used in the ECHO-CT model may become even more relevant for promoting clear communication and patient safety during transitions of care.

The ECHO CT team thanks Sabrina Carretie for her contributions in data collection and analysis.

As the population of the United States continues to age, hospitals are seeing an increasing number of older patients with significant medical and social complexity. Medicare data have shown that an increasing number require post–acute care after a hospitalization.¹ Discharges to post–acute care settings are often longer and more costly compared with discharges to other settings, which suggests that targeting quality improvement efforts at this transition period may improve the value of care.²

The transition from the hospital setting to a post–acute care facility can be dangerous and complicated due to lapses in communication, medication errors, and the complexity of medical treatment plans. Suboptimal transitions in care can result in adverse events for the patient, as well as confusion in medication regimens or incomplete plans for follow-up care.³

The Project ECHO (Extension for Community Healthcare Outcomes) model was first developed and launched by Sanjeev Arora, MD, in New Mexico in 2003 to expand access to subspecialist care using videoconferencing.⁴ We first applied this model in 2013 to evaluate the impact of this interdisciplinary videoconferencing tool on the care of patients discharged to post–acute settings.⁵ We found that patients participating in the Extension for Community Healthcare Outcomes–Care Transitions (ECHO-CT) model experienced decreased risk of rehospitalization, decreased skilled nursing facility (SNF) length of stay, and reduced 30-day healthcare costs, compared with those patients not enrolled in this program; these outcomes were likely due to identification and correction of medication-related errors, improved care coordination, improved disease management, and clarification of goals of care.⁶ Though these investigations did identify some issues arising during the care transition process, they did not fully describe the types of problems uncovered. We sought to better characterize the clinical and operational issues identified through the ECHO-CT conference, hereafter known as transition-of-care events (TCEs). These issues may include new or evolving medical concerns, an adverse event, or a “near miss.” Identification and classification of TCEs that may contribute to unsafe or fractured care transitions are critical in developing systematic solutions to improve transitions of care, which can ultimately improve patient safety and potentially avoid preventable errors.

ECHO-CT Multidisciplinary Video Conference

We conducted ECHO-CT at a large, tertiary care academic medical center. The project design for the ECHO-CT program has been previously described.⁵ In brief, the program is a weekly, multidisciplinary videoconference between a hospital-based team and post–acute care providers to discuss patients discharged from inpatient services to post–acute care sites, including SNFs and long-term acute care hospitals (LTACHs), during the preceding week. All patients discharged from the tertiary care inpatient site to one of the eight participating SNFs or LTACHs, from either a medical or surgical service, are eligible to be discussed at this weekly interdisciplinary conference. Long-term care facilities were not included in this study. The ECHO-CT program used HIPAA (Health Insurance Portability and Accountability Act)-compliant videoconferencing technology to connect hospital and post–acute care providers.

During the videoconferences, each patient’s hospital course and discharge documentation are reviewed by a hospitalist, and a pharmacist performs a medication reconciliation of each patient’s admission, discharge, and post–acute care medication list. The discharging attending, primary care providers, residents, other trainees, and subspecialist providers are invited to attend. Typically, the interdisciplinary team at the post–acute care sites includes physicians, nurse practitioners, physical therapists, social workers, and case managers. Between 10 and 20 patients are discussed in a case-based format, which includes a summary of the patient’s hospital course, an update from the post–acute care team on the patient’s care, and an opportunity for a discussion regarding any concerns or questions raised by the post–acute care or inpatient care teams. The content and duration of discussion typically lasts approximately 3 to 10 minutes, depending on the needs of the patient and the care team. Each of the eight post–acute care sites participating in the project are assigned a 10- to 15-minute block. A copy of the ECHO-CT session process document is included in the Appendix.

Data Collection

At each interdisciplinary patient review, TCEs were identified and recorded. These events were categorized in real time by the ECHO-CT data collection team into the following categories: medication related, medical, discharge communication/coordination, or other, and recorded in a secured, deidentified database. For individuals whose TCEs could represent more than one category, authors reviewed the available information about the TCEs and determined the most appropriate category; if more than one category was felt to be applicable to a patient’s situation, the events were reclassified into all applicable categories. Data about individual patients, including gender, age at the time of discharge, and other demographic information, were obtained from hospital databases. Number of diagnoses included any diagnosis billed during the patient’s hospital stay, and these data were obtained from a hospital billing database. Average number of medications at discharge was obtained from a hospital pharmacy database.

A total of 675 patients (experiencing 743 hospitalizations) were discharged from a medical or surgical service to one of the participating post–acute care sites from January 2016 to October 2018, and were discussed at the interdisciplinary conference. During that time, 139 TCEs were recorded for review, involving 132 patients (Table 1). Patients who experienced TCEs were noted to have a slightly higher average number of diagnoses than did those in the non-TCE group (21 vs 18, respectively) and number of medications (18 vs 15).

Representative examples of TCEs are provided in Table 2. Fifty-eight issues were identified as discharge communication or coordination issues (eg, discharge summary was late or missing at time of discharge to facility, transitional issues were unclear, follow-up appointments were not appropriately scheduled or documented). An additional 52 TCEs were identified as pharmacy or medication issues (eg, medications were inadvertently omitted from discharge medication list, prehospital medication list was incorrect). Medical issues accounted for an additional 27 concerns (eg, patient was hypoglycemic on arrival, inadequate pain control, discovery of new acute medical issues or medical diagnoses that were not clearly documented or communicated by the inpatient team). “Other” issues (two) included unaddressed social concerns, such as insurance issues.

The ECHO-CT model unites hospital and post–acute care providers to improve transitions of care and is unique in its focus on the transition from hospital to post–acute care rather than to home care. In 2 years of data collection, we identified several TCEs encompassing a range of concerns. Of the 675 patients discussed, 132 (20%) were noted to have a TCE. When these percentages are applied to the 140 million Medicare hospital discharges that took place during 2000 to 2015, we would estimate nearly 5.5 million TCEs, or 375,000 TCEs per year, that may have affected this population.

The majority of TCEs were communication and coordination errors. Missing or incomplete discharge paperwork, inadequate documentation of inpatient care, and confusion about medical devices or postoperative needs (eg, slings, braces, wound care, drains) were commonly reported. Follow-up appointments with specialists were often not appropriately scheduled or communicated. This may have resulted from unstandardized discharge documentation and a lower priority given to documentation in the setting of multiple clinical demands (eg, direct patient care, complex care coordination, and clinical paperwork and charting). Studies have demonstrated that fewer than one-third of discharge summaries are received by outpatient providers before postdischarge follow-up, and additionally that nearly 40% of patients did not undergo recommended workups for medical issues identified during their hospital stay.^7,8 All of this is problematic because appropriate documentation in discharge summaries is associated with a decreased risk of hospital readmission.⁹

Pharmacy issues were the second most common TCE identified. One member of the post–acute care team noted that “omissions, additions, and replacements” relating to medications were common occurrences. Additionally, it was noted that medications were inadvertently continued for longer than planned or not adjusted appropriately with changing clinical parameters, such as renal function. The results of our analysis are consistent with current literature, which suggests that up to 60% of all medication errors occur during the period surrounding transitions of care.¹⁰

There were several limitations to this investigation. Though recording of identified TCEs occurred in real time, analysis of these identified events occurred retrospectively; therefore, investigators had limited ability to retroactively review or recategorize recorded issues, which potentially could have resulted in misclassification or misinterpretation. Additionally, the data were intended to be descriptive; therefore, outcomes such as hospital readmission and patient harm could not be linked to specific TCEs. Furthermore, it is possible that events were not detected by either the postdischarge team or the hospital-based team and, therefore, not captured in this analysis. Further work would be helpful to determine the root causes underlying the identified issues in care transitions, with the goal of improving patient safety and avoiding preventable errors during transitions of care. Although there is comprehensive literature related to errors and medication-related adverse events,¹¹ there is not a consensus of how to classify and report, in a standardized fashion, events arising during the transition period. A validated structure for systematically identifying, monitoring, recording, and reporting issues arising during care transitions will be critical in preventing errors and ensuring patient safety during this high-risk period.

Our model is a unique intervention that uses the expertise and engagement of an interdisciplinary team and seeks to identify and remedy issues arising during transitions of care—in real time—to prevent direct harm to vulnerable patients. We have already implemented interventions to improve care based on our experiences with this videoconference-based program. For example, direct feedback was given to discharging teams to improve the discharge summary and associated documentation, and changes to the medication-ordering system were implemented to address specific medication errors discovered. The TCEs identified in this investigation highlight specific areas for improvement with the goal of providing high-quality care for patients and seamless transitions to post–acute care. As health systems and hospitals face new challenges in communication and care coordination, especially due to the recent COVID-19 pandemic, the technology and communication methods used in the ECHO-CT model may become even more relevant for promoting clear communication and patient safety during transitions of care.

The ECHO CT team thanks Sabrina Carretie for her contributions in data collection and analysis.

Anaphylaxis, an acute, life-threatening allergic response, affects multiple organ systems and manifests variably. Anaphylaxis is likely taking place if one or more of the following occurs: (a) sudden- onset skin and mucosal tissue swelling, (b) skin and mucosal abnormalities or respiratory or gastrointestinal symptoms after exposure to an allergen, or (c) reduced blood pressure after exposure to an allergen. With an estimated lifetime prevalence of up to 5.1%, it is a significant cause of morbidity in adults and children.¹ The 2020 anaphylaxis practice parameter update provides recommendations on treatment, prevention, and assessment of biphasic symptom risk in patients experiencing anaphylaxis.² The guideline provides five key recommendations and four good-practice statements, which we have consolidated into five recommendations for this update.

Recommendation 1. All patients with suspected or confirmed anaphylaxis should be treated with epinephrine. (Good-practice statement)

Self-injectable epinephrine is the first-line treatment for anaphylaxis, with weight-based dosing of 0.15 mg/kg for children weighing less than 30 kg and 0.30 mg/kg for children weighing more than 30 kg and adults. Delayed administration of epinephrine can increase anaphylaxis-associated morbidity and mortality. After epinephrine administration, patients should be observed in a healthcare setting for symptom resolution.

Recommendation 2. For all patients, clinicians should assess the risk for developing biphasic anaphylaxis. (Conditional recommendation, very low quality of evidence)

Biphasic anaphylaxis is defined as the return of anaphylaxis symptoms after an asymptomatic period of at least 1 hour, all during a single instance of anaphylaxis. Biphasic anaphylaxis occurs in up to 20% of patients.³ Biphasic anaphylaxis is more likely among patients receiving repeated doses of epinephrine (odds ratio [OR], 4.82; 95% CI, 2.70-8.58), delayed epinephrine administration greater than 60 minutes (OR, 2.29; 95% CI, 1.09-4.79), or a severe initial presentation (OR, 4.82; 95% CI, 1.23-3.61).² The presence of any of these risk factors raises the risk for developing biphasic anaphylaxis by 17%.⁴ Severe anaphylaxis is characterized by life-threatening symptoms, including loss of consciousness, syncope or dizziness, hypotension, cardiovascular system collapse, or neurologic dysfunction from hypoperfusion or hypoxia after exposure to an allergen.⁵

Other risk factors for biphasic anaphylaxis in all ages include a widened pulse pressure, unknown anaphylaxis trigger, and cutaneous signs and symptoms. Drug triggers are also a risk factor in pediatric patients.²

Recommendation 3. All patients with anaphylaxis and risk factors for biphasic anaphylaxis should undergo extended clinical observation in a setting capable of managing anaphylaxis. (Conditional recommendation, very low quality of evidence)

All patients should be monitored for resolution of symptoms prior to discharge, regardless of age or severity at onset. Patients with all three of the following can be discharged 1 hour after symptom resolution because these three factors together have a 95% negative predictive value for biphasic anaphylaxis: nonsevere anaphylaxis, prompt response to epinephrine, and access to medical care.⁵ In contrast, extended observation of at least 6 hours should be offered to patients with increased risk of biphasic reactions. Patients who have potentially fatal underlying illnesses (eg, severe respiratory or cardiac disease), poor access to emergency medical services, poor self-management skills, or inability to access epinephrine should also be considered for extended observation or hospitalization. Evidence is lacking to define the optimal observation time because extended biphasic reactions can occur from 1 to 78 hours after initial anaphylaxis symptoms.⁶

Given the lack of specific evidence around length of observation, there is an opportunity for shared decision-making. Every patient should receive education regarding trigger avoidance, reasons to seek care or activate emergency medical services, and warning signs of biphasic anaphylaxis. Additionally, self-injectable epinephrine and an action plan detailing how and when to administer the epinephrine should be provided. Patients with anaphylaxis should follow up with an allergist.

Recommendation 4. Administration of glucocorticoids or antihistamines for prevention of biphasic anaphylaxis is not recommended. (Conditional recommendation, very low quality of evidence)

This guideline discourages glucocorticoids and antihistamines as a primary treatment as it may delay epinephrine administration. Despite treating the cutaneous manifestations of anaphylaxis, antihistamines fail to treat the life-threatening cardiovascular and respiratory symptoms. No clear evidence exists on whether antihistamines or glucocorticoids prevent biphasic anaphylaxis.

Recommendation 5. In adult patients receiving chemotherapy, premedication with antihistamine and/or glucocorticoid should be used to prevent anaphylaxis or infusion-related reactions for some chemotherapeutic agents in patients with no previous reaction to the drug. (Conditional recommendation, very low quality of evidence)

Premedication with antihistamines and/or glucocorticoids was associated with 51% reduced odds for anaphylaxis and infusion-related reactions to certain chemotherapy agents (pegaspargase, docetaxel, carboplatin, oxaliplatin, and rituximab) in adults who had not previously experienced a reaction to the drug (OR, 0.49; 95% CI, 0.37-0.66).² However, this same benefit was not found with other chemotherapy agents for patients without a prior allergic reaction to the agent, which allows clinicians to defer premedication. The benefit of premedication with antihistamines and/or glucocorticoids to patients with prior anaphylactic reactions to chemotherapy agents was not evaluated in this guideline, nor was the role premedication plays in desensitization to chemotherapy.

This guideline was created by a panel of allergists, clinical immunologists, and methodologists using the GRADE (Grading of Recommendations, Assessment, Development and Evaluations) approach to draft recommendations. Conflicts of interest (COI) were disclosed by all panel members according to the American Academy of Allergy, Asthma, and Immunology (AAAAI) guidelines. The inclusion of many observational studies and meta-analyses improves the generalizability of the guideline. The authors highlighted the low certainty of evidence due to the lack of randomized controlled trials and significant heterogeneity of the included studies.

Some recommendations in the guideline have implications for costs of care. A recent economic analysis looked at cost-effectiveness for extended observation for anaphylaxis and found it was cost-effective only when patients were at increased risk for biphasic anaphylaxis.⁷ Although Recommendation 4 advises against the use of glucocorticoids for prevention of biphasic anaphylaxis, one retrospective cohort study demonstrated that glucocorticoid use was associated with decreased length of stay in children admitted with anaphylaxis.⁸ Therefore, the recommendation to avoid glucocorticoids for prevention of biphasic anaphylaxis could possibly increase hospital length of stay for children. The usefulness of dexamethasone to prevent biphasic anaphylaxis in children 3 to 14 months old is being evaluated in a randomized trial (ClinicalTrials.gov, NCT03523221).

Future research should better characterize risk factors for biphasic reactions to aid in clinical triage and diagnosis. Additional studies are needed to determine the optimal observation duration for patients experiencing anaphylactic reactions or requiring multiple doses of epinephrine. The role of premedication in patients receiving chemotherapy is poorly described, with few studies evaluating the benefit of premedication in patients with previous anaphylactic reactions.

Anaphylaxis, an acute, life-threatening allergic response, affects multiple organ systems and manifests variably. Anaphylaxis is likely taking place if one or more of the following occurs: (a) sudden- onset skin and mucosal tissue swelling, (b) skin and mucosal abnormalities or respiratory or gastrointestinal symptoms after exposure to an allergen, or (c) reduced blood pressure after exposure to an allergen. With an estimated lifetime prevalence of up to 5.1%, it is a significant cause of morbidity in adults and children.¹ The 2020 anaphylaxis practice parameter update provides recommendations on treatment, prevention, and assessment of biphasic symptom risk in patients experiencing anaphylaxis.² The guideline provides five key recommendations and four good-practice statements, which we have consolidated into five recommendations for this update.

Recommendation 1. All patients with suspected or confirmed anaphylaxis should be treated with epinephrine. (Good-practice statement)

Self-injectable epinephrine is the first-line treatment for anaphylaxis, with weight-based dosing of 0.15 mg/kg for children weighing less than 30 kg and 0.30 mg/kg for children weighing more than 30 kg and adults. Delayed administration of epinephrine can increase anaphylaxis-associated morbidity and mortality. After epinephrine administration, patients should be observed in a healthcare setting for symptom resolution.

Recommendation 2. For all patients, clinicians should assess the risk for developing biphasic anaphylaxis. (Conditional recommendation, very low quality of evidence)

Biphasic anaphylaxis is defined as the return of anaphylaxis symptoms after an asymptomatic period of at least 1 hour, all during a single instance of anaphylaxis. Biphasic anaphylaxis occurs in up to 20% of patients.³ Biphasic anaphylaxis is more likely among patients receiving repeated doses of epinephrine (odds ratio [OR], 4.82; 95% CI, 2.70-8.58), delayed epinephrine administration greater than 60 minutes (OR, 2.29; 95% CI, 1.09-4.79), or a severe initial presentation (OR, 4.82; 95% CI, 1.23-3.61).² The presence of any of these risk factors raises the risk for developing biphasic anaphylaxis by 17%.⁴ Severe anaphylaxis is characterized by life-threatening symptoms, including loss of consciousness, syncope or dizziness, hypotension, cardiovascular system collapse, or neurologic dysfunction from hypoperfusion or hypoxia after exposure to an allergen.⁵

Other risk factors for biphasic anaphylaxis in all ages include a widened pulse pressure, unknown anaphylaxis trigger, and cutaneous signs and symptoms. Drug triggers are also a risk factor in pediatric patients.²

Recommendation 3. All patients with anaphylaxis and risk factors for biphasic anaphylaxis should undergo extended clinical observation in a setting capable of managing anaphylaxis. (Conditional recommendation, very low quality of evidence)

All patients should be monitored for resolution of symptoms prior to discharge, regardless of age or severity at onset. Patients with all three of the following can be discharged 1 hour after symptom resolution because these three factors together have a 95% negative predictive value for biphasic anaphylaxis: nonsevere anaphylaxis, prompt response to epinephrine, and access to medical care.⁵ In contrast, extended observation of at least 6 hours should be offered to patients with increased risk of biphasic reactions. Patients who have potentially fatal underlying illnesses (eg, severe respiratory or cardiac disease), poor access to emergency medical services, poor self-management skills, or inability to access epinephrine should also be considered for extended observation or hospitalization. Evidence is lacking to define the optimal observation time because extended biphasic reactions can occur from 1 to 78 hours after initial anaphylaxis symptoms.⁶

Given the lack of specific evidence around length of observation, there is an opportunity for shared decision-making. Every patient should receive education regarding trigger avoidance, reasons to seek care or activate emergency medical services, and warning signs of biphasic anaphylaxis. Additionally, self-injectable epinephrine and an action plan detailing how and when to administer the epinephrine should be provided. Patients with anaphylaxis should follow up with an allergist.

Recommendation 4. Administration of glucocorticoids or antihistamines for prevention of biphasic anaphylaxis is not recommended. (Conditional recommendation, very low quality of evidence)

This guideline discourages glucocorticoids and antihistamines as a primary treatment as it may delay epinephrine administration. Despite treating the cutaneous manifestations of anaphylaxis, antihistamines fail to treat the life-threatening cardiovascular and respiratory symptoms. No clear evidence exists on whether antihistamines or glucocorticoids prevent biphasic anaphylaxis.

Recommendation 5. In adult patients receiving chemotherapy, premedication with antihistamine and/or glucocorticoid should be used to prevent anaphylaxis or infusion-related reactions for some chemotherapeutic agents in patients with no previous reaction to the drug. (Conditional recommendation, very low quality of evidence)

Premedication with antihistamines and/or glucocorticoids was associated with 51% reduced odds for anaphylaxis and infusion-related reactions to certain chemotherapy agents (pegaspargase, docetaxel, carboplatin, oxaliplatin, and rituximab) in adults who had not previously experienced a reaction to the drug (OR, 0.49; 95% CI, 0.37-0.66).² However, this same benefit was not found with other chemotherapy agents for patients without a prior allergic reaction to the agent, which allows clinicians to defer premedication. The benefit of premedication with antihistamines and/or glucocorticoids to patients with prior anaphylactic reactions to chemotherapy agents was not evaluated in this guideline, nor was the role premedication plays in desensitization to chemotherapy.

This guideline was created by a panel of allergists, clinical immunologists, and methodologists using the GRADE (Grading of Recommendations, Assessment, Development and Evaluations) approach to draft recommendations. Conflicts of interest (COI) were disclosed by all panel members according to the American Academy of Allergy, Asthma, and Immunology (AAAAI) guidelines. The inclusion of many observational studies and meta-analyses improves the generalizability of the guideline. The authors highlighted the low certainty of evidence due to the lack of randomized controlled trials and significant heterogeneity of the included studies.

Some recommendations in the guideline have implications for costs of care. A recent economic analysis looked at cost-effectiveness for extended observation for anaphylaxis and found it was cost-effective only when patients were at increased risk for biphasic anaphylaxis.⁷ Although Recommendation 4 advises against the use of glucocorticoids for prevention of biphasic anaphylaxis, one retrospective cohort study demonstrated that glucocorticoid use was associated with decreased length of stay in children admitted with anaphylaxis.⁸ Therefore, the recommendation to avoid glucocorticoids for prevention of biphasic anaphylaxis could possibly increase hospital length of stay for children. The usefulness of dexamethasone to prevent biphasic anaphylaxis in children 3 to 14 months old is being evaluated in a randomized trial (ClinicalTrials.gov, NCT03523221).

Future research should better characterize risk factors for biphasic reactions to aid in clinical triage and diagnosis. Additional studies are needed to determine the optimal observation duration for patients experiencing anaphylactic reactions or requiring multiple doses of epinephrine. The role of premedication in patients receiving chemotherapy is poorly described, with few studies evaluating the benefit of premedication in patients with previous anaphylactic reactions.

Anaphylaxis, an acute, life-threatening allergic response, affects multiple organ systems and manifests variably. Anaphylaxis is likely taking place if one or more of the following occurs: (a) sudden- onset skin and mucosal tissue swelling, (b) skin and mucosal abnormalities or respiratory or gastrointestinal symptoms after exposure to an allergen, or (c) reduced blood pressure after exposure to an allergen. With an estimated lifetime prevalence of up to 5.1%, it is a significant cause of morbidity in adults and children.¹ The 2020 anaphylaxis practice parameter update provides recommendations on treatment, prevention, and assessment of biphasic symptom risk in patients experiencing anaphylaxis.² The guideline provides five key recommendations and four good-practice statements, which we have consolidated into five recommendations for this update.

Recommendation 1. All patients with suspected or confirmed anaphylaxis should be treated with epinephrine. (Good-practice statement)

Self-injectable epinephrine is the first-line treatment for anaphylaxis, with weight-based dosing of 0.15 mg/kg for children weighing less than 30 kg and 0.30 mg/kg for children weighing more than 30 kg and adults. Delayed administration of epinephrine can increase anaphylaxis-associated morbidity and mortality. After epinephrine administration, patients should be observed in a healthcare setting for symptom resolution.

Recommendation 2. For all patients, clinicians should assess the risk for developing biphasic anaphylaxis. (Conditional recommendation, very low quality of evidence)

Biphasic anaphylaxis is defined as the return of anaphylaxis symptoms after an asymptomatic period of at least 1 hour, all during a single instance of anaphylaxis. Biphasic anaphylaxis occurs in up to 20% of patients.³ Biphasic anaphylaxis is more likely among patients receiving repeated doses of epinephrine (odds ratio [OR], 4.82; 95% CI, 2.70-8.58), delayed epinephrine administration greater than 60 minutes (OR, 2.29; 95% CI, 1.09-4.79), or a severe initial presentation (OR, 4.82; 95% CI, 1.23-3.61).² The presence of any of these risk factors raises the risk for developing biphasic anaphylaxis by 17%.⁴ Severe anaphylaxis is characterized by life-threatening symptoms, including loss of consciousness, syncope or dizziness, hypotension, cardiovascular system collapse, or neurologic dysfunction from hypoperfusion or hypoxia after exposure to an allergen.⁵

Other risk factors for biphasic anaphylaxis in all ages include a widened pulse pressure, unknown anaphylaxis trigger, and cutaneous signs and symptoms. Drug triggers are also a risk factor in pediatric patients.²

Recommendation 3. All patients with anaphylaxis and risk factors for biphasic anaphylaxis should undergo extended clinical observation in a setting capable of managing anaphylaxis. (Conditional recommendation, very low quality of evidence)

All patients should be monitored for resolution of symptoms prior to discharge, regardless of age or severity at onset. Patients with all three of the following can be discharged 1 hour after symptom resolution because these three factors together have a 95% negative predictive value for biphasic anaphylaxis: nonsevere anaphylaxis, prompt response to epinephrine, and access to medical care.⁵ In contrast, extended observation of at least 6 hours should be offered to patients with increased risk of biphasic reactions. Patients who have potentially fatal underlying illnesses (eg, severe respiratory or cardiac disease), poor access to emergency medical services, poor self-management skills, or inability to access epinephrine should also be considered for extended observation or hospitalization. Evidence is lacking to define the optimal observation time because extended biphasic reactions can occur from 1 to 78 hours after initial anaphylaxis symptoms.⁶

Given the lack of specific evidence around length of observation, there is an opportunity for shared decision-making. Every patient should receive education regarding trigger avoidance, reasons to seek care or activate emergency medical services, and warning signs of biphasic anaphylaxis. Additionally, self-injectable epinephrine and an action plan detailing how and when to administer the epinephrine should be provided. Patients with anaphylaxis should follow up with an allergist.

Recommendation 4. Administration of glucocorticoids or antihistamines for prevention of biphasic anaphylaxis is not recommended. (Conditional recommendation, very low quality of evidence)

This guideline discourages glucocorticoids and antihistamines as a primary treatment as it may delay epinephrine administration. Despite treating the cutaneous manifestations of anaphylaxis, antihistamines fail to treat the life-threatening cardiovascular and respiratory symptoms. No clear evidence exists on whether antihistamines or glucocorticoids prevent biphasic anaphylaxis.

Recommendation 5. In adult patients receiving chemotherapy, premedication with antihistamine and/or glucocorticoid should be used to prevent anaphylaxis or infusion-related reactions for some chemotherapeutic agents in patients with no previous reaction to the drug. (Conditional recommendation, very low quality of evidence)

Premedication with antihistamines and/or glucocorticoids was associated with 51% reduced odds for anaphylaxis and infusion-related reactions to certain chemotherapy agents (pegaspargase, docetaxel, carboplatin, oxaliplatin, and rituximab) in adults who had not previously experienced a reaction to the drug (OR, 0.49; 95% CI, 0.37-0.66).² However, this same benefit was not found with other chemotherapy agents for patients without a prior allergic reaction to the agent, which allows clinicians to defer premedication. The benefit of premedication with antihistamines and/or glucocorticoids to patients with prior anaphylactic reactions to chemotherapy agents was not evaluated in this guideline, nor was the role premedication plays in desensitization to chemotherapy.

This guideline was created by a panel of allergists, clinical immunologists, and methodologists using the GRADE (Grading of Recommendations, Assessment, Development and Evaluations) approach to draft recommendations. Conflicts of interest (COI) were disclosed by all panel members according to the American Academy of Allergy, Asthma, and Immunology (AAAAI) guidelines. The inclusion of many observational studies and meta-analyses improves the generalizability of the guideline. The authors highlighted the low certainty of evidence due to the lack of randomized controlled trials and significant heterogeneity of the included studies.

Some recommendations in the guideline have implications for costs of care. A recent economic analysis looked at cost-effectiveness for extended observation for anaphylaxis and found it was cost-effective only when patients were at increased risk for biphasic anaphylaxis.⁷ Although Recommendation 4 advises against the use of glucocorticoids for prevention of biphasic anaphylaxis, one retrospective cohort study demonstrated that glucocorticoid use was associated with decreased length of stay in children admitted with anaphylaxis.⁸ Therefore, the recommendation to avoid glucocorticoids for prevention of biphasic anaphylaxis could possibly increase hospital length of stay for children. The usefulness of dexamethasone to prevent biphasic anaphylaxis in children 3 to 14 months old is being evaluated in a randomized trial (ClinicalTrials.gov, NCT03523221).

Future research should better characterize risk factors for biphasic reactions to aid in clinical triage and diagnosis. Additional studies are needed to determine the optimal observation duration for patients experiencing anaphylactic reactions or requiring multiple doses of epinephrine. The role of premedication in patients receiving chemotherapy is poorly described, with few studies evaluating the benefit of premedication in patients with previous anaphylactic reactions.

Osteoporosis is the most prevalent bone disease and a leading cause of morbidity and mortality in older people. According to the National Health and Nutrition Examination Survey, from 2005-2010, there were an estimated 10.2 million adults 50 years and older with osteoporosis and 43.4 million more with low bone mass in the United States.¹ Osteoporotic fracture is a leading cause of hospitalization in the United States for women 55 years or older, ahead of heart attacks, stroke, and breast cancer.² Despite elucidation of the pathogenesis of osteoporosis and the advent of effective and widely available therapies, a “treatment gap” separates the many patients who warrant therapy from the few who receive it. Systematic improvement strategies, such as coordinator-based fracture liaison services, have had a positive impact on addressing this treatment gap.³ There is an opportunity for hospitalists to further narrow this treatment gap.

The American Society of Bone and Mineral Research, in conjunction with the Center for Medical Technology Policy, developed consensus clinical recommendations to address secondary fracture prevention for people 65 years or older who have experienced a hip or vertebral fracture.⁴ We address six of the fundamental and two of the supplemental recommendations as they apply to the practice of hospital medicine.

Recommendations 1 and 2

Communicate key information to the patient and their usual healthcare provider. Patients 65 years or older with a hip or vertebral fracture likely have osteoporosis and are at high risk for subsequent fractures, which can lead to a decline in function and an increase in mortality. Patients must be counseled regarding their diagnosis, their risks, and the actions they can take to manage their disease. Primary care providers must be notified of the occurrence of the fracture, the diagnosis of osteoporosis, and the plans for management.

We recommend hospitalists act as leading advocates for at-risk patients to ensure that this communication occurs during hospitalization. We encourage hospitals and institutions to adopt systematic interventions to facilitate postdischarge care for these patients. These may include implementing a fracture liaison service, with multidisciplinary secondary fracture–prevention strategies using physicians, pharmacists, nurses, social workers, and case managers for care coordination and treatment initiation.

Elderly patients with osteoporotic fragility fractures are at risk for further morbidity and mortality. Coordination of care between the inpatient care team and the primary care provider is necessary to reduce this risk. In addition to verbal communication and especially when verbal communication is not feasible, discharge documents provided to patients and outpatient providers should clearly identify the occurrence of a hip or vertebral fracture and a discharge diagnosis of osteoporosis if not previously documented, regardless of bone mineral density (BMD) results or lack of testing.

Recommendation 3

Regularly assess fall risk. Patients 65 years or older with a current or prior hip or vertebral fracture must be regularly assessed for risk of falls. Hospitalists can assess patients’ ongoing risk for falls at time of admission or during hospitalization. Risk factors include prior falls; advanced age; visual, auditory, or cognitive impairment; decreased muscle strength; gait and balance impairment; diabetes mellitus; use of multiple medications, and others.⁵ Specialist evaluation by a physical therapist or a physiatrist should be considered. Active medications should be reviewed for adverse effects and interactions. The use of diuretics, antipsychotics, antidepressants, benzodiazepines, antiepileptics, and opioids should be minimized.

Recommendations 4, 5, 6, and 11

Offer pharmacologic therapy and initiate calcium and vitamin D supplementation. Recommendations 4 through 6 and 11 advocate pharmacologic interventions including bisphosphonates, denosumab, vitamin D, and/or calcium to reduce the risk of future fractures. Bisphosphonates are the cornerstone of pharmacologic therapy for secondary fracture prevention. The efficacy of these agents for prevention of subsequent fractures outweighs the potential for interference in healing of surgically repaired bones.⁶ Oral bisphosphonate therapy should be initiated in the hospital or at discharge. Parenteral bisphosphonates and denosumab may be utilized in patients unable to tolerate or absorb oral bisphosphonates due to esophageal or other gastrointestinal disease. Initiation of these agents should be delayed until after vitamin D and calcium supplementation have been administered for 2 weeks after the fracture to reduce the risk of precipitating hypocalcemia, and they should not be used in patients with confirmed hypocalcemia until that is resolved. BMD measurement is not necessary prior to pharmacologic therapy initiation because the risk of fracture is elevated for these patients regardless of BMD. Patients without significant dental disease or planned oral or maxillofacial procedures may begin bisphosphonate therapy prior to a full dental assessment because risk of osteonecrosis of the jaw is low.

The guidelines recommend people 65 years or older with a hip or vertebral fracture receive daily supplementation of at least 800 IU vitamin D. Patients unable to achieve an intake of 1,200 mg/day of calcium from food sources should receive daily calcium supplementation. The effect of vitamin D monotherapy on fracture risk is not clear; however, strong evidence suggests that fracture risk is reduced when individuals at high risk of deficiency receive supplementation with vitamin D and calcium. Calcium supplementation alone has not demonstrated reduction in fracture risk. Total daily calcium intake above 1,500 mg has not been shown to provide additional benefit and is potentially harmful.

Recommendation 9

Counsel patients on lifestyle modifications and consider physical therapy. Tobacco has a deleterious effect on bone density and increases risk for osteoporotic fragility fracture.⁷ Hospitalists should obtain tobacco use history from all patients with an osteoporotic fracture and provide tobacco cessation counseling when appropriate. Excessive alcohol consumption increases the risk of fall injuries.⁸ Hospitalists should counsel patients to limit alcohol intake to a maximum of two drinks a day for men and one drink a day for women.

Weight-bearing and strength-training exercises, particularly those involving balance and trunk muscle strength, are associated with reduction in fall-risk. Exercise must be tailored to the patient’s physical capacity. Hospitalists may partner with physical therapists or physiatrists to facilitate development of an exercise plan to maximize benefit and minimize risk of injury.

We found this document to be highly informative and well cited, with ample evidence to support the recommendations.

Methods in Preparing Guidelines

The multistakeholder coalition did not employ a rigorous and standardized methodology for the guideline, such as GRADE (Grading of Recommendations Assessment, Development, and Evaluation); hence, no assessment of evidence quality, benefits and harms of an intervention, or resource use was provided.

Potential Conflicts for Guideline Authors

Eight guideline authors have pharmaceutical relationships with the manufacturer of one of the medications listed on the guidelines (Amgen-denosumab, Novartis-zoledronic acid). There are no disclosures reported from the multistakeholder coalition members who are not listed as guideline authors.

We anticipate future studies may report outcomes focused on secondary prevention of fractures. Additionally, we would like to see new studies investigating patient-centered outcomes such as improvement in functional status and ambulatory independence based on improved postfracture medical therapies. We see an opportunity for studies assessing real-world outcomes to inform future recommendations, particularly after widespread implementation of secondary fracture prevention therapy either initiated during hospitalization or purposefully planned for after discharge.

We would like to see more trial data comparing the safety and cost-effectiveness of first-line therapy, namely oral bisphosphonates, to alternative treatments, particularly parenteral agents, which may improve treatment compliance because of the convenience in dosing frequency.

Osteoporosis is the most prevalent bone disease and a leading cause of morbidity and mortality in older people. According to the National Health and Nutrition Examination Survey, from 2005-2010, there were an estimated 10.2 million adults 50 years and older with osteoporosis and 43.4 million more with low bone mass in the United States.¹ Osteoporotic fracture is a leading cause of hospitalization in the United States for women 55 years or older, ahead of heart attacks, stroke, and breast cancer.² Despite elucidation of the pathogenesis of osteoporosis and the advent of effective and widely available therapies, a “treatment gap” separates the many patients who warrant therapy from the few who receive it. Systematic improvement strategies, such as coordinator-based fracture liaison services, have had a positive impact on addressing this treatment gap.³ There is an opportunity for hospitalists to further narrow this treatment gap.

The American Society of Bone and Mineral Research, in conjunction with the Center for Medical Technology Policy, developed consensus clinical recommendations to address secondary fracture prevention for people 65 years or older who have experienced a hip or vertebral fracture.⁴ We address six of the fundamental and two of the supplemental recommendations as they apply to the practice of hospital medicine.

Recommendations 1 and 2

Communicate key information to the patient and their usual healthcare provider. Patients 65 years or older with a hip or vertebral fracture likely have osteoporosis and are at high risk for subsequent fractures, which can lead to a decline in function and an increase in mortality. Patients must be counseled regarding their diagnosis, their risks, and the actions they can take to manage their disease. Primary care providers must be notified of the occurrence of the fracture, the diagnosis of osteoporosis, and the plans for management.

We recommend hospitalists act as leading advocates for at-risk patients to ensure that this communication occurs during hospitalization. We encourage hospitals and institutions to adopt systematic interventions to facilitate postdischarge care for these patients. These may include implementing a fracture liaison service, with multidisciplinary secondary fracture–prevention strategies using physicians, pharmacists, nurses, social workers, and case managers for care coordination and treatment initiation.

Elderly patients with osteoporotic fragility fractures are at risk for further morbidity and mortality. Coordination of care between the inpatient care team and the primary care provider is necessary to reduce this risk. In addition to verbal communication and especially when verbal communication is not feasible, discharge documents provided to patients and outpatient providers should clearly identify the occurrence of a hip or vertebral fracture and a discharge diagnosis of osteoporosis if not previously documented, regardless of bone mineral density (BMD) results or lack of testing.

Recommendation 3

Regularly assess fall risk. Patients 65 years or older with a current or prior hip or vertebral fracture must be regularly assessed for risk of falls. Hospitalists can assess patients’ ongoing risk for falls at time of admission or during hospitalization. Risk factors include prior falls; advanced age; visual, auditory, or cognitive impairment; decreased muscle strength; gait and balance impairment; diabetes mellitus; use of multiple medications, and others.⁵ Specialist evaluation by a physical therapist or a physiatrist should be considered. Active medications should be reviewed for adverse effects and interactions. The use of diuretics, antipsychotics, antidepressants, benzodiazepines, antiepileptics, and opioids should be minimized.

Recommendations 4, 5, 6, and 11

Offer pharmacologic therapy and initiate calcium and vitamin D supplementation. Recommendations 4 through 6 and 11 advocate pharmacologic interventions including bisphosphonates, denosumab, vitamin D, and/or calcium to reduce the risk of future fractures. Bisphosphonates are the cornerstone of pharmacologic therapy for secondary fracture prevention. The efficacy of these agents for prevention of subsequent fractures outweighs the potential for interference in healing of surgically repaired bones.⁶ Oral bisphosphonate therapy should be initiated in the hospital or at discharge. Parenteral bisphosphonates and denosumab may be utilized in patients unable to tolerate or absorb oral bisphosphonates due to esophageal or other gastrointestinal disease. Initiation of these agents should be delayed until after vitamin D and calcium supplementation have been administered for 2 weeks after the fracture to reduce the risk of precipitating hypocalcemia, and they should not be used in patients with confirmed hypocalcemia until that is resolved. BMD measurement is not necessary prior to pharmacologic therapy initiation because the risk of fracture is elevated for these patients regardless of BMD. Patients without significant dental disease or planned oral or maxillofacial procedures may begin bisphosphonate therapy prior to a full dental assessment because risk of osteonecrosis of the jaw is low.

The guidelines recommend people 65 years or older with a hip or vertebral fracture receive daily supplementation of at least 800 IU vitamin D. Patients unable to achieve an intake of 1,200 mg/day of calcium from food sources should receive daily calcium supplementation. The effect of vitamin D monotherapy on fracture risk is not clear; however, strong evidence suggests that fracture risk is reduced when individuals at high risk of deficiency receive supplementation with vitamin D and calcium. Calcium supplementation alone has not demonstrated reduction in fracture risk. Total daily calcium intake above 1,500 mg has not been shown to provide additional benefit and is potentially harmful.

Recommendation 9

Counsel patients on lifestyle modifications and consider physical therapy. Tobacco has a deleterious effect on bone density and increases risk for osteoporotic fragility fracture.⁷ Hospitalists should obtain tobacco use history from all patients with an osteoporotic fracture and provide tobacco cessation counseling when appropriate. Excessive alcohol consumption increases the risk of fall injuries.⁸ Hospitalists should counsel patients to limit alcohol intake to a maximum of two drinks a day for men and one drink a day for women.

Weight-bearing and strength-training exercises, particularly those involving balance and trunk muscle strength, are associated with reduction in fall-risk. Exercise must be tailored to the patient’s physical capacity. Hospitalists may partner with physical therapists or physiatrists to facilitate development of an exercise plan to maximize benefit and minimize risk of injury.

We found this document to be highly informative and well cited, with ample evidence to support the recommendations.

Methods in Preparing Guidelines

The multistakeholder coalition did not employ a rigorous and standardized methodology for the guideline, such as GRADE (Grading of Recommendations Assessment, Development, and Evaluation); hence, no assessment of evidence quality, benefits and harms of an intervention, or resource use was provided.

Potential Conflicts for Guideline Authors

Eight guideline authors have pharmaceutical relationships with the manufacturer of one of the medications listed on the guidelines (Amgen-denosumab, Novartis-zoledronic acid). There are no disclosures reported from the multistakeholder coalition members who are not listed as guideline authors.

We anticipate future studies may report outcomes focused on secondary prevention of fractures. Additionally, we would like to see new studies investigating patient-centered outcomes such as improvement in functional status and ambulatory independence based on improved postfracture medical therapies. We see an opportunity for studies assessing real-world outcomes to inform future recommendations, particularly after widespread implementation of secondary fracture prevention therapy either initiated during hospitalization or purposefully planned for after discharge.

We would like to see more trial data comparing the safety and cost-effectiveness of first-line therapy, namely oral bisphosphonates, to alternative treatments, particularly parenteral agents, which may improve treatment compliance because of the convenience in dosing frequency.

Osteoporosis is the most prevalent bone disease and a leading cause of morbidity and mortality in older people. According to the National Health and Nutrition Examination Survey, from 2005-2010, there were an estimated 10.2 million adults 50 years and older with osteoporosis and 43.4 million more with low bone mass in the United States.¹ Osteoporotic fracture is a leading cause of hospitalization in the United States for women 55 years or older, ahead of heart attacks, stroke, and breast cancer.² Despite elucidation of the pathogenesis of osteoporosis and the advent of effective and widely available therapies, a “treatment gap” separates the many patients who warrant therapy from the few who receive it. Systematic improvement strategies, such as coordinator-based fracture liaison services, have had a positive impact on addressing this treatment gap.³ There is an opportunity for hospitalists to further narrow this treatment gap.

The American Society of Bone and Mineral Research, in conjunction with the Center for Medical Technology Policy, developed consensus clinical recommendations to address secondary fracture prevention for people 65 years or older who have experienced a hip or vertebral fracture.⁴ We address six of the fundamental and two of the supplemental recommendations as they apply to the practice of hospital medicine.

Recommendations 1 and 2

Communicate key information to the patient and their usual healthcare provider. Patients 65 years or older with a hip or vertebral fracture likely have osteoporosis and are at high risk for subsequent fractures, which can lead to a decline in function and an increase in mortality. Patients must be counseled regarding their diagnosis, their risks, and the actions they can take to manage their disease. Primary care providers must be notified of the occurrence of the fracture, the diagnosis of osteoporosis, and the plans for management.

We recommend hospitalists act as leading advocates for at-risk patients to ensure that this communication occurs during hospitalization. We encourage hospitals and institutions to adopt systematic interventions to facilitate postdischarge care for these patients. These may include implementing a fracture liaison service, with multidisciplinary secondary fracture–prevention strategies using physicians, pharmacists, nurses, social workers, and case managers for care coordination and treatment initiation.

Elderly patients with osteoporotic fragility fractures are at risk for further morbidity and mortality. Coordination of care between the inpatient care team and the primary care provider is necessary to reduce this risk. In addition to verbal communication and especially when verbal communication is not feasible, discharge documents provided to patients and outpatient providers should clearly identify the occurrence of a hip or vertebral fracture and a discharge diagnosis of osteoporosis if not previously documented, regardless of bone mineral density (BMD) results or lack of testing.

Recommendation 3

Regularly assess fall risk. Patients 65 years or older with a current or prior hip or vertebral fracture must be regularly assessed for risk of falls. Hospitalists can assess patients’ ongoing risk for falls at time of admission or during hospitalization. Risk factors include prior falls; advanced age; visual, auditory, or cognitive impairment; decreased muscle strength; gait and balance impairment; diabetes mellitus; use of multiple medications, and others.⁵ Specialist evaluation by a physical therapist or a physiatrist should be considered. Active medications should be reviewed for adverse effects and interactions. The use of diuretics, antipsychotics, antidepressants, benzodiazepines, antiepileptics, and opioids should be minimized.

Recommendations 4, 5, 6, and 11

Offer pharmacologic therapy and initiate calcium and vitamin D supplementation. Recommendations 4 through 6 and 11 advocate pharmacologic interventions including bisphosphonates, denosumab, vitamin D, and/or calcium to reduce the risk of future fractures. Bisphosphonates are the cornerstone of pharmacologic therapy for secondary fracture prevention. The efficacy of these agents for prevention of subsequent fractures outweighs the potential for interference in healing of surgically repaired bones.⁶ Oral bisphosphonate therapy should be initiated in the hospital or at discharge. Parenteral bisphosphonates and denosumab may be utilized in patients unable to tolerate or absorb oral bisphosphonates due to esophageal or other gastrointestinal disease. Initiation of these agents should be delayed until after vitamin D and calcium supplementation have been administered for 2 weeks after the fracture to reduce the risk of precipitating hypocalcemia, and they should not be used in patients with confirmed hypocalcemia until that is resolved. BMD measurement is not necessary prior to pharmacologic therapy initiation because the risk of fracture is elevated for these patients regardless of BMD. Patients without significant dental disease or planned oral or maxillofacial procedures may begin bisphosphonate therapy prior to a full dental assessment because risk of osteonecrosis of the jaw is low.

The guidelines recommend people 65 years or older with a hip or vertebral fracture receive daily supplementation of at least 800 IU vitamin D. Patients unable to achieve an intake of 1,200 mg/day of calcium from food sources should receive daily calcium supplementation. The effect of vitamin D monotherapy on fracture risk is not clear; however, strong evidence suggests that fracture risk is reduced when individuals at high risk of deficiency receive supplementation with vitamin D and calcium. Calcium supplementation alone has not demonstrated reduction in fracture risk. Total daily calcium intake above 1,500 mg has not been shown to provide additional benefit and is potentially harmful.

Recommendation 9

Counsel patients on lifestyle modifications and consider physical therapy. Tobacco has a deleterious effect on bone density and increases risk for osteoporotic fragility fracture.⁷ Hospitalists should obtain tobacco use history from all patients with an osteoporotic fracture and provide tobacco cessation counseling when appropriate. Excessive alcohol consumption increases the risk of fall injuries.⁸ Hospitalists should counsel patients to limit alcohol intake to a maximum of two drinks a day for men and one drink a day for women.

Weight-bearing and strength-training exercises, particularly those involving balance and trunk muscle strength, are associated with reduction in fall-risk. Exercise must be tailored to the patient’s physical capacity. Hospitalists may partner with physical therapists or physiatrists to facilitate development of an exercise plan to maximize benefit and minimize risk of injury.

We found this document to be highly informative and well cited, with ample evidence to support the recommendations.

Methods in Preparing Guidelines

The multistakeholder coalition did not employ a rigorous and standardized methodology for the guideline, such as GRADE (Grading of Recommendations Assessment, Development, and Evaluation); hence, no assessment of evidence quality, benefits and harms of an intervention, or resource use was provided.

Potential Conflicts for Guideline Authors

Eight guideline authors have pharmaceutical relationships with the manufacturer of one of the medications listed on the guidelines (Amgen-denosumab, Novartis-zoledronic acid). There are no disclosures reported from the multistakeholder coalition members who are not listed as guideline authors.

We anticipate future studies may report outcomes focused on secondary prevention of fractures. Additionally, we would like to see new studies investigating patient-centered outcomes such as improvement in functional status and ambulatory independence based on improved postfracture medical therapies. We see an opportunity for studies assessing real-world outcomes to inform future recommendations, particularly after widespread implementation of secondary fracture prevention therapy either initiated during hospitalization or purposefully planned for after discharge.

We would like to see more trial data comparing the safety and cost-effectiveness of first-line therapy, namely oral bisphosphonates, to alternative treatments, particularly parenteral agents, which may improve treatment compliance because of the convenience in dosing frequency.

It was a busy night in the emergency department. EMS called to give a heads up—they were on their way with a girl who was “pretty banged up.” They warned us that the story seemed a little fishy. We thought we were ready. The trauma bay was organized; supplies were at the ready and everyone had a role. Within seconds of her arrival, it was clear that no one could ever have been truly prepared. She was unresponsive and unstable. Her injuries were widespread, brutal, and long term. My seasoned attendings would describe it as no less than horrific. There was no question—someone had done this to her.

After she was stabilized, her wounds were gently tended, her body was bathed, her hair was combed. She died several days later. While distressed, many members of her team took consolation in the idea that, after years of torture, she finally got to be loved.

It’s no wonder that every person involved with her care during her hospitalization was so deeply affected by her. How could anyone do this to another person? Or even worse, to an innocent child? “What a monster,” we said. “Only a monster could have done this.”

While anyone would agree that what this abuser—the girl’s mother—did was brutal and wrong, I would also argue that the underlying danger is much more systemic. We call her the “monster,” but I sense that the real monster is still lurking in the shadows, unnamed. I can’t help but try to understand this woman; it is unfair to condemn her without first learning her story. How were her actions guided by her own history of trauma, abuse, and violent discipline as a child? We preach to each other and to our learners that trauma-informed care is essential; you must not ask what’s wrong with you, but what happened to you. Founder and Director of the Equal Justice Initiative Bryan Stevenson has said that “each of us is more than the worst thing we’ve ever done.”¹ It’s inhumane of us to dehumanize her for this atrocity, especially without pausing to ask how her environment, personal trauma, and understanding of child development set the risk.

It is important to understand the cycle of trauma and abuse. Traumatic experiences have been shown to alter neurodevelopment and the body’s stress response, particularly when experiences take place early in life, when they are repeated and long term, and when they are severe. We know that adverse childhood experiences are cumulative and result in adverse outcomes as adults, including increased likelihood of violent or criminal activity. We know that prior history of trauma, specifically child abuse, sexual abuse, or domestic violence, is associated with higher potential for child abuse later on.

The effect of experiencing trauma is such that only 22% of adults who experienced abuse or neglect as children will achieve resiliency.² In a world in which social distancing and isolation have become the new normal, we must be even more aware of the effects of trauma on families. The COVID-19 pandemic has increased known risk factors for child abuse, including financial hardship, unemployment, increased anxiety, increased caregiver responsibilities, and decreased access to mental health services and community resources.³ Furthermore, virtual learning environments may have significant implications on the reporting of child abuse. Among cases of maltreatment of children that received an investigation or alternative response in 2018, 20.5% were reported by education personnel.⁴ While remote learning options may be necessary to minimize risk of viral spread, fewer interactions between children and mandatory reporters may result in child maltreatment going undetected. In the face of these challenges, I urge our healthcare system to use current constraints as fuel for creative interventions including the following:

• Applying advances in telemedicine to create a new opportunity to interface with families, provide mental health support, connect them with resources, and offer gentle guidance about safe parenting.
• Improving both screening methods for parental trauma and distress and referrals for support services.
• Advocating for adequate access to life-sustaining resources including shelter, food, and healthcare for all families. This is a necessary foundation for building resilience.
• Providing bias training for mandatory reporters to ensure that all children and their families are approached with respect and compassion.
• Prioritizing innovation that provides long-lasting, sustainable, and equitable access to support and healing.

To best protect our children, we must heal their adult caregivers; we must help them to conquer their monsters.

Our patient and her family have since visited me in my thoughts and dreams, less often now than before. While I never truly knew her, she has left an open void where there should have been the promise of a healthy, growing, and developing child. Within that void resides fear. I fear for other “hidden children” and the abuse they are at risk for experiencing. I fear that her siblings, now living without their mother, will become victims of the instability of being “in the system.” I fear that by turning to punishment as our only solution, we miss opportunities to prevent such tragedy. Despite the darkness, she also brings me hope. I hope that her siblings can rely on each other as a foundation for resilience. I hope that we as a healthcare system can continue to love our patients without question or condition. I hope that we as a society can invest in breaking the cycle of trauma and in supporting parents. I hope that we can create a system in which children can grow up free from abuse.

It was a busy night in the emergency department. EMS called to give a heads up—they were on their way with a girl who was “pretty banged up.” They warned us that the story seemed a little fishy. We thought we were ready. The trauma bay was organized; supplies were at the ready and everyone had a role. Within seconds of her arrival, it was clear that no one could ever have been truly prepared. She was unresponsive and unstable. Her injuries were widespread, brutal, and long term. My seasoned attendings would describe it as no less than horrific. There was no question—someone had done this to her.

After she was stabilized, her wounds were gently tended, her body was bathed, her hair was combed. She died several days later. While distressed, many members of her team took consolation in the idea that, after years of torture, she finally got to be loved.

It’s no wonder that every person involved with her care during her hospitalization was so deeply affected by her. How could anyone do this to another person? Or even worse, to an innocent child? “What a monster,” we said. “Only a monster could have done this.”

While anyone would agree that what this abuser—the girl’s mother—did was brutal and wrong, I would also argue that the underlying danger is much more systemic. We call her the “monster,” but I sense that the real monster is still lurking in the shadows, unnamed. I can’t help but try to understand this woman; it is unfair to condemn her without first learning her story. How were her actions guided by her own history of trauma, abuse, and violent discipline as a child? We preach to each other and to our learners that trauma-informed care is essential; you must not ask what’s wrong with you, but what happened to you. Founder and Director of the Equal Justice Initiative Bryan Stevenson has said that “each of us is more than the worst thing we’ve ever done.”¹ It’s inhumane of us to dehumanize her for this atrocity, especially without pausing to ask how her environment, personal trauma, and understanding of child development set the risk.

It is important to understand the cycle of trauma and abuse. Traumatic experiences have been shown to alter neurodevelopment and the body’s stress response, particularly when experiences take place early in life, when they are repeated and long term, and when they are severe. We know that adverse childhood experiences are cumulative and result in adverse outcomes as adults, including increased likelihood of violent or criminal activity. We know that prior history of trauma, specifically child abuse, sexual abuse, or domestic violence, is associated with higher potential for child abuse later on.

The effect of experiencing trauma is such that only 22% of adults who experienced abuse or neglect as children will achieve resiliency.² In a world in which social distancing and isolation have become the new normal, we must be even more aware of the effects of trauma on families. The COVID-19 pandemic has increased known risk factors for child abuse, including financial hardship, unemployment, increased anxiety, increased caregiver responsibilities, and decreased access to mental health services and community resources.³ Furthermore, virtual learning environments may have significant implications on the reporting of child abuse. Among cases of maltreatment of children that received an investigation or alternative response in 2018, 20.5% were reported by education personnel.⁴ While remote learning options may be necessary to minimize risk of viral spread, fewer interactions between children and mandatory reporters may result in child maltreatment going undetected. In the face of these challenges, I urge our healthcare system to use current constraints as fuel for creative interventions including the following:

• Applying advances in telemedicine to create a new opportunity to interface with families, provide mental health support, connect them with resources, and offer gentle guidance about safe parenting.
• Improving both screening methods for parental trauma and distress and referrals for support services.
• Advocating for adequate access to life-sustaining resources including shelter, food, and healthcare for all families. This is a necessary foundation for building resilience.
• Providing bias training for mandatory reporters to ensure that all children and their families are approached with respect and compassion.
• Prioritizing innovation that provides long-lasting, sustainable, and equitable access to support and healing.

To best protect our children, we must heal their adult caregivers; we must help them to conquer their monsters.

Our patient and her family have since visited me in my thoughts and dreams, less often now than before. While I never truly knew her, she has left an open void where there should have been the promise of a healthy, growing, and developing child. Within that void resides fear. I fear for other “hidden children” and the abuse they are at risk for experiencing. I fear that her siblings, now living without their mother, will become victims of the instability of being “in the system.” I fear that by turning to punishment as our only solution, we miss opportunities to prevent such tragedy. Despite the darkness, she also brings me hope. I hope that her siblings can rely on each other as a foundation for resilience. I hope that we as a healthcare system can continue to love our patients without question or condition. I hope that we as a society can invest in breaking the cycle of trauma and in supporting parents. I hope that we can create a system in which children can grow up free from abuse.

It was a busy night in the emergency department. EMS called to give a heads up—they were on their way with a girl who was “pretty banged up.” They warned us that the story seemed a little fishy. We thought we were ready. The trauma bay was organized; supplies were at the ready and everyone had a role. Within seconds of her arrival, it was clear that no one could ever have been truly prepared. She was unresponsive and unstable. Her injuries were widespread, brutal, and long term. My seasoned attendings would describe it as no less than horrific. There was no question—someone had done this to her.

After she was stabilized, her wounds were gently tended, her body was bathed, her hair was combed. She died several days later. While distressed, many members of her team took consolation in the idea that, after years of torture, she finally got to be loved.

It’s no wonder that every person involved with her care during her hospitalization was so deeply affected by her. How could anyone do this to another person? Or even worse, to an innocent child? “What a monster,” we said. “Only a monster could have done this.”

While anyone would agree that what this abuser—the girl’s mother—did was brutal and wrong, I would also argue that the underlying danger is much more systemic. We call her the “monster,” but I sense that the real monster is still lurking in the shadows, unnamed. I can’t help but try to understand this woman; it is unfair to condemn her without first learning her story. How were her actions guided by her own history of trauma, abuse, and violent discipline as a child? We preach to each other and to our learners that trauma-informed care is essential; you must not ask what’s wrong with you, but what happened to you. Founder and Director of the Equal Justice Initiative Bryan Stevenson has said that “each of us is more than the worst thing we’ve ever done.”¹ It’s inhumane of us to dehumanize her for this atrocity, especially without pausing to ask how her environment, personal trauma, and understanding of child development set the risk.

It is important to understand the cycle of trauma and abuse. Traumatic experiences have been shown to alter neurodevelopment and the body’s stress response, particularly when experiences take place early in life, when they are repeated and long term, and when they are severe. We know that adverse childhood experiences are cumulative and result in adverse outcomes as adults, including increased likelihood of violent or criminal activity. We know that prior history of trauma, specifically child abuse, sexual abuse, or domestic violence, is associated with higher potential for child abuse later on.

The effect of experiencing trauma is such that only 22% of adults who experienced abuse or neglect as children will achieve resiliency.² In a world in which social distancing and isolation have become the new normal, we must be even more aware of the effects of trauma on families. The COVID-19 pandemic has increased known risk factors for child abuse, including financial hardship, unemployment, increased anxiety, increased caregiver responsibilities, and decreased access to mental health services and community resources.³ Furthermore, virtual learning environments may have significant implications on the reporting of child abuse. Among cases of maltreatment of children that received an investigation or alternative response in 2018, 20.5% were reported by education personnel.⁴ While remote learning options may be necessary to minimize risk of viral spread, fewer interactions between children and mandatory reporters may result in child maltreatment going undetected. In the face of these challenges, I urge our healthcare system to use current constraints as fuel for creative interventions including the following:

• Applying advances in telemedicine to create a new opportunity to interface with families, provide mental health support, connect them with resources, and offer gentle guidance about safe parenting.
• Improving both screening methods for parental trauma and distress and referrals for support services.
• Advocating for adequate access to life-sustaining resources including shelter, food, and healthcare for all families. This is a necessary foundation for building resilience.
• Providing bias training for mandatory reporters to ensure that all children and their families are approached with respect and compassion.
• Prioritizing innovation that provides long-lasting, sustainable, and equitable access to support and healing.

To best protect our children, we must heal their adult caregivers; we must help them to conquer their monsters.

Our patient and her family have since visited me in my thoughts and dreams, less often now than before. While I never truly knew her, she has left an open void where there should have been the promise of a healthy, growing, and developing child. Within that void resides fear. I fear for other “hidden children” and the abuse they are at risk for experiencing. I fear that her siblings, now living without their mother, will become victims of the instability of being “in the system.” I fear that by turning to punishment as our only solution, we miss opportunities to prevent such tragedy. Despite the darkness, she also brings me hope. I hope that her siblings can rely on each other as a foundation for resilience. I hope that we as a healthcare system can continue to love our patients without question or condition. I hope that we as a society can invest in breaking the cycle of trauma and in supporting parents. I hope that we can create a system in which children can grow up free from abuse.

The day after Memorial Day 2020 marked an important transition in the United States’ experience with coronavirus disease 2019 (COVID-19), with many states making initial plans to reopen. Alongside this reopening process, the US healthcare system needed to reopen to provide needed care to communities. This reopening, however, was in the context of several months of staggering financial losses for many medical centers that expected a larger surge than occurred locally and lost profit because of delayed elective procedures, all amid a national economic recession. Each medical center also faced a persistent risk of infection and a call for social equity as each one decided how to reopen. These decisions balanced the risks of reopening from COVID-19 exposure with patients’ medical needs and the healthcare industry’s financial needs.

This year’s widespread healthcare closures were necessary to reduce COVID-19 transmission and prepare for a future patient surge, but these closures had unintended consequences. Nearly half of adults polled said they or someone in their household had foregone or delayed care since the outbreak began.¹ This was especially true for visits to emergency departments and doctors’ offices for strokes, heart attacks, and routine medical care.² In a survey across 49 states, only 7% of primary care practices considered scheduling preventive visits as a high priority.³ Eleven percent of polled adults reported delaying care worsened their condition,¹ and in hard-hit areas such as New York City, non-COVID mortality was 22% higher than expected.⁴

Avoidance of the medical system decreased not only use of necessary, high-value care but also use of low-value care. Low-value services are those in which the “potential for harms exceed the potential benefits,”⁵ such as unnecessary hospitalizations, avoidable emergency department or clinic visits, unwarranted or excessive diagnostic testing (eg, annual physicals), and certain procedures (eg, spinal fusion surgery for low-back pain). Low-value care is costly, with $75.7 to $101.2 billion of the gross domestic product (GDP) spent on overuse.⁶ This care risks contributing to financial and, in turn, clinical harm for patients because the average health plan deductible exceeds a typical family’s available savings⁷and 25% of Americans say they have foregone treatment for a serious medical condition in the past year because of these costs.⁸ Medical centers’ significant financial losses are a sobering reminder of how much our system relies on fee-for-service billing that encourages high-margin profitable services regardless of necessity.⁹ We must avoid quick reactions of increasing these procedures to respond to the sudden financial loses.Medical centers across the country are choosing how to “reboot”—either deliberately changing how services are organized and delivered or returning to prior practices. Medical centers are facing potential for their own Renaissance in transitioning their organizations to modern healthcare delivery. In the 15th century ad, after experiencing the bubonic plague, Europe similarly transitioned toward modernity and great social change. Through the initial pandemic wave, we learned that even the largest health system could change their practices rapidly. COVID-19 achieved in 8 weeks what years of research, policy initiatives (eg, Choosing Wisely®, RightCare, Less Is More), and emphasizing value in reimbursement could not: stopping the delivery of a wide range of low-value services. We share three lessons learned from medical centers that have begun reopening services that can help us to better ensure higher-value, more affordable care that meets patients’ needs.

Medical centers can better focus on high-value care by defining their high-risk patient populations; high-value treatments, procedures, and preventive care; and phases of reopening. During the first pandemic wave, medical centers tried to reassure patients about emergency care, such as coming in for chest pain or neurologic symptoms, through personal outreach and media campaigns. Outpatient virtual visits also continued, including primary care, specialty services, mental health treatment, and physical therapy. While reopening, some medical centers have assessed disparities by relying on their data analytics and, if available, embedded health services researchers to understand what care was stopped and what populations were most affected.

The University of California health systems, for example, had a learning collaborative focused on sharing methods to restore care delivery that prioritizes patient needs. Some campuses conducted analyses using both electronic health record data and input from patients and their care teams to identify clinical needs and determine patient outreach plans. Some approaches used machine learning models to identify patients at highest risk of hospitalization or emergency department visits over the next 12 months and to conduct additional outreach to schedule these patients in primary and specialty care if clinically appropriate. Similarly, surgical specialties identified the highest-priority nonemergent surgeries for scheduling, including cancer resection, radiation therapy, and pain-management procedures. Similar guidance toward the most meaningful care has been prioritized within the United States Department of Veterans Affairs.

The rapid deployment of telehealth and payment models that reimburse video and in-clinic visits equally created new opportunities for medical centers to expand high-value care in lower-cost home settings. Similarly, new infrastructure is being developed to help define smarter use of virtual visits and home-based lab collection and monitoring.

Medical centers also must pay careful attention to redeploying service capacity for underused, high-value services. The pandemic uncovered existing staff that could be redeployed to support these changes. For example, with an “all hands on deck” mentality during the pandemic, in some medical centers, analysts or care managers from less-prioritized or duplicative areas were reassigned to vital COVID-19 efforts. Medical centers may realize that this staff can provide more value in the future by supporting increased high-value, affordable healthcare.

During the intial wave of the pandemic, medical centers greatly reduced the care they provided, often focusing on delivering essential care. This preparation for a surge of COVID patients had the effect of halting many unnecessary services by moving care from the clinic to home under new reimbursement changes, such as those affecting telehealth payments. The experience of reducing low-value medical services and visits can be extended to limiting unnecessary diagnostic testing. Medical centers could, for example, focus only on tests that advance care plans; reduce unnecessary blood draws, procedures, and vital sign checks on stable patients; shift to medications with less-frequent dosing intervals; and consolidate visits by treatment teams.^10,11

Medical centers, however, now face continued pressures to increase revenue because 75% report their organization’s top priority is focused on increasing patient volume.¹²Nearly 95% of healthcare payments have been based on fee-for-service models,¹³ and the COVID-19 pandemic highlighted the financial vulnerability of our health system when we reduce in-person care, especially among rural medical centers who often have no financial reserve.¹⁴ Similarly, nearly half of hospitals’ revenue comes from surgical admissions, though not all of these are necessary.^15-18 The fiscal realities facing medical centers make it challenging to not simply “ramp up” all service, regardless of necessity, in the context of payment models dependent on fee for service, which are present in most areas of the country.

As medical centers reboot, operational and clinical leaders must proactively view changes through an equity lens to avoid exacerbating health disparities among vulnerable populations. The pandemic has focused national attention on the severity and pervasiveness of disparities and created an imperative for substantive action to evaluate how every decision will affect health equity. For example, medical centers are expanding use of telehealth to improve patient outreach. However, in a survey of primary care physicians, 72% said they have patients who are unable to access telehealth because they do not have access to technology.³ Exclusion of these patients from programs risks worsening health disparities. In a recent survey, nearly 65% of medical centers report reexamining existing policies, protocols, and practices for patients at risk of disparities.¹² Medical centers now have an opportunity to strengthen, not eliminate, existing services such as education and community outreach programs that support vulnerable patients to improve trust among patients and improved downstream health outcomes even with recent financial losses in mind.

Medical centers nationwide will need payment reform that provides greater financial stability beyond the pandemic to support high-value care delivery. They also will need flexibility to invest in prevention and to deliver the appropriate intensity of care to meet patients’ and communities’ needs.^15-17 Options to provide this support include prospective population-based payments that may create more resilience in protecting access to care when it is most needed. Models can include fully capitated payment for physician practices.^19,20 For example, after Vermont entered a single accountable care organization (ACO) model with the Centers for Medicare & Medicaid Services (CMS) in 2018, they not only generated a $97 million Medicaid savings, but also had a financial cushion that was later used in their COVID-19 response.^21,22 The advanced payments allowed primary care practices and community agencies to invest in a digital tool to support outreach to patient at high risk for virus complications.

Hospitals similarly can adapt global budgets that incentivize financial stewardship by encouraging clinicians to resume necessary services and not unnecessary ones.¹⁶ For example, CMS partnered with Pennsylvania’s Department of Health to provide prospective all-payer global budgets for rural hospitals and Maryland’s Health Services Cost Review Commission that negotiates a budget with each hospital. During the COVID-19 pandemic, hospitals in these programs have had more financial protection from fluctuating finances by allowing for easier shifts in service delivery location and adjustments in rates to compensate for declines in visits and procedures.²³

Policy makers and payers also can hold medical centers accountable to evidence-based guidelines and appropriate use of care, especially when necessary but expensive (eg, percutaneous coronary interventions, spinal surgeries, or cancer care). Funding agencies, additionally, can support these efforts by focusing on research, dissemination, and reliable implementation of these practices.

The COVID-19 crisis presents a tremendous opportunity for each medical center to revitalize healthcare. This opportunity can be seized only with reform by policy makers, payers, and regulatory agencies who encourage restarting high-value care without low-value services. We must take deliberate action so the nation’s medical centers can better meet patients’ needs to make healthcare more resilient, efficient, and fair.

The day after Memorial Day 2020 marked an important transition in the United States’ experience with coronavirus disease 2019 (COVID-19), with many states making initial plans to reopen. Alongside this reopening process, the US healthcare system needed to reopen to provide needed care to communities. This reopening, however, was in the context of several months of staggering financial losses for many medical centers that expected a larger surge than occurred locally and lost profit because of delayed elective procedures, all amid a national economic recession. Each medical center also faced a persistent risk of infection and a call for social equity as each one decided how to reopen. These decisions balanced the risks of reopening from COVID-19 exposure with patients’ medical needs and the healthcare industry’s financial needs.

This year’s widespread healthcare closures were necessary to reduce COVID-19 transmission and prepare for a future patient surge, but these closures had unintended consequences. Nearly half of adults polled said they or someone in their household had foregone or delayed care since the outbreak began.¹ This was especially true for visits to emergency departments and doctors’ offices for strokes, heart attacks, and routine medical care.² In a survey across 49 states, only 7% of primary care practices considered scheduling preventive visits as a high priority.³ Eleven percent of polled adults reported delaying care worsened their condition,¹ and in hard-hit areas such as New York City, non-COVID mortality was 22% higher than expected.⁴

Avoidance of the medical system decreased not only use of necessary, high-value care but also use of low-value care. Low-value services are those in which the “potential for harms exceed the potential benefits,”⁵ such as unnecessary hospitalizations, avoidable emergency department or clinic visits, unwarranted or excessive diagnostic testing (eg, annual physicals), and certain procedures (eg, spinal fusion surgery for low-back pain). Low-value care is costly, with $75.7 to $101.2 billion of the gross domestic product (GDP) spent on overuse.⁶ This care risks contributing to financial and, in turn, clinical harm for patients because the average health plan deductible exceeds a typical family’s available savings⁷and 25% of Americans say they have foregone treatment for a serious medical condition in the past year because of these costs.⁸ Medical centers’ significant financial losses are a sobering reminder of how much our system relies on fee-for-service billing that encourages high-margin profitable services regardless of necessity.⁹ We must avoid quick reactions of increasing these procedures to respond to the sudden financial loses.Medical centers across the country are choosing how to “reboot”—either deliberately changing how services are organized and delivered or returning to prior practices. Medical centers are facing potential for their own Renaissance in transitioning their organizations to modern healthcare delivery. In the 15th century ad, after experiencing the bubonic plague, Europe similarly transitioned toward modernity and great social change. Through the initial pandemic wave, we learned that even the largest health system could change their practices rapidly. COVID-19 achieved in 8 weeks what years of research, policy initiatives (eg, Choosing Wisely®, RightCare, Less Is More), and emphasizing value in reimbursement could not: stopping the delivery of a wide range of low-value services. We share three lessons learned from medical centers that have begun reopening services that can help us to better ensure higher-value, more affordable care that meets patients’ needs.

Medical centers can better focus on high-value care by defining their high-risk patient populations; high-value treatments, procedures, and preventive care; and phases of reopening. During the first pandemic wave, medical centers tried to reassure patients about emergency care, such as coming in for chest pain or neurologic symptoms, through personal outreach and media campaigns. Outpatient virtual visits also continued, including primary care, specialty services, mental health treatment, and physical therapy. While reopening, some medical centers have assessed disparities by relying on their data analytics and, if available, embedded health services researchers to understand what care was stopped and what populations were most affected.

The University of California health systems, for example, had a learning collaborative focused on sharing methods to restore care delivery that prioritizes patient needs. Some campuses conducted analyses using both electronic health record data and input from patients and their care teams to identify clinical needs and determine patient outreach plans. Some approaches used machine learning models to identify patients at highest risk of hospitalization or emergency department visits over the next 12 months and to conduct additional outreach to schedule these patients in primary and specialty care if clinically appropriate. Similarly, surgical specialties identified the highest-priority nonemergent surgeries for scheduling, including cancer resection, radiation therapy, and pain-management procedures. Similar guidance toward the most meaningful care has been prioritized within the United States Department of Veterans Affairs.

The rapid deployment of telehealth and payment models that reimburse video and in-clinic visits equally created new opportunities for medical centers to expand high-value care in lower-cost home settings. Similarly, new infrastructure is being developed to help define smarter use of virtual visits and home-based lab collection and monitoring.

Medical centers also must pay careful attention to redeploying service capacity for underused, high-value services. The pandemic uncovered existing staff that could be redeployed to support these changes. For example, with an “all hands on deck” mentality during the pandemic, in some medical centers, analysts or care managers from less-prioritized or duplicative areas were reassigned to vital COVID-19 efforts. Medical centers may realize that this staff can provide more value in the future by supporting increased high-value, affordable healthcare.

During the intial wave of the pandemic, medical centers greatly reduced the care they provided, often focusing on delivering essential care. This preparation for a surge of COVID patients had the effect of halting many unnecessary services by moving care from the clinic to home under new reimbursement changes, such as those affecting telehealth payments. The experience of reducing low-value medical services and visits can be extended to limiting unnecessary diagnostic testing. Medical centers could, for example, focus only on tests that advance care plans; reduce unnecessary blood draws, procedures, and vital sign checks on stable patients; shift to medications with less-frequent dosing intervals; and consolidate visits by treatment teams.^10,11

Medical centers, however, now face continued pressures to increase revenue because 75% report their organization’s top priority is focused on increasing patient volume.¹²Nearly 95% of healthcare payments have been based on fee-for-service models,¹³ and the COVID-19 pandemic highlighted the financial vulnerability of our health system when we reduce in-person care, especially among rural medical centers who often have no financial reserve.¹⁴ Similarly, nearly half of hospitals’ revenue comes from surgical admissions, though not all of these are necessary.^15-18 The fiscal realities facing medical centers make it challenging to not simply “ramp up” all service, regardless of necessity, in the context of payment models dependent on fee for service, which are present in most areas of the country.

As medical centers reboot, operational and clinical leaders must proactively view changes through an equity lens to avoid exacerbating health disparities among vulnerable populations. The pandemic has focused national attention on the severity and pervasiveness of disparities and created an imperative for substantive action to evaluate how every decision will affect health equity. For example, medical centers are expanding use of telehealth to improve patient outreach. However, in a survey of primary care physicians, 72% said they have patients who are unable to access telehealth because they do not have access to technology.³ Exclusion of these patients from programs risks worsening health disparities. In a recent survey, nearly 65% of medical centers report reexamining existing policies, protocols, and practices for patients at risk of disparities.¹² Medical centers now have an opportunity to strengthen, not eliminate, existing services such as education and community outreach programs that support vulnerable patients to improve trust among patients and improved downstream health outcomes even with recent financial losses in mind.

Medical centers nationwide will need payment reform that provides greater financial stability beyond the pandemic to support high-value care delivery. They also will need flexibility to invest in prevention and to deliver the appropriate intensity of care to meet patients’ and communities’ needs.^15-17 Options to provide this support include prospective population-based payments that may create more resilience in protecting access to care when it is most needed. Models can include fully capitated payment for physician practices.^19,20 For example, after Vermont entered a single accountable care organization (ACO) model with the Centers for Medicare & Medicaid Services (CMS) in 2018, they not only generated a $97 million Medicaid savings, but also had a financial cushion that was later used in their COVID-19 response.^21,22 The advanced payments allowed primary care practices and community agencies to invest in a digital tool to support outreach to patient at high risk for virus complications.

Hospitals similarly can adapt global budgets that incentivize financial stewardship by encouraging clinicians to resume necessary services and not unnecessary ones.¹⁶ For example, CMS partnered with Pennsylvania’s Department of Health to provide prospective all-payer global budgets for rural hospitals and Maryland’s Health Services Cost Review Commission that negotiates a budget with each hospital. During the COVID-19 pandemic, hospitals in these programs have had more financial protection from fluctuating finances by allowing for easier shifts in service delivery location and adjustments in rates to compensate for declines in visits and procedures.²³

Policy makers and payers also can hold medical centers accountable to evidence-based guidelines and appropriate use of care, especially when necessary but expensive (eg, percutaneous coronary interventions, spinal surgeries, or cancer care). Funding agencies, additionally, can support these efforts by focusing on research, dissemination, and reliable implementation of these practices.

The COVID-19 crisis presents a tremendous opportunity for each medical center to revitalize healthcare. This opportunity can be seized only with reform by policy makers, payers, and regulatory agencies who encourage restarting high-value care without low-value services. We must take deliberate action so the nation’s medical centers can better meet patients’ needs to make healthcare more resilient, efficient, and fair.

The day after Memorial Day 2020 marked an important transition in the United States’ experience with coronavirus disease 2019 (COVID-19), with many states making initial plans to reopen. Alongside this reopening process, the US healthcare system needed to reopen to provide needed care to communities. This reopening, however, was in the context of several months of staggering financial losses for many medical centers that expected a larger surge than occurred locally and lost profit because of delayed elective procedures, all amid a national economic recession. Each medical center also faced a persistent risk of infection and a call for social equity as each one decided how to reopen. These decisions balanced the risks of reopening from COVID-19 exposure with patients’ medical needs and the healthcare industry’s financial needs.

This year’s widespread healthcare closures were necessary to reduce COVID-19 transmission and prepare for a future patient surge, but these closures had unintended consequences. Nearly half of adults polled said they or someone in their household had foregone or delayed care since the outbreak began.¹ This was especially true for visits to emergency departments and doctors’ offices for strokes, heart attacks, and routine medical care.² In a survey across 49 states, only 7% of primary care practices considered scheduling preventive visits as a high priority.³ Eleven percent of polled adults reported delaying care worsened their condition,¹ and in hard-hit areas such as New York City, non-COVID mortality was 22% higher than expected.⁴

Avoidance of the medical system decreased not only use of necessary, high-value care but also use of low-value care. Low-value services are those in which the “potential for harms exceed the potential benefits,”⁵ such as unnecessary hospitalizations, avoidable emergency department or clinic visits, unwarranted or excessive diagnostic testing (eg, annual physicals), and certain procedures (eg, spinal fusion surgery for low-back pain). Low-value care is costly, with $75.7 to $101.2 billion of the gross domestic product (GDP) spent on overuse.⁶ This care risks contributing to financial and, in turn, clinical harm for patients because the average health plan deductible exceeds a typical family’s available savings⁷and 25% of Americans say they have foregone treatment for a serious medical condition in the past year because of these costs.⁸ Medical centers’ significant financial losses are a sobering reminder of how much our system relies on fee-for-service billing that encourages high-margin profitable services regardless of necessity.⁹ We must avoid quick reactions of increasing these procedures to respond to the sudden financial loses.Medical centers across the country are choosing how to “reboot”—either deliberately changing how services are organized and delivered or returning to prior practices. Medical centers are facing potential for their own Renaissance in transitioning their organizations to modern healthcare delivery. In the 15th century ad, after experiencing the bubonic plague, Europe similarly transitioned toward modernity and great social change. Through the initial pandemic wave, we learned that even the largest health system could change their practices rapidly. COVID-19 achieved in 8 weeks what years of research, policy initiatives (eg, Choosing Wisely®, RightCare, Less Is More), and emphasizing value in reimbursement could not: stopping the delivery of a wide range of low-value services. We share three lessons learned from medical centers that have begun reopening services that can help us to better ensure higher-value, more affordable care that meets patients’ needs.

Medical centers can better focus on high-value care by defining their high-risk patient populations; high-value treatments, procedures, and preventive care; and phases of reopening. During the first pandemic wave, medical centers tried to reassure patients about emergency care, such as coming in for chest pain or neurologic symptoms, through personal outreach and media campaigns. Outpatient virtual visits also continued, including primary care, specialty services, mental health treatment, and physical therapy. While reopening, some medical centers have assessed disparities by relying on their data analytics and, if available, embedded health services researchers to understand what care was stopped and what populations were most affected.

The University of California health systems, for example, had a learning collaborative focused on sharing methods to restore care delivery that prioritizes patient needs. Some campuses conducted analyses using both electronic health record data and input from patients and their care teams to identify clinical needs and determine patient outreach plans. Some approaches used machine learning models to identify patients at highest risk of hospitalization or emergency department visits over the next 12 months and to conduct additional outreach to schedule these patients in primary and specialty care if clinically appropriate. Similarly, surgical specialties identified the highest-priority nonemergent surgeries for scheduling, including cancer resection, radiation therapy, and pain-management procedures. Similar guidance toward the most meaningful care has been prioritized within the United States Department of Veterans Affairs.

The rapid deployment of telehealth and payment models that reimburse video and in-clinic visits equally created new opportunities for medical centers to expand high-value care in lower-cost home settings. Similarly, new infrastructure is being developed to help define smarter use of virtual visits and home-based lab collection and monitoring.

Medical centers also must pay careful attention to redeploying service capacity for underused, high-value services. The pandemic uncovered existing staff that could be redeployed to support these changes. For example, with an “all hands on deck” mentality during the pandemic, in some medical centers, analysts or care managers from less-prioritized or duplicative areas were reassigned to vital COVID-19 efforts. Medical centers may realize that this staff can provide more value in the future by supporting increased high-value, affordable healthcare.

During the intial wave of the pandemic, medical centers greatly reduced the care they provided, often focusing on delivering essential care. This preparation for a surge of COVID patients had the effect of halting many unnecessary services by moving care from the clinic to home under new reimbursement changes, such as those affecting telehealth payments. The experience of reducing low-value medical services and visits can be extended to limiting unnecessary diagnostic testing. Medical centers could, for example, focus only on tests that advance care plans; reduce unnecessary blood draws, procedures, and vital sign checks on stable patients; shift to medications with less-frequent dosing intervals; and consolidate visits by treatment teams.^10,11

Medical centers, however, now face continued pressures to increase revenue because 75% report their organization’s top priority is focused on increasing patient volume.¹²Nearly 95% of healthcare payments have been based on fee-for-service models,¹³ and the COVID-19 pandemic highlighted the financial vulnerability of our health system when we reduce in-person care, especially among rural medical centers who often have no financial reserve.¹⁴ Similarly, nearly half of hospitals’ revenue comes from surgical admissions, though not all of these are necessary.^15-18 The fiscal realities facing medical centers make it challenging to not simply “ramp up” all service, regardless of necessity, in the context of payment models dependent on fee for service, which are present in most areas of the country.

As medical centers reboot, operational and clinical leaders must proactively view changes through an equity lens to avoid exacerbating health disparities among vulnerable populations. The pandemic has focused national attention on the severity and pervasiveness of disparities and created an imperative for substantive action to evaluate how every decision will affect health equity. For example, medical centers are expanding use of telehealth to improve patient outreach. However, in a survey of primary care physicians, 72% said they have patients who are unable to access telehealth because they do not have access to technology.³ Exclusion of these patients from programs risks worsening health disparities. In a recent survey, nearly 65% of medical centers report reexamining existing policies, protocols, and practices for patients at risk of disparities.¹² Medical centers now have an opportunity to strengthen, not eliminate, existing services such as education and community outreach programs that support vulnerable patients to improve trust among patients and improved downstream health outcomes even with recent financial losses in mind.

Medical centers nationwide will need payment reform that provides greater financial stability beyond the pandemic to support high-value care delivery. They also will need flexibility to invest in prevention and to deliver the appropriate intensity of care to meet patients’ and communities’ needs.^15-17 Options to provide this support include prospective population-based payments that may create more resilience in protecting access to care when it is most needed. Models can include fully capitated payment for physician practices.^19,20 For example, after Vermont entered a single accountable care organization (ACO) model with the Centers for Medicare & Medicaid Services (CMS) in 2018, they not only generated a $97 million Medicaid savings, but also had a financial cushion that was later used in their COVID-19 response.^21,22 The advanced payments allowed primary care practices and community agencies to invest in a digital tool to support outreach to patient at high risk for virus complications.

Hospitals similarly can adapt global budgets that incentivize financial stewardship by encouraging clinicians to resume necessary services and not unnecessary ones.¹⁶ For example, CMS partnered with Pennsylvania’s Department of Health to provide prospective all-payer global budgets for rural hospitals and Maryland’s Health Services Cost Review Commission that negotiates a budget with each hospital. During the COVID-19 pandemic, hospitals in these programs have had more financial protection from fluctuating finances by allowing for easier shifts in service delivery location and adjustments in rates to compensate for declines in visits and procedures.²³

Policy makers and payers also can hold medical centers accountable to evidence-based guidelines and appropriate use of care, especially when necessary but expensive (eg, percutaneous coronary interventions, spinal surgeries, or cancer care). Funding agencies, additionally, can support these efforts by focusing on research, dissemination, and reliable implementation of these practices.

The COVID-19 crisis presents a tremendous opportunity for each medical center to revitalize healthcare. This opportunity can be seized only with reform by policy makers, payers, and regulatory agencies who encourage restarting high-value care without low-value services. We must take deliberate action so the nation’s medical centers can better meet patients’ needs to make healthcare more resilient, efficient, and fair.

Professional upheavals caused by the coronavirus disease 2019 (COVID-19) pandemic have affected the academic productivity of many physicians. This is due in part to rapid changes in clinical care and medical education: physician-researchers have been redeployed to frontline clinical care; clinician-educators have been forced to rapidly transition in-person curricula to virtual platforms; and primary care physicians and subspecialists have been forced to transition to telehealth-based practices. In addition to these changes in clinical and educational responsibilities, the COVID-19 pandemic has substantially altered the personal lives of physicians. During the height of the pandemic, clinicians simultaneously wrestled with a lack of available childcare, unexpected home-schooling responsibilities, decreased income, and many other COVID-19-related stresses.¹ Additionally, the ever-present “second pandemic” of structural racism, persistent health disparities, and racial inequity has further increased the personal and professional demands facing academic faculty.²

In particular, the pandemic has placed personal and professional pressure on female and minority faculty members. In spite of these pressures, however, the academic promotions process still requires rigid accounting of scholarly productivity. As the focus of academic practices has shifted to support clinical care during the pandemic, scholarly productivity has suffered for clinicians on the frontline. As a result, academic clinical faculty have expressed significant stress and concerns about failing to meet benchmarks for promotion (eg, publications, curricula development, national presentations). To counter these shifts (and the inherent inequity that they create for female clinicians and for men and women who are Black, Indigenous, and/or of color), academic institutions should not only recognize the effects the COVID-19 pandemic has had on faculty, but also adopt immediate solutions to more equitably account for such disruptions to academic portfolios. In this paper, we explore populations whose career trajectories are most at-risk and propose a framework to capture novel and nontraditional contributions while also acknowledging the rapid changes the COVID-19 pandemic has brought to academic medicine.

Even before the COVID-19 pandemic, physician mothers, underrepresented racial/ethnic minority groups, and junior faculty were most at-risk for career disruptions. The closure of daycare facilities and schools and shift to online learning resulting from the pandemic, along with the common challenges of parenting, have taken a significant toll on the lives of working parents. Because women tend to carry a disproportionate share of childcare and household responsibilities, these changes have inequitably leveraged themselves as a “mommy tax” on working women.^3,4

As underrepresented medicine faculty (particularly Black, Hispanic, Latino, and Native American clinicians) comprise only 8% of the academic medical workforce,they currently face a variety of personal and professional challenges.⁵ This is especially true for Black and Latinx physicians who have been experiencing an increased COVID-19 burden in their communities, while concurrently fighting entrenched structural racism and police violence. In academia, these challenges have worsened because of the “minority tax”—the toll of often uncompensated extra responsibilities (time or money) placed on minority faculty in the name of achieving diversity. The unintended consequences of these responsibilities result in having fewer mentors,⁶ caring for underserved populations,⁷ and performing more clinical care⁸ than non-underrepresented minority faculty. Because minority faculty are unlikely to be in leadership positions, it is reasonable to conclude they have been shouldering heavier clinical obligations and facing greater career disruption of scholarly work due to the COVID-19 pandemic.

Junior faculty (eg, instructors and assistant professors) also remain professionally vulnerable during the COVID-19 pandemic. Because junior faculty are often more clinically focused and less likely to hold leadership positions than senior faculty, they are more likely to have assumed frontline clinical positions, which come at the expense of academic work. Junior faculty are also at a critical building phase in their academic career—a time when they benefit from the opportunity to share their scholarly work and network at conferences. Unfortunately, many conferences have been canceled or moved to a virtual platform. Given that some institutions may be freezing academic funding for conferences due to budgetary shortfalls from the pandemic, junior faculty may be particularly at risk if they are not able to present their work. In addition, junior faculty often face disproportionate struggles at home, trying to balance demands of work and caring for young children. Considering the unique needs of each of these groups, it is especially important to consider intersectionality, or the compounded issues for individuals who exist in multiple disproportionately affected groups (eg, a Black female junior faculty member who is also a mother).

The typical format of a professional curriculum vitae (CV) at most academic institutions does not allow one to document potential disruptions or novel contributions, including those that occurred during the COVID-19 pandemic. As a group of academic clinicians, educators, and researchers whose careers have been affected by the pandemic, we created a COVID-19 CV matrix, a potential framework to serve as a supplement for faculty. In this matrix, faculty members may document their contributions, disruptions that affected their work, and caregiving responsibilities during this time period, while also providing a rubric for promotions and tenure committees to equitably evaluate the pandemic period on an academic CV. Our COVID-19 CV matrix consists of six domains: (1) clinical care, (2) research, (3) education, (4) service, (5) advocacy/media, and (6) social media. These domains encompass traditional and nontraditional contributions made by healthcare professionals during the pandemic (Table). This matrix broadens the ability of both faculty and institutions to determine the actual impact of individuals during the pandemic.

ACCOUNT FOR YOUR (NEW) IMPACT

Throughout the COVID-19 pandemic, academic faculty have been innovative, contributing in novel ways not routinely captured by promotions committees—eg, the digital health researcher who now directs the telemedicine response for their institution and the health disparities researcher who now leads daily webinar sessions on structural racism to medical students. Other novel contributions include advancing COVID-19 innovations and engaging in media and community advocacy (eg, organizing large-scale donations of equipment and funds to support organizations in need). While such nontraditional contributions may not have been readily captured or thought “CV worthy” in the past, faculty should now account for them. More importantly, promotions committees need to recognize that these pivots or alterations in career paths are not signals of professional failure, but rather evidence of a shifting landscape and the respective response of the individual. Furthermore, because these pivots often help fulfill an institutional mission, they are impactful.

ACKNOWLEDGE THE DISRUPTION

It is important for promotions and tenure committees to recognize the impact and disruption COVID-19 has had on traditional academic work, acknowledging the time and energy required for a faculty member to make needed work adjustments. This enables a leader to better assess how a faculty member’s academic portfolio has been affected. For example, researchers have had to halt studies, medical educators have had to redevelop and transition curricula to virtual platforms, and physicians have had to discontinue clinician quality improvement initiatives due to competing hospital priorities. Faculty members who document such unintentional alterations in their academic career path can explain to their institution how they have continued to positively influence their field and the community during the pandemic. This approach is analogous to the current model of accounting for clinical time when judging faculty members’ contributions in scholarly achievement.

The COVID-19 CV matrix has the potential to be annotated to explain the burden of one’s personal situation, which is often “invisible” in the professional environment. For example, many physicians have had to assume additional childcare responsibilities, tend to sick family members, friends, and even themselves. It is also possible that a faculty member has a partner who is also an essential worker, one who had to self-isolate due to COVID-19 exposure or illness, or who has been working overtime due to high patient volumes.

INSTITUTIONAL RESPONSE

How can institutions respond to the altered academic landscape caused by the COVID-19 pandemic? Promotions committees typically have two main tools at their disposal: adjusting the tenure clock or the benchmarks. Extending the period of time available to qualify for tenure is commonplace in the “publish-or-perish” academic tracks of university research professors. Clock adjustments are typically granted to faculty following the birth of a child or for other specific family- or health-related hardships, in accordance with the Family and Medical Leave Act. Unfortunately, tenure-clock extensions for female faculty members can exacerbate gender inequity: Data on tenure-clock extensions show a higher rate of tenure granted to male faculty compared to female faculty.⁹ For this reason, it is also important to explore adjustments or modifications to benchmark criteria. This could be accomplished by broadening the criteria for promotion, recognizing that impact occurs in many forms, thereby enabling meeting a benchmark. It can also occur by examining the trajectory of an individual within a promotion pathway before it was disrupted to determine impact. To avoid exacerbating social and gender inequities within academia, institutions should use these professional levers and create new ones to provide parity and equality across the promotional playing field. While the CV matrix openly acknowledges the disruptions and tangents the COVID-19 pandemic has had on academic careers, it remains important for academic institutions to recognize these disruptions and innovate the manner in which they acknowledge scholarly contributions.

While academic rigidity and known social taxes (minority and mommy taxes) are particularly problematic in the current climate, these issues have always been at play in evaluating academic success. Improved documentation of novel contributions, disruptions, caregiving, and other challenges can enable more holistic and timely professional advancement for all faculty, regardless of their sex, race, ethnicity, or social background. Ultimately, we hope this framework initiates further conversations among academic institutions on how to define productivity in an age where journal impact factor or number of publications is not the fullest measure of one’s impact in their field.

Professional upheavals caused by the coronavirus disease 2019 (COVID-19) pandemic have affected the academic productivity of many physicians. This is due in part to rapid changes in clinical care and medical education: physician-researchers have been redeployed to frontline clinical care; clinician-educators have been forced to rapidly transition in-person curricula to virtual platforms; and primary care physicians and subspecialists have been forced to transition to telehealth-based practices. In addition to these changes in clinical and educational responsibilities, the COVID-19 pandemic has substantially altered the personal lives of physicians. During the height of the pandemic, clinicians simultaneously wrestled with a lack of available childcare, unexpected home-schooling responsibilities, decreased income, and many other COVID-19-related stresses.¹ Additionally, the ever-present “second pandemic” of structural racism, persistent health disparities, and racial inequity has further increased the personal and professional demands facing academic faculty.²

In particular, the pandemic has placed personal and professional pressure on female and minority faculty members. In spite of these pressures, however, the academic promotions process still requires rigid accounting of scholarly productivity. As the focus of academic practices has shifted to support clinical care during the pandemic, scholarly productivity has suffered for clinicians on the frontline. As a result, academic clinical faculty have expressed significant stress and concerns about failing to meet benchmarks for promotion (eg, publications, curricula development, national presentations). To counter these shifts (and the inherent inequity that they create for female clinicians and for men and women who are Black, Indigenous, and/or of color), academic institutions should not only recognize the effects the COVID-19 pandemic has had on faculty, but also adopt immediate solutions to more equitably account for such disruptions to academic portfolios. In this paper, we explore populations whose career trajectories are most at-risk and propose a framework to capture novel and nontraditional contributions while also acknowledging the rapid changes the COVID-19 pandemic has brought to academic medicine.

Even before the COVID-19 pandemic, physician mothers, underrepresented racial/ethnic minority groups, and junior faculty were most at-risk for career disruptions. The closure of daycare facilities and schools and shift to online learning resulting from the pandemic, along with the common challenges of parenting, have taken a significant toll on the lives of working parents. Because women tend to carry a disproportionate share of childcare and household responsibilities, these changes have inequitably leveraged themselves as a “mommy tax” on working women.^3,4

As underrepresented medicine faculty (particularly Black, Hispanic, Latino, and Native American clinicians) comprise only 8% of the academic medical workforce,they currently face a variety of personal and professional challenges.⁵ This is especially true for Black and Latinx physicians who have been experiencing an increased COVID-19 burden in their communities, while concurrently fighting entrenched structural racism and police violence. In academia, these challenges have worsened because of the “minority tax”—the toll of often uncompensated extra responsibilities (time or money) placed on minority faculty in the name of achieving diversity. The unintended consequences of these responsibilities result in having fewer mentors,⁶ caring for underserved populations,⁷ and performing more clinical care⁸ than non-underrepresented minority faculty. Because minority faculty are unlikely to be in leadership positions, it is reasonable to conclude they have been shouldering heavier clinical obligations and facing greater career disruption of scholarly work due to the COVID-19 pandemic.

Junior faculty (eg, instructors and assistant professors) also remain professionally vulnerable during the COVID-19 pandemic. Because junior faculty are often more clinically focused and less likely to hold leadership positions than senior faculty, they are more likely to have assumed frontline clinical positions, which come at the expense of academic work. Junior faculty are also at a critical building phase in their academic career—a time when they benefit from the opportunity to share their scholarly work and network at conferences. Unfortunately, many conferences have been canceled or moved to a virtual platform. Given that some institutions may be freezing academic funding for conferences due to budgetary shortfalls from the pandemic, junior faculty may be particularly at risk if they are not able to present their work. In addition, junior faculty often face disproportionate struggles at home, trying to balance demands of work and caring for young children. Considering the unique needs of each of these groups, it is especially important to consider intersectionality, or the compounded issues for individuals who exist in multiple disproportionately affected groups (eg, a Black female junior faculty member who is also a mother).

The typical format of a professional curriculum vitae (CV) at most academic institutions does not allow one to document potential disruptions or novel contributions, including those that occurred during the COVID-19 pandemic. As a group of academic clinicians, educators, and researchers whose careers have been affected by the pandemic, we created a COVID-19 CV matrix, a potential framework to serve as a supplement for faculty. In this matrix, faculty members may document their contributions, disruptions that affected their work, and caregiving responsibilities during this time period, while also providing a rubric for promotions and tenure committees to equitably evaluate the pandemic period on an academic CV. Our COVID-19 CV matrix consists of six domains: (1) clinical care, (2) research, (3) education, (4) service, (5) advocacy/media, and (6) social media. These domains encompass traditional and nontraditional contributions made by healthcare professionals during the pandemic (Table). This matrix broadens the ability of both faculty and institutions to determine the actual impact of individuals during the pandemic.

ACCOUNT FOR YOUR (NEW) IMPACT

Throughout the COVID-19 pandemic, academic faculty have been innovative, contributing in novel ways not routinely captured by promotions committees—eg, the digital health researcher who now directs the telemedicine response for their institution and the health disparities researcher who now leads daily webinar sessions on structural racism to medical students. Other novel contributions include advancing COVID-19 innovations and engaging in media and community advocacy (eg, organizing large-scale donations of equipment and funds to support organizations in need). While such nontraditional contributions may not have been readily captured or thought “CV worthy” in the past, faculty should now account for them. More importantly, promotions committees need to recognize that these pivots or alterations in career paths are not signals of professional failure, but rather evidence of a shifting landscape and the respective response of the individual. Furthermore, because these pivots often help fulfill an institutional mission, they are impactful.

ACKNOWLEDGE THE DISRUPTION

It is important for promotions and tenure committees to recognize the impact and disruption COVID-19 has had on traditional academic work, acknowledging the time and energy required for a faculty member to make needed work adjustments. This enables a leader to better assess how a faculty member’s academic portfolio has been affected. For example, researchers have had to halt studies, medical educators have had to redevelop and transition curricula to virtual platforms, and physicians have had to discontinue clinician quality improvement initiatives due to competing hospital priorities. Faculty members who document such unintentional alterations in their academic career path can explain to their institution how they have continued to positively influence their field and the community during the pandemic. This approach is analogous to the current model of accounting for clinical time when judging faculty members’ contributions in scholarly achievement.

The COVID-19 CV matrix has the potential to be annotated to explain the burden of one’s personal situation, which is often “invisible” in the professional environment. For example, many physicians have had to assume additional childcare responsibilities, tend to sick family members, friends, and even themselves. It is also possible that a faculty member has a partner who is also an essential worker, one who had to self-isolate due to COVID-19 exposure or illness, or who has been working overtime due to high patient volumes.

INSTITUTIONAL RESPONSE

How can institutions respond to the altered academic landscape caused by the COVID-19 pandemic? Promotions committees typically have two main tools at their disposal: adjusting the tenure clock or the benchmarks. Extending the period of time available to qualify for tenure is commonplace in the “publish-or-perish” academic tracks of university research professors. Clock adjustments are typically granted to faculty following the birth of a child or for other specific family- or health-related hardships, in accordance with the Family and Medical Leave Act. Unfortunately, tenure-clock extensions for female faculty members can exacerbate gender inequity: Data on tenure-clock extensions show a higher rate of tenure granted to male faculty compared to female faculty.⁹ For this reason, it is also important to explore adjustments or modifications to benchmark criteria. This could be accomplished by broadening the criteria for promotion, recognizing that impact occurs in many forms, thereby enabling meeting a benchmark. It can also occur by examining the trajectory of an individual within a promotion pathway before it was disrupted to determine impact. To avoid exacerbating social and gender inequities within academia, institutions should use these professional levers and create new ones to provide parity and equality across the promotional playing field. While the CV matrix openly acknowledges the disruptions and tangents the COVID-19 pandemic has had on academic careers, it remains important for academic institutions to recognize these disruptions and innovate the manner in which they acknowledge scholarly contributions.

While academic rigidity and known social taxes (minority and mommy taxes) are particularly problematic in the current climate, these issues have always been at play in evaluating academic success. Improved documentation of novel contributions, disruptions, caregiving, and other challenges can enable more holistic and timely professional advancement for all faculty, regardless of their sex, race, ethnicity, or social background. Ultimately, we hope this framework initiates further conversations among academic institutions on how to define productivity in an age where journal impact factor or number of publications is not the fullest measure of one’s impact in their field.

Professional upheavals caused by the coronavirus disease 2019 (COVID-19) pandemic have affected the academic productivity of many physicians. This is due in part to rapid changes in clinical care and medical education: physician-researchers have been redeployed to frontline clinical care; clinician-educators have been forced to rapidly transition in-person curricula to virtual platforms; and primary care physicians and subspecialists have been forced to transition to telehealth-based practices. In addition to these changes in clinical and educational responsibilities, the COVID-19 pandemic has substantially altered the personal lives of physicians. During the height of the pandemic, clinicians simultaneously wrestled with a lack of available childcare, unexpected home-schooling responsibilities, decreased income, and many other COVID-19-related stresses.¹ Additionally, the ever-present “second pandemic” of structural racism, persistent health disparities, and racial inequity has further increased the personal and professional demands facing academic faculty.²

In particular, the pandemic has placed personal and professional pressure on female and minority faculty members. In spite of these pressures, however, the academic promotions process still requires rigid accounting of scholarly productivity. As the focus of academic practices has shifted to support clinical care during the pandemic, scholarly productivity has suffered for clinicians on the frontline. As a result, academic clinical faculty have expressed significant stress and concerns about failing to meet benchmarks for promotion (eg, publications, curricula development, national presentations). To counter these shifts (and the inherent inequity that they create for female clinicians and for men and women who are Black, Indigenous, and/or of color), academic institutions should not only recognize the effects the COVID-19 pandemic has had on faculty, but also adopt immediate solutions to more equitably account for such disruptions to academic portfolios. In this paper, we explore populations whose career trajectories are most at-risk and propose a framework to capture novel and nontraditional contributions while also acknowledging the rapid changes the COVID-19 pandemic has brought to academic medicine.

Even before the COVID-19 pandemic, physician mothers, underrepresented racial/ethnic minority groups, and junior faculty were most at-risk for career disruptions. The closure of daycare facilities and schools and shift to online learning resulting from the pandemic, along with the common challenges of parenting, have taken a significant toll on the lives of working parents. Because women tend to carry a disproportionate share of childcare and household responsibilities, these changes have inequitably leveraged themselves as a “mommy tax” on working women.^3,4

As underrepresented medicine faculty (particularly Black, Hispanic, Latino, and Native American clinicians) comprise only 8% of the academic medical workforce,they currently face a variety of personal and professional challenges.⁵ This is especially true for Black and Latinx physicians who have been experiencing an increased COVID-19 burden in their communities, while concurrently fighting entrenched structural racism and police violence. In academia, these challenges have worsened because of the “minority tax”—the toll of often uncompensated extra responsibilities (time or money) placed on minority faculty in the name of achieving diversity. The unintended consequences of these responsibilities result in having fewer mentors,⁶ caring for underserved populations,⁷ and performing more clinical care⁸ than non-underrepresented minority faculty. Because minority faculty are unlikely to be in leadership positions, it is reasonable to conclude they have been shouldering heavier clinical obligations and facing greater career disruption of scholarly work due to the COVID-19 pandemic.

Junior faculty (eg, instructors and assistant professors) also remain professionally vulnerable during the COVID-19 pandemic. Because junior faculty are often more clinically focused and less likely to hold leadership positions than senior faculty, they are more likely to have assumed frontline clinical positions, which come at the expense of academic work. Junior faculty are also at a critical building phase in their academic career—a time when they benefit from the opportunity to share their scholarly work and network at conferences. Unfortunately, many conferences have been canceled or moved to a virtual platform. Given that some institutions may be freezing academic funding for conferences due to budgetary shortfalls from the pandemic, junior faculty may be particularly at risk if they are not able to present their work. In addition, junior faculty often face disproportionate struggles at home, trying to balance demands of work and caring for young children. Considering the unique needs of each of these groups, it is especially important to consider intersectionality, or the compounded issues for individuals who exist in multiple disproportionately affected groups (eg, a Black female junior faculty member who is also a mother).

The typical format of a professional curriculum vitae (CV) at most academic institutions does not allow one to document potential disruptions or novel contributions, including those that occurred during the COVID-19 pandemic. As a group of academic clinicians, educators, and researchers whose careers have been affected by the pandemic, we created a COVID-19 CV matrix, a potential framework to serve as a supplement for faculty. In this matrix, faculty members may document their contributions, disruptions that affected their work, and caregiving responsibilities during this time period, while also providing a rubric for promotions and tenure committees to equitably evaluate the pandemic period on an academic CV. Our COVID-19 CV matrix consists of six domains: (1) clinical care, (2) research, (3) education, (4) service, (5) advocacy/media, and (6) social media. These domains encompass traditional and nontraditional contributions made by healthcare professionals during the pandemic (Table). This matrix broadens the ability of both faculty and institutions to determine the actual impact of individuals during the pandemic.

ACCOUNT FOR YOUR (NEW) IMPACT

Throughout the COVID-19 pandemic, academic faculty have been innovative, contributing in novel ways not routinely captured by promotions committees—eg, the digital health researcher who now directs the telemedicine response for their institution and the health disparities researcher who now leads daily webinar sessions on structural racism to medical students. Other novel contributions include advancing COVID-19 innovations and engaging in media and community advocacy (eg, organizing large-scale donations of equipment and funds to support organizations in need). While such nontraditional contributions may not have been readily captured or thought “CV worthy” in the past, faculty should now account for them. More importantly, promotions committees need to recognize that these pivots or alterations in career paths are not signals of professional failure, but rather evidence of a shifting landscape and the respective response of the individual. Furthermore, because these pivots often help fulfill an institutional mission, they are impactful.

ACKNOWLEDGE THE DISRUPTION

It is important for promotions and tenure committees to recognize the impact and disruption COVID-19 has had on traditional academic work, acknowledging the time and energy required for a faculty member to make needed work adjustments. This enables a leader to better assess how a faculty member’s academic portfolio has been affected. For example, researchers have had to halt studies, medical educators have had to redevelop and transition curricula to virtual platforms, and physicians have had to discontinue clinician quality improvement initiatives due to competing hospital priorities. Faculty members who document such unintentional alterations in their academic career path can explain to their institution how they have continued to positively influence their field and the community during the pandemic. This approach is analogous to the current model of accounting for clinical time when judging faculty members’ contributions in scholarly achievement.

The COVID-19 CV matrix has the potential to be annotated to explain the burden of one’s personal situation, which is often “invisible” in the professional environment. For example, many physicians have had to assume additional childcare responsibilities, tend to sick family members, friends, and even themselves. It is also possible that a faculty member has a partner who is also an essential worker, one who had to self-isolate due to COVID-19 exposure or illness, or who has been working overtime due to high patient volumes.

INSTITUTIONAL RESPONSE

How can institutions respond to the altered academic landscape caused by the COVID-19 pandemic? Promotions committees typically have two main tools at their disposal: adjusting the tenure clock or the benchmarks. Extending the period of time available to qualify for tenure is commonplace in the “publish-or-perish” academic tracks of university research professors. Clock adjustments are typically granted to faculty following the birth of a child or for other specific family- or health-related hardships, in accordance with the Family and Medical Leave Act. Unfortunately, tenure-clock extensions for female faculty members can exacerbate gender inequity: Data on tenure-clock extensions show a higher rate of tenure granted to male faculty compared to female faculty.⁹ For this reason, it is also important to explore adjustments or modifications to benchmark criteria. This could be accomplished by broadening the criteria for promotion, recognizing that impact occurs in many forms, thereby enabling meeting a benchmark. It can also occur by examining the trajectory of an individual within a promotion pathway before it was disrupted to determine impact. To avoid exacerbating social and gender inequities within academia, institutions should use these professional levers and create new ones to provide parity and equality across the promotional playing field. While the CV matrix openly acknowledges the disruptions and tangents the COVID-19 pandemic has had on academic careers, it remains important for academic institutions to recognize these disruptions and innovate the manner in which they acknowledge scholarly contributions.

While academic rigidity and known social taxes (minority and mommy taxes) are particularly problematic in the current climate, these issues have always been at play in evaluating academic success. Improved documentation of novel contributions, disruptions, caregiving, and other challenges can enable more holistic and timely professional advancement for all faculty, regardless of their sex, race, ethnicity, or social background. Ultimately, we hope this framework initiates further conversations among academic institutions on how to define productivity in an age where journal impact factor or number of publications is not the fullest measure of one’s impact in their field.

The COVID-19 pandemic is a crisis of mismatch between resources and infection burden. There is extraordinary heterogeneity across time and geography in the pandemic impact, with hospitals in New York City initially inundated while hospitals in major urban areas of California were comparatively quiet. Efforts to “flatten the curve” are intended to improve outcomes by reducing health system overload.¹ In the case of hospital-based care, health systems’ primary resources include emergency and critical care bed and staff capacity.

Prior work has documented wide variability in intensive care capacity across the United States and hypothesized that even moderate disease outbreaks could overwhelm hospital referral regions (HRRs).^2,3 Various simulations of outbreaks suggested that thousands of deaths are potentially preventable depending on the health system’s response,⁴ although the degree to which resource limitations have contributed to mortality during this COVID-19 pandemic has yet to be explored. The objective of this analysis was to examine the association between hospital resources and COVID-19 deaths amongst HRRs in the United States in the period from March 1 to July 26, 2020.

Data

This was an analysis of the American Hospital Association Annual Survey Database from 2017 and 2018, including hospital resource variables such as total hospital beds, hospitalists, intensive care beds, intensivists, emergency physicians, and nurses.⁵ The analysis was limited to general medical and surgical hospitals capable of providing acute care services, defined as those reporting at least 500 emergency department visits in 2018. Where data were missing on analysis variables (26.0% missing overall), the data were drawn from the 2017 survey results (reduced to 23.8% missing) from the same site as available, and the remainder were imputed with multivariate imputation by chained equations. An identical analysis without imputation was performed as a sensitivity analysis that showed a similar pattern of results. Total resources were tabulated amongst HRRs, and the hospital resources per COVID-19 case calculated. HRRs are a geographic category devised to represent regional health care markets, and each includes hospital sites performing major procedures.³ These were the focus of the analysis because they may represent a meaningful geographic division of hospital-based resources. COVID-19 case and death counts (as of July 26, 2020) were drawn from publicly available county-level data curated by the New York Times from state and local governments as well as health departments nationwide,⁶ separated by month (ie, March, April, May, June, and July). Data on New York City were available in aggregate (rather than separated by borough). Cases and deaths were therefore apportioned to the three HRRs involving New York City in proportion to that area’s population. To adjust for the lag between COVID-19 cases and deaths,^7,8 we offset deaths 2 weeks into the future so that the April COVID-19 death count for a given HRR included deaths that occurred for 1 month beginning 2 weeks after the start of April, and so on.

Analysis

We estimated Poisson distribution regressions for the outcome of COVID-19 death count in each HRR and month with one model for each of our six hospital-based resource variables. The offset (exposure) variable was COVID-19 case count. To adjust for the possibility of varying effects of hospital resources on deaths by month (ie, in anticipation that health systems might evolve in response to the pandemic over time), each model includes terms for the interaction between hospital-based resource and an indicator variable for month, as well as a fifth term for month. Standard errors were adjusted for clustering within HRR. We report resultant incident rate ratios (IRRs) with 95% CIs, and we report these as statistically significant at the 5% level only after adjustment for multiple comparisons across our six hospital-resource variables using the conservative Bonferroni adjustment. The pseudo-R² for each of these six models is also reported to summarize the amount of variation in deaths explained. For our model with ICU beds per COVID-19 case, we perform postestimation prediction of number of deaths by HRR, assuming the counterfactual in which HRRs with fewer than average ICU beds per COVID-19 case instead had the average observed number of ICU beds per COVID-19 case by HRR in April, which functioned as a measure of early excess deaths potentially related to resource limitations. The study was classified as exempt by the Institutional Review Board at the Yale School of Medicine, New Haven, Connecticut. Analyses were conducted in Stata 15 (StataCorp LLC) and R.

A total of 4,453 hospitals across 306 HRRs were included and linked to 2,827 county-level COVID-19 case and death counts in each of 5 months (March through July 2020). The median HRR in our analysis included 14 hospitals, with a maximum of 76 hospitals (Los Angeles, California) and a minimum of 1 (Longview, Texas). Among HRRs, 206 (67.3%) had experienced caseloads exceeding 20 per 10,000 population, while 85 (27.8%) had experienced greater than 100 per 10,000 population in the peak month during the study period. The Table depicts results of each of six Poisson distribution regression models, with the finding that greater number of ICU beds (IRR, 0.194; 95% CI, 0.076-0.491), general medical/surgical beds (IRR, 0.800; 95% CI, 0.696-0.920), and nurses (IRR, 0.927; 95% CI, 0.888-0.967) per COVID-19 case in April were statistically significantly associated with reduced deaths.

The model including ICU beds per COVID-19 case had the largest pseudo-R² at 0.6018, which suggests that ICU bed availability explains the most variation in death count among hospital resource variables analyzed. The incident rate ratio in this model implies that, for an entire additional ICU bed for each COVID-19 case (a one-unit increase in that variable), there is an associated one-fifth decrease in incidence rate (IRR, 0.194) of death in April. The mean value among HRRs in April was 0.25 ICU beds per case (one ICU bed for every four COVID-19 cases), but it was as low as 0.01 to 0.005 in hard-hit areas (one ICU bed for every 100 to 200 COVID-19 cases). The early excess deaths observed in April were not observed in later months. The magnitude of this effect can be summarized as follows: If the 152 HRRs in April with fewer than the mean number of ICU beds per COVID-19 case were to instead have the mean number (one ICU bed for every four COVID-19 cases), our model estimates that there would have been 15,571 fewer deaths that month. The HRRs with the largest number of early excess deaths were Manhattan in New York City (1,466), Bronx in New York City (1,315), Boston, Massachusetts (1,293), Philadelphia, Pennsylvania (955), Hartford, Connecticut (682), Detroit, Michigan (499), and Camden, New Jersey (484). The Figure depicts HRRs in the United States with early excess deaths by this measure in April.

We found significant associations between availability of hospital-based resources and COVID-19 deaths in the month of April 2020. This observation was consistent across measures of both hospital bed and staff capacity but not statistically significant in all cases. This provides empiric evidence in support of a preprint simulation publication by Branas et al showing the potential for thousands of excess deaths related to lack of available resources.⁴ Interestingly, the relationship between hospital-based resources per COVID-19 case and death count is not seen in May, June, or July. This may be because hospitals and health systems were rapidly adapting to pandemic demands⁹ by shifting resources or reorganizing existing infrastructure to free up beds and personnel.

Our findings underscore the importance of analyses that address heterogeneity in health system response over time and across different geographic areas. That the relationship is not seen after the early pandemic period, when hospitals and health systems were most overwhelmed, suggests that health systems and communities were able to adapt. Importantly, this work does not address the likely complex relationships among hospital resources and outcomes (for example, the benefit of ICU bed availability is likely limited when there are insufficient intensivists and nurses). These complexities should be a focus of future work. Furthermore, hospital resource flexibility, community efforts to slow transmission, and improvements in testing availability and the management of COVID-19 among hospitalized patients may all play a role in attenuating the relationship between baseline resource limitations and outcomes for patients with COVID-19.

These results merit further granular studies to examine specific hospital resources and observed variation in outcomes. Prior literature has linked inpatient capacity—variously defined as high census, acuity, turnover, or delayed admission—to outcomes including mortality among patients with stroke, among those with acute coronary syndrome, and among those requiring intensive care.¹⁰ Literature from Italy’s experience shows there was large variation in the case fatality rate among regions of Northern Italy and argues this was partially due to hospital resource limitations.¹¹ Future work can also address whether just-in-time resource mobilization, such as temporary ICU expansion, physician cross-staffing, telemedicine, and dedicated units for COVID-19 patients, attenuated the impact of potential hospital resource scarcity on outcomes.

The present analysis is limited by the quality of the data. There is likely variation of available COVID-19 testing by HRR. It may be that areas with larger outbreaks early on generally tested a smaller, sicker proportion of population-level cases than did those with smaller outbreaks. This effect may be reversed if larger HRRs in urban areas have health systems and public health departments more inclined toward or capable of doing more testing. Furthermore, deaths related to COVID-19 are likely related to community-based factors, including nonhealthcare resources and underlying population characteristics, that likely correlate with the availability of hospital-based resources within HRRs. Some have called into question whether, a priori, we should expect hospital-based capacity to be an important driver of mortality at all,¹² arguing that, when critical care capacity is exceeded, resources may be efficiently reallocated away from patients who are least likely to benefit. Because we used the American Hospital Association data, this snapshot of hospital resources is not limited to critical care capacity because there could be alternative explanations for situations in which mortality for both COVID-19 and non–COVID-19 patients may be lower and hospital resources are better matched with demand. For example, patients may seek care earlier in their disease course (whether COVID-19 or otherwise)¹³ if their local emergency department is not thought to be overwhelmed with case volume.

We find that COVID-19 deaths vary among HRRs. The availability of several hospital-based resources is associated with death rates and supports early efforts across the United States to “flatten the curve” to prevent hospital overload. Continued surveillance of this relationship is essential to guide policymakers and hospitals seeking to balance the still limited supply of resources with the demands of caring for both infectious and noninfectious patients in the coming months of this outbreak and in future pandemics.

The authors gratefully acknowledge the help of Carolyn Lusch, AICP, in generating depictions of results in Geographic Information Systems.

The COVID-19 pandemic is a crisis of mismatch between resources and infection burden. There is extraordinary heterogeneity across time and geography in the pandemic impact, with hospitals in New York City initially inundated while hospitals in major urban areas of California were comparatively quiet. Efforts to “flatten the curve” are intended to improve outcomes by reducing health system overload.¹ In the case of hospital-based care, health systems’ primary resources include emergency and critical care bed and staff capacity.

Prior work has documented wide variability in intensive care capacity across the United States and hypothesized that even moderate disease outbreaks could overwhelm hospital referral regions (HRRs).^2,3 Various simulations of outbreaks suggested that thousands of deaths are potentially preventable depending on the health system’s response,⁴ although the degree to which resource limitations have contributed to mortality during this COVID-19 pandemic has yet to be explored. The objective of this analysis was to examine the association between hospital resources and COVID-19 deaths amongst HRRs in the United States in the period from March 1 to July 26, 2020.

Data

This was an analysis of the American Hospital Association Annual Survey Database from 2017 and 2018, including hospital resource variables such as total hospital beds, hospitalists, intensive care beds, intensivists, emergency physicians, and nurses.⁵ The analysis was limited to general medical and surgical hospitals capable of providing acute care services, defined as those reporting at least 500 emergency department visits in 2018. Where data were missing on analysis variables (26.0% missing overall), the data were drawn from the 2017 survey results (reduced to 23.8% missing) from the same site as available, and the remainder were imputed with multivariate imputation by chained equations. An identical analysis without imputation was performed as a sensitivity analysis that showed a similar pattern of results. Total resources were tabulated amongst HRRs, and the hospital resources per COVID-19 case calculated. HRRs are a geographic category devised to represent regional health care markets, and each includes hospital sites performing major procedures.³ These were the focus of the analysis because they may represent a meaningful geographic division of hospital-based resources. COVID-19 case and death counts (as of July 26, 2020) were drawn from publicly available county-level data curated by the New York Times from state and local governments as well as health departments nationwide,⁶ separated by month (ie, March, April, May, June, and July). Data on New York City were available in aggregate (rather than separated by borough). Cases and deaths were therefore apportioned to the three HRRs involving New York City in proportion to that area’s population. To adjust for the lag between COVID-19 cases and deaths,^7,8 we offset deaths 2 weeks into the future so that the April COVID-19 death count for a given HRR included deaths that occurred for 1 month beginning 2 weeks after the start of April, and so on.

Analysis

We estimated Poisson distribution regressions for the outcome of COVID-19 death count in each HRR and month with one model for each of our six hospital-based resource variables. The offset (exposure) variable was COVID-19 case count. To adjust for the possibility of varying effects of hospital resources on deaths by month (ie, in anticipation that health systems might evolve in response to the pandemic over time), each model includes terms for the interaction between hospital-based resource and an indicator variable for month, as well as a fifth term for month. Standard errors were adjusted for clustering within HRR. We report resultant incident rate ratios (IRRs) with 95% CIs, and we report these as statistically significant at the 5% level only after adjustment for multiple comparisons across our six hospital-resource variables using the conservative Bonferroni adjustment. The pseudo-R² for each of these six models is also reported to summarize the amount of variation in deaths explained. For our model with ICU beds per COVID-19 case, we perform postestimation prediction of number of deaths by HRR, assuming the counterfactual in which HRRs with fewer than average ICU beds per COVID-19 case instead had the average observed number of ICU beds per COVID-19 case by HRR in April, which functioned as a measure of early excess deaths potentially related to resource limitations. The study was classified as exempt by the Institutional Review Board at the Yale School of Medicine, New Haven, Connecticut. Analyses were conducted in Stata 15 (StataCorp LLC) and R.

A total of 4,453 hospitals across 306 HRRs were included and linked to 2,827 county-level COVID-19 case and death counts in each of 5 months (March through July 2020). The median HRR in our analysis included 14 hospitals, with a maximum of 76 hospitals (Los Angeles, California) and a minimum of 1 (Longview, Texas). Among HRRs, 206 (67.3%) had experienced caseloads exceeding 20 per 10,000 population, while 85 (27.8%) had experienced greater than 100 per 10,000 population in the peak month during the study period. The Table depicts results of each of six Poisson distribution regression models, with the finding that greater number of ICU beds (IRR, 0.194; 95% CI, 0.076-0.491), general medical/surgical beds (IRR, 0.800; 95% CI, 0.696-0.920), and nurses (IRR, 0.927; 95% CI, 0.888-0.967) per COVID-19 case in April were statistically significantly associated with reduced deaths.

The model including ICU beds per COVID-19 case had the largest pseudo-R² at 0.6018, which suggests that ICU bed availability explains the most variation in death count among hospital resource variables analyzed. The incident rate ratio in this model implies that, for an entire additional ICU bed for each COVID-19 case (a one-unit increase in that variable), there is an associated one-fifth decrease in incidence rate (IRR, 0.194) of death in April. The mean value among HRRs in April was 0.25 ICU beds per case (one ICU bed for every four COVID-19 cases), but it was as low as 0.01 to 0.005 in hard-hit areas (one ICU bed for every 100 to 200 COVID-19 cases). The early excess deaths observed in April were not observed in later months. The magnitude of this effect can be summarized as follows: If the 152 HRRs in April with fewer than the mean number of ICU beds per COVID-19 case were to instead have the mean number (one ICU bed for every four COVID-19 cases), our model estimates that there would have been 15,571 fewer deaths that month. The HRRs with the largest number of early excess deaths were Manhattan in New York City (1,466), Bronx in New York City (1,315), Boston, Massachusetts (1,293), Philadelphia, Pennsylvania (955), Hartford, Connecticut (682), Detroit, Michigan (499), and Camden, New Jersey (484). The Figure depicts HRRs in the United States with early excess deaths by this measure in April.

We found significant associations between availability of hospital-based resources and COVID-19 deaths in the month of April 2020. This observation was consistent across measures of both hospital bed and staff capacity but not statistically significant in all cases. This provides empiric evidence in support of a preprint simulation publication by Branas et al showing the potential for thousands of excess deaths related to lack of available resources.⁴ Interestingly, the relationship between hospital-based resources per COVID-19 case and death count is not seen in May, June, or July. This may be because hospitals and health systems were rapidly adapting to pandemic demands⁹ by shifting resources or reorganizing existing infrastructure to free up beds and personnel.

Our findings underscore the importance of analyses that address heterogeneity in health system response over time and across different geographic areas. That the relationship is not seen after the early pandemic period, when hospitals and health systems were most overwhelmed, suggests that health systems and communities were able to adapt. Importantly, this work does not address the likely complex relationships among hospital resources and outcomes (for example, the benefit of ICU bed availability is likely limited when there are insufficient intensivists and nurses). These complexities should be a focus of future work. Furthermore, hospital resource flexibility, community efforts to slow transmission, and improvements in testing availability and the management of COVID-19 among hospitalized patients may all play a role in attenuating the relationship between baseline resource limitations and outcomes for patients with COVID-19.

These results merit further granular studies to examine specific hospital resources and observed variation in outcomes. Prior literature has linked inpatient capacity—variously defined as high census, acuity, turnover, or delayed admission—to outcomes including mortality among patients with stroke, among those with acute coronary syndrome, and among those requiring intensive care.¹⁰ Literature from Italy’s experience shows there was large variation in the case fatality rate among regions of Northern Italy and argues this was partially due to hospital resource limitations.¹¹ Future work can also address whether just-in-time resource mobilization, such as temporary ICU expansion, physician cross-staffing, telemedicine, and dedicated units for COVID-19 patients, attenuated the impact of potential hospital resource scarcity on outcomes.

The present analysis is limited by the quality of the data. There is likely variation of available COVID-19 testing by HRR. It may be that areas with larger outbreaks early on generally tested a smaller, sicker proportion of population-level cases than did those with smaller outbreaks. This effect may be reversed if larger HRRs in urban areas have health systems and public health departments more inclined toward or capable of doing more testing. Furthermore, deaths related to COVID-19 are likely related to community-based factors, including nonhealthcare resources and underlying population characteristics, that likely correlate with the availability of hospital-based resources within HRRs. Some have called into question whether, a priori, we should expect hospital-based capacity to be an important driver of mortality at all,¹² arguing that, when critical care capacity is exceeded, resources may be efficiently reallocated away from patients who are least likely to benefit. Because we used the American Hospital Association data, this snapshot of hospital resources is not limited to critical care capacity because there could be alternative explanations for situations in which mortality for both COVID-19 and non–COVID-19 patients may be lower and hospital resources are better matched with demand. For example, patients may seek care earlier in their disease course (whether COVID-19 or otherwise)¹³ if their local emergency department is not thought to be overwhelmed with case volume.

We find that COVID-19 deaths vary among HRRs. The availability of several hospital-based resources is associated with death rates and supports early efforts across the United States to “flatten the curve” to prevent hospital overload. Continued surveillance of this relationship is essential to guide policymakers and hospitals seeking to balance the still limited supply of resources with the demands of caring for both infectious and noninfectious patients in the coming months of this outbreak and in future pandemics.

The authors gratefully acknowledge the help of Carolyn Lusch, AICP, in generating depictions of results in Geographic Information Systems.

The COVID-19 pandemic is a crisis of mismatch between resources and infection burden. There is extraordinary heterogeneity across time and geography in the pandemic impact, with hospitals in New York City initially inundated while hospitals in major urban areas of California were comparatively quiet. Efforts to “flatten the curve” are intended to improve outcomes by reducing health system overload.¹ In the case of hospital-based care, health systems’ primary resources include emergency and critical care bed and staff capacity.

Prior work has documented wide variability in intensive care capacity across the United States and hypothesized that even moderate disease outbreaks could overwhelm hospital referral regions (HRRs).^2,3 Various simulations of outbreaks suggested that thousands of deaths are potentially preventable depending on the health system’s response,⁴ although the degree to which resource limitations have contributed to mortality during this COVID-19 pandemic has yet to be explored. The objective of this analysis was to examine the association between hospital resources and COVID-19 deaths amongst HRRs in the United States in the period from March 1 to July 26, 2020.

Data

This was an analysis of the American Hospital Association Annual Survey Database from 2017 and 2018, including hospital resource variables such as total hospital beds, hospitalists, intensive care beds, intensivists, emergency physicians, and nurses.⁵ The analysis was limited to general medical and surgical hospitals capable of providing acute care services, defined as those reporting at least 500 emergency department visits in 2018. Where data were missing on analysis variables (26.0% missing overall), the data were drawn from the 2017 survey results (reduced to 23.8% missing) from the same site as available, and the remainder were imputed with multivariate imputation by chained equations. An identical analysis without imputation was performed as a sensitivity analysis that showed a similar pattern of results. Total resources were tabulated amongst HRRs, and the hospital resources per COVID-19 case calculated. HRRs are a geographic category devised to represent regional health care markets, and each includes hospital sites performing major procedures.³ These were the focus of the analysis because they may represent a meaningful geographic division of hospital-based resources. COVID-19 case and death counts (as of July 26, 2020) were drawn from publicly available county-level data curated by the New York Times from state and local governments as well as health departments nationwide,⁶ separated by month (ie, March, April, May, June, and July). Data on New York City were available in aggregate (rather than separated by borough). Cases and deaths were therefore apportioned to the three HRRs involving New York City in proportion to that area’s population. To adjust for the lag between COVID-19 cases and deaths,^7,8 we offset deaths 2 weeks into the future so that the April COVID-19 death count for a given HRR included deaths that occurred for 1 month beginning 2 weeks after the start of April, and so on.

Analysis

We estimated Poisson distribution regressions for the outcome of COVID-19 death count in each HRR and month with one model for each of our six hospital-based resource variables. The offset (exposure) variable was COVID-19 case count. To adjust for the possibility of varying effects of hospital resources on deaths by month (ie, in anticipation that health systems might evolve in response to the pandemic over time), each model includes terms for the interaction between hospital-based resource and an indicator variable for month, as well as a fifth term for month. Standard errors were adjusted for clustering within HRR. We report resultant incident rate ratios (IRRs) with 95% CIs, and we report these as statistically significant at the 5% level only after adjustment for multiple comparisons across our six hospital-resource variables using the conservative Bonferroni adjustment. The pseudo-R² for each of these six models is also reported to summarize the amount of variation in deaths explained. For our model with ICU beds per COVID-19 case, we perform postestimation prediction of number of deaths by HRR, assuming the counterfactual in which HRRs with fewer than average ICU beds per COVID-19 case instead had the average observed number of ICU beds per COVID-19 case by HRR in April, which functioned as a measure of early excess deaths potentially related to resource limitations. The study was classified as exempt by the Institutional Review Board at the Yale School of Medicine, New Haven, Connecticut. Analyses were conducted in Stata 15 (StataCorp LLC) and R.

A total of 4,453 hospitals across 306 HRRs were included and linked to 2,827 county-level COVID-19 case and death counts in each of 5 months (March through July 2020). The median HRR in our analysis included 14 hospitals, with a maximum of 76 hospitals (Los Angeles, California) and a minimum of 1 (Longview, Texas). Among HRRs, 206 (67.3%) had experienced caseloads exceeding 20 per 10,000 population, while 85 (27.8%) had experienced greater than 100 per 10,000 population in the peak month during the study period. The Table depicts results of each of six Poisson distribution regression models, with the finding that greater number of ICU beds (IRR, 0.194; 95% CI, 0.076-0.491), general medical/surgical beds (IRR, 0.800; 95% CI, 0.696-0.920), and nurses (IRR, 0.927; 95% CI, 0.888-0.967) per COVID-19 case in April were statistically significantly associated with reduced deaths.

The model including ICU beds per COVID-19 case had the largest pseudo-R² at 0.6018, which suggests that ICU bed availability explains the most variation in death count among hospital resource variables analyzed. The incident rate ratio in this model implies that, for an entire additional ICU bed for each COVID-19 case (a one-unit increase in that variable), there is an associated one-fifth decrease in incidence rate (IRR, 0.194) of death in April. The mean value among HRRs in April was 0.25 ICU beds per case (one ICU bed for every four COVID-19 cases), but it was as low as 0.01 to 0.005 in hard-hit areas (one ICU bed for every 100 to 200 COVID-19 cases). The early excess deaths observed in April were not observed in later months. The magnitude of this effect can be summarized as follows: If the 152 HRRs in April with fewer than the mean number of ICU beds per COVID-19 case were to instead have the mean number (one ICU bed for every four COVID-19 cases), our model estimates that there would have been 15,571 fewer deaths that month. The HRRs with the largest number of early excess deaths were Manhattan in New York City (1,466), Bronx in New York City (1,315), Boston, Massachusetts (1,293), Philadelphia, Pennsylvania (955), Hartford, Connecticut (682), Detroit, Michigan (499), and Camden, New Jersey (484). The Figure depicts HRRs in the United States with early excess deaths by this measure in April.

We found significant associations between availability of hospital-based resources and COVID-19 deaths in the month of April 2020. This observation was consistent across measures of both hospital bed and staff capacity but not statistically significant in all cases. This provides empiric evidence in support of a preprint simulation publication by Branas et al showing the potential for thousands of excess deaths related to lack of available resources.⁴ Interestingly, the relationship between hospital-based resources per COVID-19 case and death count is not seen in May, June, or July. This may be because hospitals and health systems were rapidly adapting to pandemic demands⁹ by shifting resources or reorganizing existing infrastructure to free up beds and personnel.

Our findings underscore the importance of analyses that address heterogeneity in health system response over time and across different geographic areas. That the relationship is not seen after the early pandemic period, when hospitals and health systems were most overwhelmed, suggests that health systems and communities were able to adapt. Importantly, this work does not address the likely complex relationships among hospital resources and outcomes (for example, the benefit of ICU bed availability is likely limited when there are insufficient intensivists and nurses). These complexities should be a focus of future work. Furthermore, hospital resource flexibility, community efforts to slow transmission, and improvements in testing availability and the management of COVID-19 among hospitalized patients may all play a role in attenuating the relationship between baseline resource limitations and outcomes for patients with COVID-19.

These results merit further granular studies to examine specific hospital resources and observed variation in outcomes. Prior literature has linked inpatient capacity—variously defined as high census, acuity, turnover, or delayed admission—to outcomes including mortality among patients with stroke, among those with acute coronary syndrome, and among those requiring intensive care.¹⁰ Literature from Italy’s experience shows there was large variation in the case fatality rate among regions of Northern Italy and argues this was partially due to hospital resource limitations.¹¹ Future work can also address whether just-in-time resource mobilization, such as temporary ICU expansion, physician cross-staffing, telemedicine, and dedicated units for COVID-19 patients, attenuated the impact of potential hospital resource scarcity on outcomes.

The present analysis is limited by the quality of the data. There is likely variation of available COVID-19 testing by HRR. It may be that areas with larger outbreaks early on generally tested a smaller, sicker proportion of population-level cases than did those with smaller outbreaks. This effect may be reversed if larger HRRs in urban areas have health systems and public health departments more inclined toward or capable of doing more testing. Furthermore, deaths related to COVID-19 are likely related to community-based factors, including nonhealthcare resources and underlying population characteristics, that likely correlate with the availability of hospital-based resources within HRRs. Some have called into question whether, a priori, we should expect hospital-based capacity to be an important driver of mortality at all,¹² arguing that, when critical care capacity is exceeded, resources may be efficiently reallocated away from patients who are least likely to benefit. Because we used the American Hospital Association data, this snapshot of hospital resources is not limited to critical care capacity because there could be alternative explanations for situations in which mortality for both COVID-19 and non–COVID-19 patients may be lower and hospital resources are better matched with demand. For example, patients may seek care earlier in their disease course (whether COVID-19 or otherwise)¹³ if their local emergency department is not thought to be overwhelmed with case volume.

We find that COVID-19 deaths vary among HRRs. The availability of several hospital-based resources is associated with death rates and supports early efforts across the United States to “flatten the curve” to prevent hospital overload. Continued surveillance of this relationship is essential to guide policymakers and hospitals seeking to balance the still limited supply of resources with the demands of caring for both infectious and noninfectious patients in the coming months of this outbreak and in future pandemics.

The authors gratefully acknowledge the help of Carolyn Lusch, AICP, in generating depictions of results in Geographic Information Systems.

Social determinants of health (SDoH) are social, economic, environmental, and occupational factors that are known to influence an individual’s health care utilization and clinical outcomes.^1,2 Because the Veterans Health Administration (VHA) is charged to address both the medical and nonmedical needs of the veteran population, it is increasingly interested in the impact SDoH have on veteran care.^3,4 To combat the adverse impact of such factors, the VHA has implemented several large-scale programs across the US that focus on prevalent SDoH, such as homelessness, substance abuse, and alcohol use disorders.^5,6 While such risk factors are generally universal in their distribution, variation across regions, between urban and rural spaces, and even within cities has been shown to exist in private settings.⁷ Understanding such variability potentially could be helpful to US Department of Veterans Affairs (VA) policymakers and leaders to better allocate funding and resources to address such issues.

Although previous work has highlighted regional and neighborhood-level variability of SDoH, no study has examined the facility-level variability of commonly encountered social risk factors within the VHA.^4,8 The aim of this study was to describe the interfacility variation of 5 common SDoH known to influence health and health outcomes among a national cohort of veterans hospitalized for common medical issues by using administrative data.

Methods

We used a national cohort of veterans aged ≥ 65 years who were hospitalized at a VHA acute care facility with a primary discharge diagnosis of acute myocardial infarction (AMI), heart failure (HF), or pneumonia in 2012. These conditions were chosen because they are publicly reported and frequently used for interfacility comparison.

Using the International Classification of Diseases–9th Revision (ICD-9) and VHA clinical stop codes, we calculated the median documented proportion of patients with any of the following 5 SDoH: lived alone, marginal housing, alcohol use disorder, substance use disorder, and use of substance use services for patients presenting with HF, MI, and pneumonia (Table). These SDoH were chosen because they are intervenable risk factors for which the VHA has several programs (eg, homeless outreach, substance abuse, and tobacco cessation). To examine the variability of these SDoH across VHA facilities, we determined the number of hospitals that had a sufficient number of admissions (≥ 50) to be included in the analyses. We then examined the administratively documented, facility-level variation in the proportion of individuals with any of the 5 SDoH administrative codes and examined the distribution of their use across all qualifying facilities.

Results

In total, 21,991 patients with either HF (9,853), pneumonia (9,362), or AMI (2,776) were identified across 91 VHA facilities. The majority were male (98%) and had a median (SD) age of 77.0 (9.0) years. The median facility-level proportion of veterans who had any of the SDoH risk factors extracted through administrative codes was low across all conditions, ranging from 0.5 to 2.2%. The most prevalent factors among patients admitted for HF, AMI, and pneumonia were lives alone (2.0% [Interquartile range (IQR), 1.0-5.2], 1.4% [IQR, 0-3.4], and 1.9% [IQR, 0.7-5.4]), substance use disorder (1.2% [IQR, 0-2.2], 1.6% [IQR: 0-3.0], and 1.3% [IQR, 0-2.2] and use of substance use services (0.9% [IQR, 0-1.6%], 1.0% [IQR, 0-1.7%], and 1.6% [IQR, 0-2.2%], respectively [Table]).

When utilizing the NLP algorithm, the documented prevalence of lives alone in the free text of the medical record was higher than administrative coding across all conditions (12.3% vs. 2.2%; P < .01). Among each of the 3 assessed conditions, HF (14.4% vs 2.0%, P < .01) had higher levels of lives alone compared with pneumonia (11% vs 1.9%, P < .01), and AMI (10.2% vs 1.4%, P < .01) when using the NLP algorithm. When we examined the documented facility-level variation in the proportion of individuals with any of the 5 SDoH administrative codes or NLP, we found large variability across all facilities—regardless of extraction method (Figure).

Discussion

While SDoH are known to impact health outcomes, the presence of these risk factors in administrative data among individuals hospitalized for common medical issues is low and variable across VHA facilities. Understanding the documented, facility-level variability of these measures may assist the VHA in determining how it invests time and resources—as different facilities may disproportionately serve a higher number of vulnerable individuals. Beyond the VHA, these findings have generalizable lessons for the US health care system, which has come to recognize how these risk factors impact patients’ health.¹⁰

Although the proportion of individuals with any of the assessed SDoH identified by administrative data was low, our findings are in line with recent studies that showed other risk factors such as social isolation (0.65%), housing issues (0.19%), and financial strain (0.07%) had similarly low prevalence.^8,11 Although the exact prevalence of such factors remains unclear, these findings highlight that SDoH do not appear to be well documented in administrative data. Low coding rates are likely due to the fact that SDoH administrative codes are not tied to financial reimbursement—thus not incentivizing their use by clinicians or hospital systems.

In 2014, an Institute of Medicine report suggested that collection of SDoH in electronic health data as a means to better empower clinicians and health care systems to address social disparities and further support research in SDoH.¹² Since then, data collection using SDoH screening tools has become more common across settings, but is not consistently translated to standardized data due to lack of industry consensus and technical barriers.¹³ To improve this process, the Centers for Medicare and Medicaid Services created “z-codes” for the ICD-10 classification system—a subset of codes that are meant to better capture patients’ underlying social risk.¹⁴ It remains to be seen if such administrative codes have improved the documentation of SDoH.

As health care systems have grown to understand the impact of SDoH on health outcomes,other means of collecting these data have evolved.^1,10 For example, NLP-based extraction methods and electronic screening tools have been proposed and utilized as alternative for obtaining this information. Our findings suggest that some of these measures (eg, lives alone) often may be documented as part of routine care in the electronic health record, thus highlighting NLP as a tool to obtain such data. However, other studies using NLP technology to extract SDoH have shown this technology is often complicated by quality issues (ie, missing data), complex methods, and poor integration with current information technology infrastructures—thus limiting its use in health care delivery.^15-18

While variance among SDoH across a national health care system is natural, it remains an important systems-level characteristic that health care leaders and policymakers should appreciate. As health care systems disperse financial resources and initiate quality improvement initiatives to address SDoH, knowing that not all facilities are equally affected by SDoH should impact allocation of such resources and energies. Although previous work has highlighted regional and neighborhood levels of variation within the VHA and other health care systems, to our knowledge, this is the first study to examine variability at the facility-level within the VHA.^2,4,13,19

Limitations

There are several limitations to this study. First, though our findings are in line with previous data in other health care systems, generalizability beyond the VA, which primarily cares for older, male patients, may be limited.⁸ Though, as the nation’s largest health care system, lessons from the VHA can still be useful for other health care systems as they consider SDoH variation. Second, among the many SDoH previously identified to impact health, our analysis only focused on 5 such variables. Administrative and medical record documentation of other SDoH may be more common and less variable across institutions. Third, while our data suggests facility-level variation in these measures, this may be in part related to variation in coding across facilities. However, the single SDoH variable extracted using NLP also varied at the facility-level, suggesting that coding may not entirely drive the variation observed.

Conclusions

As US health care systems continue to address SDoH, our findings highlight the various challenges in obtaining accurate data on a patient’s social risk. Moreover, these findings highlight the large variability that exists among institutions in a national integrated health care system. Future work should explore the prevalence and variance of other SDoH as a means to help guide resource allocation and prioritize spending to better address SDoH where it is most needed.

Acknowledgments

This work was supported by NHLBI R01 RO1 HL116522-01A1. Support for VA/CMS data is provided by the US Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development, VA Information Resource Center (Project Numbers SDR 02-237 and 98-004).

Social determinants of health (SDoH) are social, economic, environmental, and occupational factors that are known to influence an individual’s health care utilization and clinical outcomes.^1,2 Because the Veterans Health Administration (VHA) is charged to address both the medical and nonmedical needs of the veteran population, it is increasingly interested in the impact SDoH have on veteran care.^3,4 To combat the adverse impact of such factors, the VHA has implemented several large-scale programs across the US that focus on prevalent SDoH, such as homelessness, substance abuse, and alcohol use disorders.^5,6 While such risk factors are generally universal in their distribution, variation across regions, between urban and rural spaces, and even within cities has been shown to exist in private settings.⁷ Understanding such variability potentially could be helpful to US Department of Veterans Affairs (VA) policymakers and leaders to better allocate funding and resources to address such issues.

Although previous work has highlighted regional and neighborhood-level variability of SDoH, no study has examined the facility-level variability of commonly encountered social risk factors within the VHA.^4,8 The aim of this study was to describe the interfacility variation of 5 common SDoH known to influence health and health outcomes among a national cohort of veterans hospitalized for common medical issues by using administrative data.

Methods

We used a national cohort of veterans aged ≥ 65 years who were hospitalized at a VHA acute care facility with a primary discharge diagnosis of acute myocardial infarction (AMI), heart failure (HF), or pneumonia in 2012. These conditions were chosen because they are publicly reported and frequently used for interfacility comparison.

Using the International Classification of Diseases–9th Revision (ICD-9) and VHA clinical stop codes, we calculated the median documented proportion of patients with any of the following 5 SDoH: lived alone, marginal housing, alcohol use disorder, substance use disorder, and use of substance use services for patients presenting with HF, MI, and pneumonia (Table). These SDoH were chosen because they are intervenable risk factors for which the VHA has several programs (eg, homeless outreach, substance abuse, and tobacco cessation). To examine the variability of these SDoH across VHA facilities, we determined the number of hospitals that had a sufficient number of admissions (≥ 50) to be included in the analyses. We then examined the administratively documented, facility-level variation in the proportion of individuals with any of the 5 SDoH administrative codes and examined the distribution of their use across all qualifying facilities.

Results

In total, 21,991 patients with either HF (9,853), pneumonia (9,362), or AMI (2,776) were identified across 91 VHA facilities. The majority were male (98%) and had a median (SD) age of 77.0 (9.0) years. The median facility-level proportion of veterans who had any of the SDoH risk factors extracted through administrative codes was low across all conditions, ranging from 0.5 to 2.2%. The most prevalent factors among patients admitted for HF, AMI, and pneumonia were lives alone (2.0% [Interquartile range (IQR), 1.0-5.2], 1.4% [IQR, 0-3.4], and 1.9% [IQR, 0.7-5.4]), substance use disorder (1.2% [IQR, 0-2.2], 1.6% [IQR: 0-3.0], and 1.3% [IQR, 0-2.2] and use of substance use services (0.9% [IQR, 0-1.6%], 1.0% [IQR, 0-1.7%], and 1.6% [IQR, 0-2.2%], respectively [Table]).

When utilizing the NLP algorithm, the documented prevalence of lives alone in the free text of the medical record was higher than administrative coding across all conditions (12.3% vs. 2.2%; P < .01). Among each of the 3 assessed conditions, HF (14.4% vs 2.0%, P < .01) had higher levels of lives alone compared with pneumonia (11% vs 1.9%, P < .01), and AMI (10.2% vs 1.4%, P < .01) when using the NLP algorithm. When we examined the documented facility-level variation in the proportion of individuals with any of the 5 SDoH administrative codes or NLP, we found large variability across all facilities—regardless of extraction method (Figure).

Discussion

While SDoH are known to impact health outcomes, the presence of these risk factors in administrative data among individuals hospitalized for common medical issues is low and variable across VHA facilities. Understanding the documented, facility-level variability of these measures may assist the VHA in determining how it invests time and resources—as different facilities may disproportionately serve a higher number of vulnerable individuals. Beyond the VHA, these findings have generalizable lessons for the US health care system, which has come to recognize how these risk factors impact patients’ health.¹⁰

Although the proportion of individuals with any of the assessed SDoH identified by administrative data was low, our findings are in line with recent studies that showed other risk factors such as social isolation (0.65%), housing issues (0.19%), and financial strain (0.07%) had similarly low prevalence.^8,11 Although the exact prevalence of such factors remains unclear, these findings highlight that SDoH do not appear to be well documented in administrative data. Low coding rates are likely due to the fact that SDoH administrative codes are not tied to financial reimbursement—thus not incentivizing their use by clinicians or hospital systems.

In 2014, an Institute of Medicine report suggested that collection of SDoH in electronic health data as a means to better empower clinicians and health care systems to address social disparities and further support research in SDoH.¹² Since then, data collection using SDoH screening tools has become more common across settings, but is not consistently translated to standardized data due to lack of industry consensus and technical barriers.¹³ To improve this process, the Centers for Medicare and Medicaid Services created “z-codes” for the ICD-10 classification system—a subset of codes that are meant to better capture patients’ underlying social risk.¹⁴ It remains to be seen if such administrative codes have improved the documentation of SDoH.

As health care systems have grown to understand the impact of SDoH on health outcomes,other means of collecting these data have evolved.^1,10 For example, NLP-based extraction methods and electronic screening tools have been proposed and utilized as alternative for obtaining this information. Our findings suggest that some of these measures (eg, lives alone) often may be documented as part of routine care in the electronic health record, thus highlighting NLP as a tool to obtain such data. However, other studies using NLP technology to extract SDoH have shown this technology is often complicated by quality issues (ie, missing data), complex methods, and poor integration with current information technology infrastructures—thus limiting its use in health care delivery.^15-18

While variance among SDoH across a national health care system is natural, it remains an important systems-level characteristic that health care leaders and policymakers should appreciate. As health care systems disperse financial resources and initiate quality improvement initiatives to address SDoH, knowing that not all facilities are equally affected by SDoH should impact allocation of such resources and energies. Although previous work has highlighted regional and neighborhood levels of variation within the VHA and other health care systems, to our knowledge, this is the first study to examine variability at the facility-level within the VHA.^2,4,13,19

Limitations

There are several limitations to this study. First, though our findings are in line with previous data in other health care systems, generalizability beyond the VA, which primarily cares for older, male patients, may be limited.⁸ Though, as the nation’s largest health care system, lessons from the VHA can still be useful for other health care systems as they consider SDoH variation. Second, among the many SDoH previously identified to impact health, our analysis only focused on 5 such variables. Administrative and medical record documentation of other SDoH may be more common and less variable across institutions. Third, while our data suggests facility-level variation in these measures, this may be in part related to variation in coding across facilities. However, the single SDoH variable extracted using NLP also varied at the facility-level, suggesting that coding may not entirely drive the variation observed.

Conclusions

As US health care systems continue to address SDoH, our findings highlight the various challenges in obtaining accurate data on a patient’s social risk. Moreover, these findings highlight the large variability that exists among institutions in a national integrated health care system. Future work should explore the prevalence and variance of other SDoH as a means to help guide resource allocation and prioritize spending to better address SDoH where it is most needed.

Acknowledgments

This work was supported by NHLBI R01 RO1 HL116522-01A1. Support for VA/CMS data is provided by the US Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development, VA Information Resource Center (Project Numbers SDR 02-237 and 98-004).

Social determinants of health (SDoH) are social, economic, environmental, and occupational factors that are known to influence an individual’s health care utilization and clinical outcomes.^1,2 Because the Veterans Health Administration (VHA) is charged to address both the medical and nonmedical needs of the veteran population, it is increasingly interested in the impact SDoH have on veteran care.^3,4 To combat the adverse impact of such factors, the VHA has implemented several large-scale programs across the US that focus on prevalent SDoH, such as homelessness, substance abuse, and alcohol use disorders.^5,6 While such risk factors are generally universal in their distribution, variation across regions, between urban and rural spaces, and even within cities has been shown to exist in private settings.⁷ Understanding such variability potentially could be helpful to US Department of Veterans Affairs (VA) policymakers and leaders to better allocate funding and resources to address such issues.

Although previous work has highlighted regional and neighborhood-level variability of SDoH, no study has examined the facility-level variability of commonly encountered social risk factors within the VHA.^4,8 The aim of this study was to describe the interfacility variation of 5 common SDoH known to influence health and health outcomes among a national cohort of veterans hospitalized for common medical issues by using administrative data.

Methods

We used a national cohort of veterans aged ≥ 65 years who were hospitalized at a VHA acute care facility with a primary discharge diagnosis of acute myocardial infarction (AMI), heart failure (HF), or pneumonia in 2012. These conditions were chosen because they are publicly reported and frequently used for interfacility comparison.

Using the International Classification of Diseases–9th Revision (ICD-9) and VHA clinical stop codes, we calculated the median documented proportion of patients with any of the following 5 SDoH: lived alone, marginal housing, alcohol use disorder, substance use disorder, and use of substance use services for patients presenting with HF, MI, and pneumonia (Table). These SDoH were chosen because they are intervenable risk factors for which the VHA has several programs (eg, homeless outreach, substance abuse, and tobacco cessation). To examine the variability of these SDoH across VHA facilities, we determined the number of hospitals that had a sufficient number of admissions (≥ 50) to be included in the analyses. We then examined the administratively documented, facility-level variation in the proportion of individuals with any of the 5 SDoH administrative codes and examined the distribution of their use across all qualifying facilities.

Results

In total, 21,991 patients with either HF (9,853), pneumonia (9,362), or AMI (2,776) were identified across 91 VHA facilities. The majority were male (98%) and had a median (SD) age of 77.0 (9.0) years. The median facility-level proportion of veterans who had any of the SDoH risk factors extracted through administrative codes was low across all conditions, ranging from 0.5 to 2.2%. The most prevalent factors among patients admitted for HF, AMI, and pneumonia were lives alone (2.0% [Interquartile range (IQR), 1.0-5.2], 1.4% [IQR, 0-3.4], and 1.9% [IQR, 0.7-5.4]), substance use disorder (1.2% [IQR, 0-2.2], 1.6% [IQR: 0-3.0], and 1.3% [IQR, 0-2.2] and use of substance use services (0.9% [IQR, 0-1.6%], 1.0% [IQR, 0-1.7%], and 1.6% [IQR, 0-2.2%], respectively [Table]).

When utilizing the NLP algorithm, the documented prevalence of lives alone in the free text of the medical record was higher than administrative coding across all conditions (12.3% vs. 2.2%; P < .01). Among each of the 3 assessed conditions, HF (14.4% vs 2.0%, P < .01) had higher levels of lives alone compared with pneumonia (11% vs 1.9%, P < .01), and AMI (10.2% vs 1.4%, P < .01) when using the NLP algorithm. When we examined the documented facility-level variation in the proportion of individuals with any of the 5 SDoH administrative codes or NLP, we found large variability across all facilities—regardless of extraction method (Figure).

Discussion

While SDoH are known to impact health outcomes, the presence of these risk factors in administrative data among individuals hospitalized for common medical issues is low and variable across VHA facilities. Understanding the documented, facility-level variability of these measures may assist the VHA in determining how it invests time and resources—as different facilities may disproportionately serve a higher number of vulnerable individuals. Beyond the VHA, these findings have generalizable lessons for the US health care system, which has come to recognize how these risk factors impact patients’ health.¹⁰

Although the proportion of individuals with any of the assessed SDoH identified by administrative data was low, our findings are in line with recent studies that showed other risk factors such as social isolation (0.65%), housing issues (0.19%), and financial strain (0.07%) had similarly low prevalence.^8,11 Although the exact prevalence of such factors remains unclear, these findings highlight that SDoH do not appear to be well documented in administrative data. Low coding rates are likely due to the fact that SDoH administrative codes are not tied to financial reimbursement—thus not incentivizing their use by clinicians or hospital systems.

In 2014, an Institute of Medicine report suggested that collection of SDoH in electronic health data as a means to better empower clinicians and health care systems to address social disparities and further support research in SDoH.¹² Since then, data collection using SDoH screening tools has become more common across settings, but is not consistently translated to standardized data due to lack of industry consensus and technical barriers.¹³ To improve this process, the Centers for Medicare and Medicaid Services created “z-codes” for the ICD-10 classification system—a subset of codes that are meant to better capture patients’ underlying social risk.¹⁴ It remains to be seen if such administrative codes have improved the documentation of SDoH.

As health care systems have grown to understand the impact of SDoH on health outcomes,other means of collecting these data have evolved.^1,10 For example, NLP-based extraction methods and electronic screening tools have been proposed and utilized as alternative for obtaining this information. Our findings suggest that some of these measures (eg, lives alone) often may be documented as part of routine care in the electronic health record, thus highlighting NLP as a tool to obtain such data. However, other studies using NLP technology to extract SDoH have shown this technology is often complicated by quality issues (ie, missing data), complex methods, and poor integration with current information technology infrastructures—thus limiting its use in health care delivery.^15-18

While variance among SDoH across a national health care system is natural, it remains an important systems-level characteristic that health care leaders and policymakers should appreciate. As health care systems disperse financial resources and initiate quality improvement initiatives to address SDoH, knowing that not all facilities are equally affected by SDoH should impact allocation of such resources and energies. Although previous work has highlighted regional and neighborhood levels of variation within the VHA and other health care systems, to our knowledge, this is the first study to examine variability at the facility-level within the VHA.^2,4,13,19

Limitations

There are several limitations to this study. First, though our findings are in line with previous data in other health care systems, generalizability beyond the VA, which primarily cares for older, male patients, may be limited.⁸ Though, as the nation’s largest health care system, lessons from the VHA can still be useful for other health care systems as they consider SDoH variation. Second, among the many SDoH previously identified to impact health, our analysis only focused on 5 such variables. Administrative and medical record documentation of other SDoH may be more common and less variable across institutions. Third, while our data suggests facility-level variation in these measures, this may be in part related to variation in coding across facilities. However, the single SDoH variable extracted using NLP also varied at the facility-level, suggesting that coding may not entirely drive the variation observed.

Conclusions

As US health care systems continue to address SDoH, our findings highlight the various challenges in obtaining accurate data on a patient’s social risk. Moreover, these findings highlight the large variability that exists among institutions in a national integrated health care system. Future work should explore the prevalence and variance of other SDoH as a means to help guide resource allocation and prioritize spending to better address SDoH where it is most needed.

Acknowledgments

This work was supported by NHLBI R01 RO1 HL116522-01A1. Support for VA/CMS data is provided by the US Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development, VA Information Resource Center (Project Numbers SDR 02-237 and 98-004).

During the initial peak of coronavirus disease 2019 (COVID-19) cases, US models suggested hospital bed shortages, hinting at the dire possibility of an overwhelmed healthcare system.^1,2 Such projections invoked widespread uncertainty and fear of massive loss of life secondary to an undersupply of treatment resources. This led many state governments to rush into a series of historically unprecedented interventions, including the rapid deployment of field hospitals. US state governments, in partnership with the Army Corps of Engineers, invested more than $660 million to transform convention halls, university campus buildings, and even abandoned industrial warehouses, into overflow hospitals for the care of COVID-19 patients.¹ Such a national scale of field hospital construction is truly historic, never before having occurred at this speed and on this scale. The only other time field hospitals were deployed nearly as widely in the United States was during the Civil War.³

The use of COVID-19 field hospital resources has been variable, with patient volumes ranging from 0 at many to more than 1,000 at the Javits Center field hospital in New York City.¹ In fact, most field hospitals did not treat any patients because early public health measures, such as stay-at-home orders, helped contain the virus in most states.¹ As of this writing, the United States has seen a dramatic surge in COVID-19 transmission and hospitalizations. This has led many states to re-introduce field hospitals into their COVID emergency response.

Our site, the Baltimore Convention Center Field Hospital (BCCFH), is one of few sites that is still operational and, to our knowledge, is the longest-running US COVID-19 field hospital. We have cared for 543 patients since opening and have had no cardiac arrests or on-site deaths. To safely offload lower-acuity COVID-19 patients from Maryland hospitals, we designed admission criteria and care processes to provide medical care on site until patients are ready for discharge. However, we anticipated that some patients would decompensate and need to return to a higher level of care. Here, we share our experience with identifying, assessing, resuscitating, and transporting unstable patients. We believe that this process has allowed us to treat about 80% of our patients in place with successful discharge to outpatient care. We have safely transferred about 20% to a higher level of care, having learned from our early cases to refine and improve our rapid response process.

Case 1

A 39-year-old man was transferred to the BCCFH on his 9th day of symptoms following a 3-day hospital admission for COVID-19. On BCCFH day 1, he developed an oxygen requirement of 2 L/min and a fever of 39.9 ^oC. Testing revealed worsening hyponatremia and new proteinuria, and a chest radiograph showed increased bilateral interstitial infiltrates. Cefdinir and fluid restriction were initiated. On BCCFH day 2, the patient developed hypotension (88/55 mm Hg), tachycardia (180 bpm), an oxygen requirement of 3 L/min, and a brief syncopal episode while sitting in bed. The charge physician and nurse were directed to the bedside. They instructed staff to bring a stretcher and intravenous (IV) supplies. Unable to locate these supplies in the triage bay, the staff found them in various locations. An IV line was inserted, and fluids administered, after which vital signs improved. Emergency medical services (EMS), which were on standby outside the field hospital, were alerted via radio; they donned personal protective equipment (PPE) and arrived at the triage bay. They were redirected to patient bedside, whence they transported the patient to the hospital.

Case 2

A 64-year-old man with a history of homelessness, myocardial infarctions, cerebrovascular accident, and paroxysmal atrial fibrillation was transferred to the BCCFH on his 6th day of symptoms after a 2-day hospitalization with COVID-19 respiratory illness. On BCCFH day 1, he had a temperature of 39.3 ^oC and atypical chest pain. A laboratory workup was unrevealing. On BCCFH day 2, he had asymptomatic hypotension and a heart rate of 60-85 bpm while receiving his usual metoprolol dose. On BCCFH day 3, he reported dizziness and was found to be hypotensive (83/41 mm Hg) and febrile (38.6 ^oC). The rapid response team (RRT) was called over radio, and they quickly assessed the patient and transported him to the triage bay. EMS, signaled through the RRT radio announcement, arrived at the triage bay and transported the patient to a traditional hospital.

The BCCFH, which opened in April 2020, is a 252-bed facility that’s spread over a single exhibit hall floor and cares for stable adult COVID-19 patients from any hospital or emergency department in Maryland (Appendix A). The site offers basic laboratory tests, radiography, a limited on-site pharmacy, and spot vital sign monitoring without telemetry. Both EMS and a certified registered nurse anesthetist are on standby in the nonclinical area and must don PPE before entering the patient care area when called. The appendices show the patient beds (Appendix B) and triage area (Appendix C) used for patient evaluation and resuscitation. Unlike conventional hospitals, the BCCFH has limited consultant access, and there are frequent changes in clinical teams. In addition to clinicians, our site has physical therapists, occupational therapists, and social work teams to assist in patient care and discharge planning. As of this writing, we have cared for 543 patients, sent to us from one-third of Maryland’s hospitals. Use during the first wave of COVID was variable, with some hospitals sending us just a few patients. One Baltimore hospital sent us 8% of its COVID-19 patients. Because the patients have an average 5-day stay, the BCCFH has offloaded 2,600 bed-days of care from acute hospitals.

COVID-19 field hospitals must be prepared to respond effectively to decompensating patients. In our experience, effective RRTs provide a standard and reproducible approach to patient emergencies. In the conventional hospital setting, these teams consist of clinicians who can be called on by any healthcare worker to quickly assess deteriorating patients and intervene with treatment. The purpose of an RRT is to provide immediate care to a patient before progression to respiratory or cardiac arrest. RRTs proliferated in US hospitals after 2004 when the Institute for Healthcare Improvement in Boston, Massachusetts, recommended such teams for improved quality of care. Though studies report conflicting findings on the impact of RRTs on mortality rates, these studies were performed in traditional hospitals with ample resources, consultants, and clinicians familiar with their patients rather than in resource-limited field hospitals.^4-13 Our field hospital has found RRTs, and the principles behind them, useful in the identification and management of decompensating COVID-19 patients.

An approach to managing decompensating patients in a COVID-19 field hospital can be considered in four phases: identification, assessment, resuscitation, and transport. Referring to these phases, the first case shows opportunities for improvement in resuscitation and transport. Although decompensation was identified, the patient was not transported to the triage bay for resuscitation, and there was confusion when trying to obtain the proper equipment. Additionally, EMS awaited the patient in the triage bay, while he remained in his cubicle, which delayed transport to an acute care hospital. The second case shows opportunities for improvement in identification and assessment. The patient had signs of impending decompensation that were not immediately recognized and treated. However, once decompensation occurred, the RRT was called and the patient was transported quickly to the triage bay, and then to the hospital via EMS.

In our experience at the BCCFH, identification is a key phase in COVID-19 care at a field hospital. Identification involves recognizing impending deterioration, as well as understanding risk factors for decompensation. For COVID-19 specifically, this requires heightened awareness of patients who are in the 2nd to 3rd week of symptoms. Data from Wuhan, China, suggest that decompensation occurs predictably around symptom day 9.^14,15 At the BCCFH, the median symptom duration for patients who decompensated and returned to a hospital was 13 days. In both introductory cases, patients were in the high-risk 2nd week of symptoms when decompensation occurred. Clinicians at the BCCFH now discuss patient symptom day during their handoffs, when rounding, and when making decisions regarding acute care transfer. Our team has also integrated clinical information from our electronic health record to create a dashboard describing those patients requiring acute care transfer to assist in identifying other trends or predictive factors (Appendix D).

Although RRTs are designed to activate when an individual patient decompensates, they should fit within a larger operational framework for patient safety. Our experience with emergencies at the BCCFH has yielded four opportunities for learning relevant to COVID-19 care in nontraditional settings (Table). These lessons include how to update staff on clinical process changes, unify communication systems, create a clinical drilling culture, and review cases to improve performance. They illustrate the importance of standardizing emergency processes, conducting frequent updates and drills, and ensuring continuous improvement. We found that, while caring for patients with an unpredictable, novel disease in a nontraditional setting and while wearing PPE and working with new colleagues during every shift, the best approach to support patients and staff is to anticipate emergencies rather than relying on individual staff to develop on-the-spot solutions.

The COVID-19 era has seen the unprecedented construction and utilization of emergency field hospital facilities. Such facilities can serve to offload some COVID-19 patients from strained healthcare infrastructure and provide essential care to these patients. We share many of the unique physical and logistical considerations specific to a nontraditional site. We optimized our space, our equipment, and our communication system. We learned how to identify, assess, resuscitate, and transport decompensating COVID-19 patients. Ultimately, our field hospital has been well utilized and successful at caring for patients because of its adaptability, accessibility, and safety record. Of the 15% of patients we transferred to a hospital for care, 81% were successfully stabilized and were willing to return to the BCCFH to complete their care. Our design included supportive care such as social work, physical and occupational therapy, and treatment of comorbidities, such as diabetes and substance use disorder. Our model demonstrates an effective nonhospital option for the care of lower-acuity, medically complex COVID-19 patients. If such facilities are used in subsequent COVID-19 outbreaks, we advise structured planning for the care of decompensating patients that takes into account the need for effective communication, drilling, and ongoing process improvement.

During the initial peak of coronavirus disease 2019 (COVID-19) cases, US models suggested hospital bed shortages, hinting at the dire possibility of an overwhelmed healthcare system.^1,2 Such projections invoked widespread uncertainty and fear of massive loss of life secondary to an undersupply of treatment resources. This led many state governments to rush into a series of historically unprecedented interventions, including the rapid deployment of field hospitals. US state governments, in partnership with the Army Corps of Engineers, invested more than $660 million to transform convention halls, university campus buildings, and even abandoned industrial warehouses, into overflow hospitals for the care of COVID-19 patients.¹ Such a national scale of field hospital construction is truly historic, never before having occurred at this speed and on this scale. The only other time field hospitals were deployed nearly as widely in the United States was during the Civil War.³

The use of COVID-19 field hospital resources has been variable, with patient volumes ranging from 0 at many to more than 1,000 at the Javits Center field hospital in New York City.¹ In fact, most field hospitals did not treat any patients because early public health measures, such as stay-at-home orders, helped contain the virus in most states.¹ As of this writing, the United States has seen a dramatic surge in COVID-19 transmission and hospitalizations. This has led many states to re-introduce field hospitals into their COVID emergency response.

Our site, the Baltimore Convention Center Field Hospital (BCCFH), is one of few sites that is still operational and, to our knowledge, is the longest-running US COVID-19 field hospital. We have cared for 543 patients since opening and have had no cardiac arrests or on-site deaths. To safely offload lower-acuity COVID-19 patients from Maryland hospitals, we designed admission criteria and care processes to provide medical care on site until patients are ready for discharge. However, we anticipated that some patients would decompensate and need to return to a higher level of care. Here, we share our experience with identifying, assessing, resuscitating, and transporting unstable patients. We believe that this process has allowed us to treat about 80% of our patients in place with successful discharge to outpatient care. We have safely transferred about 20% to a higher level of care, having learned from our early cases to refine and improve our rapid response process.

Case 1

A 39-year-old man was transferred to the BCCFH on his 9th day of symptoms following a 3-day hospital admission for COVID-19. On BCCFH day 1, he developed an oxygen requirement of 2 L/min and a fever of 39.9 ^oC. Testing revealed worsening hyponatremia and new proteinuria, and a chest radiograph showed increased bilateral interstitial infiltrates. Cefdinir and fluid restriction were initiated. On BCCFH day 2, the patient developed hypotension (88/55 mm Hg), tachycardia (180 bpm), an oxygen requirement of 3 L/min, and a brief syncopal episode while sitting in bed. The charge physician and nurse were directed to the bedside. They instructed staff to bring a stretcher and intravenous (IV) supplies. Unable to locate these supplies in the triage bay, the staff found them in various locations. An IV line was inserted, and fluids administered, after which vital signs improved. Emergency medical services (EMS), which were on standby outside the field hospital, were alerted via radio; they donned personal protective equipment (PPE) and arrived at the triage bay. They were redirected to patient bedside, whence they transported the patient to the hospital.

Case 2

A 64-year-old man with a history of homelessness, myocardial infarctions, cerebrovascular accident, and paroxysmal atrial fibrillation was transferred to the BCCFH on his 6th day of symptoms after a 2-day hospitalization with COVID-19 respiratory illness. On BCCFH day 1, he had a temperature of 39.3 ^oC and atypical chest pain. A laboratory workup was unrevealing. On BCCFH day 2, he had asymptomatic hypotension and a heart rate of 60-85 bpm while receiving his usual metoprolol dose. On BCCFH day 3, he reported dizziness and was found to be hypotensive (83/41 mm Hg) and febrile (38.6 ^oC). The rapid response team (RRT) was called over radio, and they quickly assessed the patient and transported him to the triage bay. EMS, signaled through the RRT radio announcement, arrived at the triage bay and transported the patient to a traditional hospital.

The BCCFH, which opened in April 2020, is a 252-bed facility that’s spread over a single exhibit hall floor and cares for stable adult COVID-19 patients from any hospital or emergency department in Maryland (Appendix A). The site offers basic laboratory tests, radiography, a limited on-site pharmacy, and spot vital sign monitoring without telemetry. Both EMS and a certified registered nurse anesthetist are on standby in the nonclinical area and must don PPE before entering the patient care area when called. The appendices show the patient beds (Appendix B) and triage area (Appendix C) used for patient evaluation and resuscitation. Unlike conventional hospitals, the BCCFH has limited consultant access, and there are frequent changes in clinical teams. In addition to clinicians, our site has physical therapists, occupational therapists, and social work teams to assist in patient care and discharge planning. As of this writing, we have cared for 543 patients, sent to us from one-third of Maryland’s hospitals. Use during the first wave of COVID was variable, with some hospitals sending us just a few patients. One Baltimore hospital sent us 8% of its COVID-19 patients. Because the patients have an average 5-day stay, the BCCFH has offloaded 2,600 bed-days of care from acute hospitals.

COVID-19 field hospitals must be prepared to respond effectively to decompensating patients. In our experience, effective RRTs provide a standard and reproducible approach to patient emergencies. In the conventional hospital setting, these teams consist of clinicians who can be called on by any healthcare worker to quickly assess deteriorating patients and intervene with treatment. The purpose of an RRT is to provide immediate care to a patient before progression to respiratory or cardiac arrest. RRTs proliferated in US hospitals after 2004 when the Institute for Healthcare Improvement in Boston, Massachusetts, recommended such teams for improved quality of care. Though studies report conflicting findings on the impact of RRTs on mortality rates, these studies were performed in traditional hospitals with ample resources, consultants, and clinicians familiar with their patients rather than in resource-limited field hospitals.^4-13 Our field hospital has found RRTs, and the principles behind them, useful in the identification and management of decompensating COVID-19 patients.

An approach to managing decompensating patients in a COVID-19 field hospital can be considered in four phases: identification, assessment, resuscitation, and transport. Referring to these phases, the first case shows opportunities for improvement in resuscitation and transport. Although decompensation was identified, the patient was not transported to the triage bay for resuscitation, and there was confusion when trying to obtain the proper equipment. Additionally, EMS awaited the patient in the triage bay, while he remained in his cubicle, which delayed transport to an acute care hospital. The second case shows opportunities for improvement in identification and assessment. The patient had signs of impending decompensation that were not immediately recognized and treated. However, once decompensation occurred, the RRT was called and the patient was transported quickly to the triage bay, and then to the hospital via EMS.

In our experience at the BCCFH, identification is a key phase in COVID-19 care at a field hospital. Identification involves recognizing impending deterioration, as well as understanding risk factors for decompensation. For COVID-19 specifically, this requires heightened awareness of patients who are in the 2nd to 3rd week of symptoms. Data from Wuhan, China, suggest that decompensation occurs predictably around symptom day 9.^14,15 At the BCCFH, the median symptom duration for patients who decompensated and returned to a hospital was 13 days. In both introductory cases, patients were in the high-risk 2nd week of symptoms when decompensation occurred. Clinicians at the BCCFH now discuss patient symptom day during their handoffs, when rounding, and when making decisions regarding acute care transfer. Our team has also integrated clinical information from our electronic health record to create a dashboard describing those patients requiring acute care transfer to assist in identifying other trends or predictive factors (Appendix D).

Although RRTs are designed to activate when an individual patient decompensates, they should fit within a larger operational framework for patient safety. Our experience with emergencies at the BCCFH has yielded four opportunities for learning relevant to COVID-19 care in nontraditional settings (Table). These lessons include how to update staff on clinical process changes, unify communication systems, create a clinical drilling culture, and review cases to improve performance. They illustrate the importance of standardizing emergency processes, conducting frequent updates and drills, and ensuring continuous improvement. We found that, while caring for patients with an unpredictable, novel disease in a nontraditional setting and while wearing PPE and working with new colleagues during every shift, the best approach to support patients and staff is to anticipate emergencies rather than relying on individual staff to develop on-the-spot solutions.

The COVID-19 era has seen the unprecedented construction and utilization of emergency field hospital facilities. Such facilities can serve to offload some COVID-19 patients from strained healthcare infrastructure and provide essential care to these patients. We share many of the unique physical and logistical considerations specific to a nontraditional site. We optimized our space, our equipment, and our communication system. We learned how to identify, assess, resuscitate, and transport decompensating COVID-19 patients. Ultimately, our field hospital has been well utilized and successful at caring for patients because of its adaptability, accessibility, and safety record. Of the 15% of patients we transferred to a hospital for care, 81% were successfully stabilized and were willing to return to the BCCFH to complete their care. Our design included supportive care such as social work, physical and occupational therapy, and treatment of comorbidities, such as diabetes and substance use disorder. Our model demonstrates an effective nonhospital option for the care of lower-acuity, medically complex COVID-19 patients. If such facilities are used in subsequent COVID-19 outbreaks, we advise structured planning for the care of decompensating patients that takes into account the need for effective communication, drilling, and ongoing process improvement.

During the initial peak of coronavirus disease 2019 (COVID-19) cases, US models suggested hospital bed shortages, hinting at the dire possibility of an overwhelmed healthcare system.^1,2 Such projections invoked widespread uncertainty and fear of massive loss of life secondary to an undersupply of treatment resources. This led many state governments to rush into a series of historically unprecedented interventions, including the rapid deployment of field hospitals. US state governments, in partnership with the Army Corps of Engineers, invested more than $660 million to transform convention halls, university campus buildings, and even abandoned industrial warehouses, into overflow hospitals for the care of COVID-19 patients.¹ Such a national scale of field hospital construction is truly historic, never before having occurred at this speed and on this scale. The only other time field hospitals were deployed nearly as widely in the United States was during the Civil War.³

The use of COVID-19 field hospital resources has been variable, with patient volumes ranging from 0 at many to more than 1,000 at the Javits Center field hospital in New York City.¹ In fact, most field hospitals did not treat any patients because early public health measures, such as stay-at-home orders, helped contain the virus in most states.¹ As of this writing, the United States has seen a dramatic surge in COVID-19 transmission and hospitalizations. This has led many states to re-introduce field hospitals into their COVID emergency response.

Our site, the Baltimore Convention Center Field Hospital (BCCFH), is one of few sites that is still operational and, to our knowledge, is the longest-running US COVID-19 field hospital. We have cared for 543 patients since opening and have had no cardiac arrests or on-site deaths. To safely offload lower-acuity COVID-19 patients from Maryland hospitals, we designed admission criteria and care processes to provide medical care on site until patients are ready for discharge. However, we anticipated that some patients would decompensate and need to return to a higher level of care. Here, we share our experience with identifying, assessing, resuscitating, and transporting unstable patients. We believe that this process has allowed us to treat about 80% of our patients in place with successful discharge to outpatient care. We have safely transferred about 20% to a higher level of care, having learned from our early cases to refine and improve our rapid response process.

Case 1

A 39-year-old man was transferred to the BCCFH on his 9th day of symptoms following a 3-day hospital admission for COVID-19. On BCCFH day 1, he developed an oxygen requirement of 2 L/min and a fever of 39.9 ^oC. Testing revealed worsening hyponatremia and new proteinuria, and a chest radiograph showed increased bilateral interstitial infiltrates. Cefdinir and fluid restriction were initiated. On BCCFH day 2, the patient developed hypotension (88/55 mm Hg), tachycardia (180 bpm), an oxygen requirement of 3 L/min, and a brief syncopal episode while sitting in bed. The charge physician and nurse were directed to the bedside. They instructed staff to bring a stretcher and intravenous (IV) supplies. Unable to locate these supplies in the triage bay, the staff found them in various locations. An IV line was inserted, and fluids administered, after which vital signs improved. Emergency medical services (EMS), which were on standby outside the field hospital, were alerted via radio; they donned personal protective equipment (PPE) and arrived at the triage bay. They were redirected to patient bedside, whence they transported the patient to the hospital.

Case 2

A 64-year-old man with a history of homelessness, myocardial infarctions, cerebrovascular accident, and paroxysmal atrial fibrillation was transferred to the BCCFH on his 6th day of symptoms after a 2-day hospitalization with COVID-19 respiratory illness. On BCCFH day 1, he had a temperature of 39.3 ^oC and atypical chest pain. A laboratory workup was unrevealing. On BCCFH day 2, he had asymptomatic hypotension and a heart rate of 60-85 bpm while receiving his usual metoprolol dose. On BCCFH day 3, he reported dizziness and was found to be hypotensive (83/41 mm Hg) and febrile (38.6 ^oC). The rapid response team (RRT) was called over radio, and they quickly assessed the patient and transported him to the triage bay. EMS, signaled through the RRT radio announcement, arrived at the triage bay and transported the patient to a traditional hospital.

The BCCFH, which opened in April 2020, is a 252-bed facility that’s spread over a single exhibit hall floor and cares for stable adult COVID-19 patients from any hospital or emergency department in Maryland (Appendix A). The site offers basic laboratory tests, radiography, a limited on-site pharmacy, and spot vital sign monitoring without telemetry. Both EMS and a certified registered nurse anesthetist are on standby in the nonclinical area and must don PPE before entering the patient care area when called. The appendices show the patient beds (Appendix B) and triage area (Appendix C) used for patient evaluation and resuscitation. Unlike conventional hospitals, the BCCFH has limited consultant access, and there are frequent changes in clinical teams. In addition to clinicians, our site has physical therapists, occupational therapists, and social work teams to assist in patient care and discharge planning. As of this writing, we have cared for 543 patients, sent to us from one-third of Maryland’s hospitals. Use during the first wave of COVID was variable, with some hospitals sending us just a few patients. One Baltimore hospital sent us 8% of its COVID-19 patients. Because the patients have an average 5-day stay, the BCCFH has offloaded 2,600 bed-days of care from acute hospitals.

COVID-19 field hospitals must be prepared to respond effectively to decompensating patients. In our experience, effective RRTs provide a standard and reproducible approach to patient emergencies. In the conventional hospital setting, these teams consist of clinicians who can be called on by any healthcare worker to quickly assess deteriorating patients and intervene with treatment. The purpose of an RRT is to provide immediate care to a patient before progression to respiratory or cardiac arrest. RRTs proliferated in US hospitals after 2004 when the Institute for Healthcare Improvement in Boston, Massachusetts, recommended such teams for improved quality of care. Though studies report conflicting findings on the impact of RRTs on mortality rates, these studies were performed in traditional hospitals with ample resources, consultants, and clinicians familiar with their patients rather than in resource-limited field hospitals.^4-13 Our field hospital has found RRTs, and the principles behind them, useful in the identification and management of decompensating COVID-19 patients.

An approach to managing decompensating patients in a COVID-19 field hospital can be considered in four phases: identification, assessment, resuscitation, and transport. Referring to these phases, the first case shows opportunities for improvement in resuscitation and transport. Although decompensation was identified, the patient was not transported to the triage bay for resuscitation, and there was confusion when trying to obtain the proper equipment. Additionally, EMS awaited the patient in the triage bay, while he remained in his cubicle, which delayed transport to an acute care hospital. The second case shows opportunities for improvement in identification and assessment. The patient had signs of impending decompensation that were not immediately recognized and treated. However, once decompensation occurred, the RRT was called and the patient was transported quickly to the triage bay, and then to the hospital via EMS.

In our experience at the BCCFH, identification is a key phase in COVID-19 care at a field hospital. Identification involves recognizing impending deterioration, as well as understanding risk factors for decompensation. For COVID-19 specifically, this requires heightened awareness of patients who are in the 2nd to 3rd week of symptoms. Data from Wuhan, China, suggest that decompensation occurs predictably around symptom day 9.^14,15 At the BCCFH, the median symptom duration for patients who decompensated and returned to a hospital was 13 days. In both introductory cases, patients were in the high-risk 2nd week of symptoms when decompensation occurred. Clinicians at the BCCFH now discuss patient symptom day during their handoffs, when rounding, and when making decisions regarding acute care transfer. Our team has also integrated clinical information from our electronic health record to create a dashboard describing those patients requiring acute care transfer to assist in identifying other trends or predictive factors (Appendix D).

Although RRTs are designed to activate when an individual patient decompensates, they should fit within a larger operational framework for patient safety. Our experience with emergencies at the BCCFH has yielded four opportunities for learning relevant to COVID-19 care in nontraditional settings (Table). These lessons include how to update staff on clinical process changes, unify communication systems, create a clinical drilling culture, and review cases to improve performance. They illustrate the importance of standardizing emergency processes, conducting frequent updates and drills, and ensuring continuous improvement. We found that, while caring for patients with an unpredictable, novel disease in a nontraditional setting and while wearing PPE and working with new colleagues during every shift, the best approach to support patients and staff is to anticipate emergencies rather than relying on individual staff to develop on-the-spot solutions.

The COVID-19 era has seen the unprecedented construction and utilization of emergency field hospital facilities. Such facilities can serve to offload some COVID-19 patients from strained healthcare infrastructure and provide essential care to these patients. We share many of the unique physical and logistical considerations specific to a nontraditional site. We optimized our space, our equipment, and our communication system. We learned how to identify, assess, resuscitate, and transport decompensating COVID-19 patients. Ultimately, our field hospital has been well utilized and successful at caring for patients because of its adaptability, accessibility, and safety record. Of the 15% of patients we transferred to a hospital for care, 81% were successfully stabilized and were willing to return to the BCCFH to complete their care. Our design included supportive care such as social work, physical and occupational therapy, and treatment of comorbidities, such as diabetes and substance use disorder. Our model demonstrates an effective nonhospital option for the care of lower-acuity, medically complex COVID-19 patients. If such facilities are used in subsequent COVID-19 outbreaks, we advise structured planning for the care of decompensating patients that takes into account the need for effective communication, drilling, and ongoing process improvement.

Pediatric Hospital Medicine (PHM) only recently became a recognized pediatric subspecialty with the first certification exam taking place in 2019. As a new field composed largely of women, it has a unique opportunity to set the example of how to operationalize gender equity in leadership by tracking metrics, creating intentional processes for hiring and promotion, and implementing policies in a transparent way.

In this issue of the Journal of Hospital Medicine, Allan et al¹ report that women, who comprise 70% of the field, appear proportionally represented in associate/assistant but not senior leadership roles when compared to the PHM field at large. Eighty-one percent of associate division directors but only 55% of division directors were women, and 82% of assistant fellowship directors but only 66% of fellowship directors were women. These downward trends in the proportion of women in leadership roles as the roles become more senior is not an unfamiliar pattern. This echoes academic pediatric positions more broadly: women’s representation slides from 63% of active physicians to approximately 57% active faculty and then to 26% as department chairs.² The same story holds true for deans’ offices in US medical schools, where 34% of associate deans are women and yet only 18% of deans are women. The number of women deans has only increased by about one each year, on average, since 2009.³ C-suite leadership roles in healthcare mimic this same downward trajectory.⁴ Burden et al found that while there was equal gender representation of hospitalists and general internists who worked in university hospitals, women led only a minority of (adult) hospital medicine (16%) or general internal medicine (35%) sections or divisions at university hospitals.⁵ Women with intersectionality, such as Black women and other women of color, are even more grossly underrepresented in leadership roles.

How can we change this pattern to ensure that leadership in PHM, and in medicine in general, represents diverse voices and reflects the community it serves? Allan et al have established an important baseline for tracking gender equity in PHM. Institutions, organizations, and societies must now prioritize, value and promote a culture of diversity, inclusivity, sponsorship, and allyship. For example, institutions can create and enforce policies in which compensation and promotion are tied to a leader’s achievement of transparent gender equity and diversity targets to ensure accountability. Institutions should commit dedicated and substantive funding to diversity, equity, and inclusion efforts and provide a regular diversity report that tracks gender distribution, hiring and attrition, and representation in leadership. Institutions should implement “best search practices” for all leadership positions. Additionally, all faculty should receive regular and ongoing professional development planning to enhance academic productivity and professional satisfaction and improve retention.

Women in medicine disproportionately experience many issues, including harassment, bias, and childcare and household responsibilities, that adversely affect their career trajectory. PHM is in a unique position to trailblaze a new framework for ensuring gender equity in its field. Let’s not lose this opportunity to set a new course that other specialties can follow.

Pediatric Hospital Medicine (PHM) only recently became a recognized pediatric subspecialty with the first certification exam taking place in 2019. As a new field composed largely of women, it has a unique opportunity to set the example of how to operationalize gender equity in leadership by tracking metrics, creating intentional processes for hiring and promotion, and implementing policies in a transparent way.

In this issue of the Journal of Hospital Medicine, Allan et al¹ report that women, who comprise 70% of the field, appear proportionally represented in associate/assistant but not senior leadership roles when compared to the PHM field at large. Eighty-one percent of associate division directors but only 55% of division directors were women, and 82% of assistant fellowship directors but only 66% of fellowship directors were women. These downward trends in the proportion of women in leadership roles as the roles become more senior is not an unfamiliar pattern. This echoes academic pediatric positions more broadly: women’s representation slides from 63% of active physicians to approximately 57% active faculty and then to 26% as department chairs.² The same story holds true for deans’ offices in US medical schools, where 34% of associate deans are women and yet only 18% of deans are women. The number of women deans has only increased by about one each year, on average, since 2009.³ C-suite leadership roles in healthcare mimic this same downward trajectory.⁴ Burden et al found that while there was equal gender representation of hospitalists and general internists who worked in university hospitals, women led only a minority of (adult) hospital medicine (16%) or general internal medicine (35%) sections or divisions at university hospitals.⁵ Women with intersectionality, such as Black women and other women of color, are even more grossly underrepresented in leadership roles.

How can we change this pattern to ensure that leadership in PHM, and in medicine in general, represents diverse voices and reflects the community it serves? Allan et al have established an important baseline for tracking gender equity in PHM. Institutions, organizations, and societies must now prioritize, value and promote a culture of diversity, inclusivity, sponsorship, and allyship. For example, institutions can create and enforce policies in which compensation and promotion are tied to a leader’s achievement of transparent gender equity and diversity targets to ensure accountability. Institutions should commit dedicated and substantive funding to diversity, equity, and inclusion efforts and provide a regular diversity report that tracks gender distribution, hiring and attrition, and representation in leadership. Institutions should implement “best search practices” for all leadership positions. Additionally, all faculty should receive regular and ongoing professional development planning to enhance academic productivity and professional satisfaction and improve retention.

Women in medicine disproportionately experience many issues, including harassment, bias, and childcare and household responsibilities, that adversely affect their career trajectory. PHM is in a unique position to trailblaze a new framework for ensuring gender equity in its field. Let’s not lose this opportunity to set a new course that other specialties can follow.

Pediatric Hospital Medicine (PHM) only recently became a recognized pediatric subspecialty with the first certification exam taking place in 2019. As a new field composed largely of women, it has a unique opportunity to set the example of how to operationalize gender equity in leadership by tracking metrics, creating intentional processes for hiring and promotion, and implementing policies in a transparent way.

In this issue of the Journal of Hospital Medicine, Allan et al¹ report that women, who comprise 70% of the field, appear proportionally represented in associate/assistant but not senior leadership roles when compared to the PHM field at large. Eighty-one percent of associate division directors but only 55% of division directors were women, and 82% of assistant fellowship directors but only 66% of fellowship directors were women. These downward trends in the proportion of women in leadership roles as the roles become more senior is not an unfamiliar pattern. This echoes academic pediatric positions more broadly: women’s representation slides from 63% of active physicians to approximately 57% active faculty and then to 26% as department chairs.² The same story holds true for deans’ offices in US medical schools, where 34% of associate deans are women and yet only 18% of deans are women. The number of women deans has only increased by about one each year, on average, since 2009.³ C-suite leadership roles in healthcare mimic this same downward trajectory.⁴ Burden et al found that while there was equal gender representation of hospitalists and general internists who worked in university hospitals, women led only a minority of (adult) hospital medicine (16%) or general internal medicine (35%) sections or divisions at university hospitals.⁵ Women with intersectionality, such as Black women and other women of color, are even more grossly underrepresented in leadership roles.

How can we change this pattern to ensure that leadership in PHM, and in medicine in general, represents diverse voices and reflects the community it serves? Allan et al have established an important baseline for tracking gender equity in PHM. Institutions, organizations, and societies must now prioritize, value and promote a culture of diversity, inclusivity, sponsorship, and allyship. For example, institutions can create and enforce policies in which compensation and promotion are tied to a leader’s achievement of transparent gender equity and diversity targets to ensure accountability. Institutions should commit dedicated and substantive funding to diversity, equity, and inclusion efforts and provide a regular diversity report that tracks gender distribution, hiring and attrition, and representation in leadership. Institutions should implement “best search practices” for all leadership positions. Additionally, all faculty should receive regular and ongoing professional development planning to enhance academic productivity and professional satisfaction and improve retention.

Women in medicine disproportionately experience many issues, including harassment, bias, and childcare and household responsibilities, that adversely affect their career trajectory. PHM is in a unique position to trailblaze a new framework for ensuring gender equity in its field. Let’s not lose this opportunity to set a new course that other specialties can follow.