Hospital Medicine

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.

Nearly 30% of healthcare spending may relate to overuse of unnecessary medical interventions.¹ Deimplementation of such practices can reduce negative outcomes and unnecessary costs.² Nonetheless, changing practice is difficult. Why is it so hard to stop doing things that don’t work? A variety of factors influences deimplementation, and research aiming to identify and understand these factors can promote the delivery of more appropriate care.²

In this issue, Wolk et al describe barriers and facilitators in deimplementing non-guideline adherent use of continuous pulse oximetry (CPO) in pediatric patients with bronchiolitis not requiring supplemental oxygen.³ Unnecessary CPO use for these patients is associated with increased hospitalization rates, length of stay, alarm fatigue, and costs, without evidence of improved clinical outcomes. Despite these data, many hospitals participating in the multicenter Eliminating Monitor Overuse study struggled to decrease CPO usage. The authors conducted semistructured interviews with a broad range of stakeholders from 12 hospitals, representing a variety of institutions with low and high CPO utilization rates.

Specific barriers to deimplementation included institutional factors, eg, unclear or missing guidelines, a culture of high utilization, and challenges educating medical staff. Perceived parental discomfort with stopping CPO was also observed. Four key facilitators were noted: strong institutional leadership, evidence-based guidelines, electronic health record order sets or reminders, and clear institutional policy. These results are similar to other deimplementation studies.

A commonality to deimplementation studies is the difficulty of changing practice. Much like implementation, deimplementation requires multipronged approaches that are sensitive to contextual factors. Interventions must account for local conditions, such as resource availability, practice norms, current workflows and processes of care, relationships among clinicians, and leadership, to create feasible and sustainable change.

Deimplementation may be even more challenging than implementation of new practices, however, because of loss aversion—the tendency to prefer avoiding losses to acquiring equivalent gains. “Taking away” something that clinicians are used to, even when proven to not be helpful, can feel uncomfortable, hindering adoption. Rather than simply discontinuing a practice, replacing it with a better option may help to overcome behavioral inertia and motivate change.

Underscoring the importance of local influences, clinicians often respond more to their close colleagues’ practices than to knowledge of national guidelines. Leveraging existing peer networks can facilitate collaboration, learning, and behavior change.⁴ Nudge strategies, in which local contexts are primed to promote desired behaviors, are also increasingly used.⁴ Priming has been effective in deimplementation efforts in medication prescribing and diagnostic testing.⁴

Including patients’ and families’ perspectives in deimplementation research is critical to practice change. Because diagnostic and treatment plans occur in the context of collaborative decision-making with patients, caregivers, and families, these groups are critical to engage in deimplementation efforts.

Hospitalists’ efforts at the front line of improvement require us to become more proficient in not only adopting evidence-based practices, but also in discontinuing ineffective ones. Identifying what we should stop doing is only the first step. Deimplementation is critical to this effort. Wolk et al provide insights into factors that influence deimplementation success. However, more work is needed, particularly regarding adapting approaches to local contexts, minimizing perceived loss, leveraging local conditions to shape behavior, and partnering with patients and families to achieve higher-value care.

Nearly 30% of healthcare spending may relate to overuse of unnecessary medical interventions.¹ Deimplementation of such practices can reduce negative outcomes and unnecessary costs.² Nonetheless, changing practice is difficult. Why is it so hard to stop doing things that don’t work? A variety of factors influences deimplementation, and research aiming to identify and understand these factors can promote the delivery of more appropriate care.²

In this issue, Wolk et al describe barriers and facilitators in deimplementing non-guideline adherent use of continuous pulse oximetry (CPO) in pediatric patients with bronchiolitis not requiring supplemental oxygen.³ Unnecessary CPO use for these patients is associated with increased hospitalization rates, length of stay, alarm fatigue, and costs, without evidence of improved clinical outcomes. Despite these data, many hospitals participating in the multicenter Eliminating Monitor Overuse study struggled to decrease CPO usage. The authors conducted semistructured interviews with a broad range of stakeholders from 12 hospitals, representing a variety of institutions with low and high CPO utilization rates.

Specific barriers to deimplementation included institutional factors, eg, unclear or missing guidelines, a culture of high utilization, and challenges educating medical staff. Perceived parental discomfort with stopping CPO was also observed. Four key facilitators were noted: strong institutional leadership, evidence-based guidelines, electronic health record order sets or reminders, and clear institutional policy. These results are similar to other deimplementation studies.

A commonality to deimplementation studies is the difficulty of changing practice. Much like implementation, deimplementation requires multipronged approaches that are sensitive to contextual factors. Interventions must account for local conditions, such as resource availability, practice norms, current workflows and processes of care, relationships among clinicians, and leadership, to create feasible and sustainable change.

Deimplementation may be even more challenging than implementation of new practices, however, because of loss aversion—the tendency to prefer avoiding losses to acquiring equivalent gains. “Taking away” something that clinicians are used to, even when proven to not be helpful, can feel uncomfortable, hindering adoption. Rather than simply discontinuing a practice, replacing it with a better option may help to overcome behavioral inertia and motivate change.

Underscoring the importance of local influences, clinicians often respond more to their close colleagues’ practices than to knowledge of national guidelines. Leveraging existing peer networks can facilitate collaboration, learning, and behavior change.⁴ Nudge strategies, in which local contexts are primed to promote desired behaviors, are also increasingly used.⁴ Priming has been effective in deimplementation efforts in medication prescribing and diagnostic testing.⁴

Including patients’ and families’ perspectives in deimplementation research is critical to practice change. Because diagnostic and treatment plans occur in the context of collaborative decision-making with patients, caregivers, and families, these groups are critical to engage in deimplementation efforts.

Hospitalists’ efforts at the front line of improvement require us to become more proficient in not only adopting evidence-based practices, but also in discontinuing ineffective ones. Identifying what we should stop doing is only the first step. Deimplementation is critical to this effort. Wolk et al provide insights into factors that influence deimplementation success. However, more work is needed, particularly regarding adapting approaches to local contexts, minimizing perceived loss, leveraging local conditions to shape behavior, and partnering with patients and families to achieve higher-value care.

Nearly 30% of healthcare spending may relate to overuse of unnecessary medical interventions.¹ Deimplementation of such practices can reduce negative outcomes and unnecessary costs.² Nonetheless, changing practice is difficult. Why is it so hard to stop doing things that don’t work? A variety of factors influences deimplementation, and research aiming to identify and understand these factors can promote the delivery of more appropriate care.²

In this issue, Wolk et al describe barriers and facilitators in deimplementing non-guideline adherent use of continuous pulse oximetry (CPO) in pediatric patients with bronchiolitis not requiring supplemental oxygen.³ Unnecessary CPO use for these patients is associated with increased hospitalization rates, length of stay, alarm fatigue, and costs, without evidence of improved clinical outcomes. Despite these data, many hospitals participating in the multicenter Eliminating Monitor Overuse study struggled to decrease CPO usage. The authors conducted semistructured interviews with a broad range of stakeholders from 12 hospitals, representing a variety of institutions with low and high CPO utilization rates.

Specific barriers to deimplementation included institutional factors, eg, unclear or missing guidelines, a culture of high utilization, and challenges educating medical staff. Perceived parental discomfort with stopping CPO was also observed. Four key facilitators were noted: strong institutional leadership, evidence-based guidelines, electronic health record order sets or reminders, and clear institutional policy. These results are similar to other deimplementation studies.

A commonality to deimplementation studies is the difficulty of changing practice. Much like implementation, deimplementation requires multipronged approaches that are sensitive to contextual factors. Interventions must account for local conditions, such as resource availability, practice norms, current workflows and processes of care, relationships among clinicians, and leadership, to create feasible and sustainable change.

Deimplementation may be even more challenging than implementation of new practices, however, because of loss aversion—the tendency to prefer avoiding losses to acquiring equivalent gains. “Taking away” something that clinicians are used to, even when proven to not be helpful, can feel uncomfortable, hindering adoption. Rather than simply discontinuing a practice, replacing it with a better option may help to overcome behavioral inertia and motivate change.

Underscoring the importance of local influences, clinicians often respond more to their close colleagues’ practices than to knowledge of national guidelines. Leveraging existing peer networks can facilitate collaboration, learning, and behavior change.⁴ Nudge strategies, in which local contexts are primed to promote desired behaviors, are also increasingly used.⁴ Priming has been effective in deimplementation efforts in medication prescribing and diagnostic testing.⁴

Including patients’ and families’ perspectives in deimplementation research is critical to practice change. Because diagnostic and treatment plans occur in the context of collaborative decision-making with patients, caregivers, and families, these groups are critical to engage in deimplementation efforts.

Hospitalists’ efforts at the front line of improvement require us to become more proficient in not only adopting evidence-based practices, but also in discontinuing ineffective ones. Identifying what we should stop doing is only the first step. Deimplementation is critical to this effort. Wolk et al provide insights into factors that influence deimplementation success. However, more work is needed, particularly regarding adapting approaches to local contexts, minimizing perceived loss, leveraging local conditions to shape behavior, and partnering with patients and families to achieve higher-value care.

In recent years, there has been increased scrutiny of transitions of care in medicine, particularly at hospital discharge. Much focus has been on preventing readmissions, motivated at least in part by the Affordable Care Act’s Hospital Readmissions Reduction Program, which financially penalizes hospitals for higher-than-expected readmission rates.¹ However, the problem of transition from hospital to home is not just a readmissions issue—it is a quality and patient safety issue.² Therefore, measuring readmissions alone is inadequate. More effective systems for transition from hospital to home are needed in order to deliver high-quality care that actually restores patient well-being after hospitalization.

In this month’s issue of Journal of Hospital Medicine, Schnipper and Samal, et al report the results of a stepped-wedge randomized trial examining the effect of a multifaceted intervention on postdischarge patient-centered outcomes when compared with usual care.³ At 30 days after discharge, adverse events were reduced from 23 per 100 patients in the usual care group to 18 per 100 patients in the intervention group, with an incidence rate ratio of 0.55 (95% CI, 0.35-0.84) after adjustment for study month and baseline characteristics. Interestingly, there was no statistically significant difference in nonelective readmissions, and penetrance was notably poor: The majority of components of the intervention were received by fewer than half of intended patients, and 13% failed to receive any component at all.

With such incomplete implementation, what explains the reduction in adverse events? To best answer this, it is helpful to recognize the transition from hospital to home as a complex problem rather than a complicated one.⁴ The difference is key. Complicated problems follow a predictable set of rules that can be thought of and planned for, and when the plan is methodically followed, complicated problems can be solved. Complex problems, on the other hand, have a more unpredictable interplay between multiple nonindependent and sometimes unknown factors. Complex problems cannot be solved by merely following a well-designed plan; rather, they require tremendous preparation, adaptability, and active management as the problem plays itself out.

Fortunately, Schnipper and Samal, et al properly identified the problem of transition from hospital to home as complex and approached it from a complexity mindset. In their design of a multifaceted intervention, they aimed high and cast a wide net. Understanding that different practices have different cultures and resources, they standardized the function of the intervention components rather than the exact form. As the trial progressed, they allowed for modification of the intervention, incorporating input from multiple stakeholders and feedback from early failures. Thus, by recognizing and embracing the complexity of the problem, the authors set themselves and their patients up for success. The most likely explanation for the observed effect of the intervention on this complex problem is therefore quite simple: The study design allowed for the components most likely to work to be most readily implemented on a patient-by-patient and practice-by-practice basis.

While the trial aims to imitate the “real world,” it does not leave clear-cut answers for real healthcare professionals. Without knowing if any individual component of the intervention was effective on its own, it may be difficult for institutions to justify the cost of implementation. And while there should be adequate incentive to action for any intervention that improves how patients function or feel, without a reduction in readmissions, the financial downside may in some instances be prohibitive.

Despite these limitations, the path forward is clear. Institutions looking to implement a similar program now should approach the problem with a complexity mindset, even if their downstream interventions may differ. Researchers looking to design similar trials should focus on narrowing the scope of the intervention while maintaining a complexity mindset, which might help lead to more widespread implementation of evidence-based interventions in the future. In teaching us more about the approach to finding a solution than the solution itself, the present study marks an important next step in hospital to home transitions of care and transitions-of-care research.

In recent years, there has been increased scrutiny of transitions of care in medicine, particularly at hospital discharge. Much focus has been on preventing readmissions, motivated at least in part by the Affordable Care Act’s Hospital Readmissions Reduction Program, which financially penalizes hospitals for higher-than-expected readmission rates.¹ However, the problem of transition from hospital to home is not just a readmissions issue—it is a quality and patient safety issue.² Therefore, measuring readmissions alone is inadequate. More effective systems for transition from hospital to home are needed in order to deliver high-quality care that actually restores patient well-being after hospitalization.

In this month’s issue of Journal of Hospital Medicine, Schnipper and Samal, et al report the results of a stepped-wedge randomized trial examining the effect of a multifaceted intervention on postdischarge patient-centered outcomes when compared with usual care.³ At 30 days after discharge, adverse events were reduced from 23 per 100 patients in the usual care group to 18 per 100 patients in the intervention group, with an incidence rate ratio of 0.55 (95% CI, 0.35-0.84) after adjustment for study month and baseline characteristics. Interestingly, there was no statistically significant difference in nonelective readmissions, and penetrance was notably poor: The majority of components of the intervention were received by fewer than half of intended patients, and 13% failed to receive any component at all.

With such incomplete implementation, what explains the reduction in adverse events? To best answer this, it is helpful to recognize the transition from hospital to home as a complex problem rather than a complicated one.⁴ The difference is key. Complicated problems follow a predictable set of rules that can be thought of and planned for, and when the plan is methodically followed, complicated problems can be solved. Complex problems, on the other hand, have a more unpredictable interplay between multiple nonindependent and sometimes unknown factors. Complex problems cannot be solved by merely following a well-designed plan; rather, they require tremendous preparation, adaptability, and active management as the problem plays itself out.

Fortunately, Schnipper and Samal, et al properly identified the problem of transition from hospital to home as complex and approached it from a complexity mindset. In their design of a multifaceted intervention, they aimed high and cast a wide net. Understanding that different practices have different cultures and resources, they standardized the function of the intervention components rather than the exact form. As the trial progressed, they allowed for modification of the intervention, incorporating input from multiple stakeholders and feedback from early failures. Thus, by recognizing and embracing the complexity of the problem, the authors set themselves and their patients up for success. The most likely explanation for the observed effect of the intervention on this complex problem is therefore quite simple: The study design allowed for the components most likely to work to be most readily implemented on a patient-by-patient and practice-by-practice basis.

While the trial aims to imitate the “real world,” it does not leave clear-cut answers for real healthcare professionals. Without knowing if any individual component of the intervention was effective on its own, it may be difficult for institutions to justify the cost of implementation. And while there should be adequate incentive to action for any intervention that improves how patients function or feel, without a reduction in readmissions, the financial downside may in some instances be prohibitive.

Despite these limitations, the path forward is clear. Institutions looking to implement a similar program now should approach the problem with a complexity mindset, even if their downstream interventions may differ. Researchers looking to design similar trials should focus on narrowing the scope of the intervention while maintaining a complexity mindset, which might help lead to more widespread implementation of evidence-based interventions in the future. In teaching us more about the approach to finding a solution than the solution itself, the present study marks an important next step in hospital to home transitions of care and transitions-of-care research.

In recent years, there has been increased scrutiny of transitions of care in medicine, particularly at hospital discharge. Much focus has been on preventing readmissions, motivated at least in part by the Affordable Care Act’s Hospital Readmissions Reduction Program, which financially penalizes hospitals for higher-than-expected readmission rates.¹ However, the problem of transition from hospital to home is not just a readmissions issue—it is a quality and patient safety issue.² Therefore, measuring readmissions alone is inadequate. More effective systems for transition from hospital to home are needed in order to deliver high-quality care that actually restores patient well-being after hospitalization.

In this month’s issue of Journal of Hospital Medicine, Schnipper and Samal, et al report the results of a stepped-wedge randomized trial examining the effect of a multifaceted intervention on postdischarge patient-centered outcomes when compared with usual care.³ At 30 days after discharge, adverse events were reduced from 23 per 100 patients in the usual care group to 18 per 100 patients in the intervention group, with an incidence rate ratio of 0.55 (95% CI, 0.35-0.84) after adjustment for study month and baseline characteristics. Interestingly, there was no statistically significant difference in nonelective readmissions, and penetrance was notably poor: The majority of components of the intervention were received by fewer than half of intended patients, and 13% failed to receive any component at all.

With such incomplete implementation, what explains the reduction in adverse events? To best answer this, it is helpful to recognize the transition from hospital to home as a complex problem rather than a complicated one.⁴ The difference is key. Complicated problems follow a predictable set of rules that can be thought of and planned for, and when the plan is methodically followed, complicated problems can be solved. Complex problems, on the other hand, have a more unpredictable interplay between multiple nonindependent and sometimes unknown factors. Complex problems cannot be solved by merely following a well-designed plan; rather, they require tremendous preparation, adaptability, and active management as the problem plays itself out.

Fortunately, Schnipper and Samal, et al properly identified the problem of transition from hospital to home as complex and approached it from a complexity mindset. In their design of a multifaceted intervention, they aimed high and cast a wide net. Understanding that different practices have different cultures and resources, they standardized the function of the intervention components rather than the exact form. As the trial progressed, they allowed for modification of the intervention, incorporating input from multiple stakeholders and feedback from early failures. Thus, by recognizing and embracing the complexity of the problem, the authors set themselves and their patients up for success. The most likely explanation for the observed effect of the intervention on this complex problem is therefore quite simple: The study design allowed for the components most likely to work to be most readily implemented on a patient-by-patient and practice-by-practice basis.

While the trial aims to imitate the “real world,” it does not leave clear-cut answers for real healthcare professionals. Without knowing if any individual component of the intervention was effective on its own, it may be difficult for institutions to justify the cost of implementation. And while there should be adequate incentive to action for any intervention that improves how patients function or feel, without a reduction in readmissions, the financial downside may in some instances be prohibitive.

Despite these limitations, the path forward is clear. Institutions looking to implement a similar program now should approach the problem with a complexity mindset, even if their downstream interventions may differ. Researchers looking to design similar trials should focus on narrowing the scope of the intervention while maintaining a complexity mindset, which might help lead to more widespread implementation of evidence-based interventions in the future. In teaching us more about the approach to finding a solution than the solution itself, the present study marks an important next step in hospital to home transitions of care and transitions-of-care research.

The early days of the coronavirus disease 2019 (COVID-19) pandemic were fraught with uncertainty as hospitalists struggled to develop standards of care for noncritically ill patients. Although data were available from intensive care units (ICUs) in Asia and Europe, it was unclear whether these findings applied to the acute but noncritically ill patients who would ultimately make up most coronavirus admissions. Which therapeutics could benefit these patients? Who needs continuous cardiopulmonary monitoring? And perhaps most importantly, which patients are at risk for clinical deterioration?

In this issue, Nemer et al begin to answer these questions using a retrospective analysis of 350 noncritically ill COVID-19 patients admitted to non-ICU care at Cleveland Clinic hospitals in Ohio and Florida between March 13 and May 1, 2020.¹ The primary outcome was a composite of three endpoints: increased respiratory support (high-flow nasal cannula, noninvasive positive pressure ventilation, or intubation), ICU transfer, or death. The primary outcome occurred in 18% of all patients and the risk was greatest among patients with high admission levels of C-reactive protein (CRP). This analysis found that while clinically significant arrhythmias occurred in 14% of patients, 90% of those were in patients with either known cardiac disease or an elevated admission troponin T level and in only one case (<1%) necessitated transition to a higher level of care. Overall mortality for COVID-19 patients initially admitted to non-ICU settings was 3%.

While several tests have been proposed as clinically relevant to coronavirus disease, those recommendations are based on studies performed on critically ill patients outside of the US and have focused on survival, not clinical deterioration.^2,3 In their cohort of noncritically ill patients in the US, Nemer et al found that not only is CRP associated with clinical worsening, but that increasing levels of CRP are associated with increasing risk of deterioration. Perhaps even more interesting was the finding that no patient with a normal CRP suffered the composite outcome, including death. The authors did not report levels of other laboratory tests that have been associated with severe coronavirus disease, such as platelets, fibrin degradation products, or prolonged prothrombin time/activated partial thromboplastin time. As many clinicians will note, CRP’s lack of specificity may be its Achilles heel, potentially lowering its prognostic value. Still, given its wide availability, low cost, and rapid turnaround, CRP could serve as a screening tool to risk stratify admitted coronavirus patients, while also providing reassurance when it is normal.

The results of this study could also impact use of hospital resources. The findings regarding the low risk of arrhythmias provide support for limiting the use of continuous cardiac monitoring in noncritically ill patients without previous histories of cardiac disease or elevated admission troponin levels. Patients with normal admission CRP levels could potentially be monitored safely with intermittent pulse oximetry. Continuous pulse oximetry and cardiac monitoring are already overused in many hospitals, and in the case of coronavirus the implications are even more significant given the importance of minimizing unnecessary healthcare worker exposures.

The vast majority (79% to 90%) of patients hospitalized for coronavirus will be cared for in non–ICU settings,^4,5 yet most research has thus far focused on ICU patients. Nemer et al provide much-needed information on how to care for the noncritically ill coronavirus patients whom hospitalists are most likely to treat. As a resurgence of infections is expected this winter, this work has the potential to help physicians identify not only those who have the highest probability of deteriorating, but also those who may not. In a world of limited resources, knowing which patient is unlikely to deteriorate may be just as important as recognizing which one is.

The early days of the coronavirus disease 2019 (COVID-19) pandemic were fraught with uncertainty as hospitalists struggled to develop standards of care for noncritically ill patients. Although data were available from intensive care units (ICUs) in Asia and Europe, it was unclear whether these findings applied to the acute but noncritically ill patients who would ultimately make up most coronavirus admissions. Which therapeutics could benefit these patients? Who needs continuous cardiopulmonary monitoring? And perhaps most importantly, which patients are at risk for clinical deterioration?

In this issue, Nemer et al begin to answer these questions using a retrospective analysis of 350 noncritically ill COVID-19 patients admitted to non-ICU care at Cleveland Clinic hospitals in Ohio and Florida between March 13 and May 1, 2020.¹ The primary outcome was a composite of three endpoints: increased respiratory support (high-flow nasal cannula, noninvasive positive pressure ventilation, or intubation), ICU transfer, or death. The primary outcome occurred in 18% of all patients and the risk was greatest among patients with high admission levels of C-reactive protein (CRP). This analysis found that while clinically significant arrhythmias occurred in 14% of patients, 90% of those were in patients with either known cardiac disease or an elevated admission troponin T level and in only one case (<1%) necessitated transition to a higher level of care. Overall mortality for COVID-19 patients initially admitted to non-ICU settings was 3%.

While several tests have been proposed as clinically relevant to coronavirus disease, those recommendations are based on studies performed on critically ill patients outside of the US and have focused on survival, not clinical deterioration.^2,3 In their cohort of noncritically ill patients in the US, Nemer et al found that not only is CRP associated with clinical worsening, but that increasing levels of CRP are associated with increasing risk of deterioration. Perhaps even more interesting was the finding that no patient with a normal CRP suffered the composite outcome, including death. The authors did not report levels of other laboratory tests that have been associated with severe coronavirus disease, such as platelets, fibrin degradation products, or prolonged prothrombin time/activated partial thromboplastin time. As many clinicians will note, CRP’s lack of specificity may be its Achilles heel, potentially lowering its prognostic value. Still, given its wide availability, low cost, and rapid turnaround, CRP could serve as a screening tool to risk stratify admitted coronavirus patients, while also providing reassurance when it is normal.

The results of this study could also impact use of hospital resources. The findings regarding the low risk of arrhythmias provide support for limiting the use of continuous cardiac monitoring in noncritically ill patients without previous histories of cardiac disease or elevated admission troponin levels. Patients with normal admission CRP levels could potentially be monitored safely with intermittent pulse oximetry. Continuous pulse oximetry and cardiac monitoring are already overused in many hospitals, and in the case of coronavirus the implications are even more significant given the importance of minimizing unnecessary healthcare worker exposures.

The vast majority (79% to 90%) of patients hospitalized for coronavirus will be cared for in non–ICU settings,^4,5 yet most research has thus far focused on ICU patients. Nemer et al provide much-needed information on how to care for the noncritically ill coronavirus patients whom hospitalists are most likely to treat. As a resurgence of infections is expected this winter, this work has the potential to help physicians identify not only those who have the highest probability of deteriorating, but also those who may not. In a world of limited resources, knowing which patient is unlikely to deteriorate may be just as important as recognizing which one is.

The early days of the coronavirus disease 2019 (COVID-19) pandemic were fraught with uncertainty as hospitalists struggled to develop standards of care for noncritically ill patients. Although data were available from intensive care units (ICUs) in Asia and Europe, it was unclear whether these findings applied to the acute but noncritically ill patients who would ultimately make up most coronavirus admissions. Which therapeutics could benefit these patients? Who needs continuous cardiopulmonary monitoring? And perhaps most importantly, which patients are at risk for clinical deterioration?

In this issue, Nemer et al begin to answer these questions using a retrospective analysis of 350 noncritically ill COVID-19 patients admitted to non-ICU care at Cleveland Clinic hospitals in Ohio and Florida between March 13 and May 1, 2020.¹ The primary outcome was a composite of three endpoints: increased respiratory support (high-flow nasal cannula, noninvasive positive pressure ventilation, or intubation), ICU transfer, or death. The primary outcome occurred in 18% of all patients and the risk was greatest among patients with high admission levels of C-reactive protein (CRP). This analysis found that while clinically significant arrhythmias occurred in 14% of patients, 90% of those were in patients with either known cardiac disease or an elevated admission troponin T level and in only one case (<1%) necessitated transition to a higher level of care. Overall mortality for COVID-19 patients initially admitted to non-ICU settings was 3%.

While several tests have been proposed as clinically relevant to coronavirus disease, those recommendations are based on studies performed on critically ill patients outside of the US and have focused on survival, not clinical deterioration.^2,3 In their cohort of noncritically ill patients in the US, Nemer et al found that not only is CRP associated with clinical worsening, but that increasing levels of CRP are associated with increasing risk of deterioration. Perhaps even more interesting was the finding that no patient with a normal CRP suffered the composite outcome, including death. The authors did not report levels of other laboratory tests that have been associated with severe coronavirus disease, such as platelets, fibrin degradation products, or prolonged prothrombin time/activated partial thromboplastin time. As many clinicians will note, CRP’s lack of specificity may be its Achilles heel, potentially lowering its prognostic value. Still, given its wide availability, low cost, and rapid turnaround, CRP could serve as a screening tool to risk stratify admitted coronavirus patients, while also providing reassurance when it is normal.

The results of this study could also impact use of hospital resources. The findings regarding the low risk of arrhythmias provide support for limiting the use of continuous cardiac monitoring in noncritically ill patients without previous histories of cardiac disease or elevated admission troponin levels. Patients with normal admission CRP levels could potentially be monitored safely with intermittent pulse oximetry. Continuous pulse oximetry and cardiac monitoring are already overused in many hospitals, and in the case of coronavirus the implications are even more significant given the importance of minimizing unnecessary healthcare worker exposures.

The vast majority (79% to 90%) of patients hospitalized for coronavirus will be cared for in non–ICU settings,^4,5 yet most research has thus far focused on ICU patients. Nemer et al provide much-needed information on how to care for the noncritically ill coronavirus patients whom hospitalists are most likely to treat. As a resurgence of infections is expected this winter, this work has the potential to help physicians identify not only those who have the highest probability of deteriorating, but also those who may not. In a world of limited resources, knowing which patient is unlikely to deteriorate may be just as important as recognizing which one is.

“Our goals can only be reached through the vehicle of a plan. There is no other route to success.”

—Pablo Picasso

Whether it be a research manuscript, quality improvement (QI) initiative, or educational curriculum, busy clinicians often struggle getting projects past the idea stage. Barriers to completion, such as a busy clinical schedule or lack of experience and mentorship, are well known. Importantly, these projects serve as “academic currency” used for promotion and advancement and also create generalizable knowledge, which can help others improve clinical practice or operational processes. Those who are successful in completing their academic project frequently follow a well-structured path. Consider the following principles to get your idea across the finish line:

Find a blueprint. Among most academic projects, whether a research paper, QI project or new curriculum, an underlying formula is commonly applied. Before starting, do your background research. Is there a paper or method that resembles your desired approach? Is there a question or concept that caught your eye? Using a blueprint from existing evidence allows you to identify important structures, phrases, and terms to inform your manuscript. Once you have identified the blueprint, define your project and approach.

Find a mentor. While career mentorship is important for professional growth, you first need a project mentor. Being a project mentor is a smaller ask for a more senior colleague than being a career mentor, and it’s a great way to test-drive a potential long-term working relationship. Moreover, the successful completion of one project can potentially lead to further opportunities, and perhaps even a long-term career mentor.

Take initiative. In business, there is a common adage: “Never bring a problem to your boss without a proposed solution in hand.”¹ In academics, consider: “Never show up with an idea without bringing a proposal.” By bringing a defined proposal to the conversation, your inquiry is more likely to get a response because (a) it is not a blind-ask and (b) it creates a foundation to build on. This is analogous to an early learner presenting their assessment and plan in the clinical setting; you don’t stop at the diagnosis (your idea) without having a plan for how you want to manage it.

Get an accountability partner. Publicly committing to a goal increases the probability of accomplishing your task by 65%, while having an accountability partner increases that by 95%.² An accountability partner serves as a coach to help you accomplish a task. This individual can be a colleague, spouse, or friend and is typically not a part of the project. By leveraging peer pressure, you increase the odds of successfully completing your project.

Carve out dedicated time. The entrepreneur and author Jim Rohn once said, “Discipline is the bridge between goals and accomplishments.”³ To complete a project, you have to make the time to do the work. While many believe that successful writers sit and write for hours on end, many famous writers only wrote for a few hours at a time—but they did so consistently.⁴ Create your routine by setting aside consistent, defined time to work on your project. To extract the most value, select a time of the day in which you work best (eg, early morning). Then, set a timer for 30 minutes and write—or work.

Because you are a busy clinician with constant demands on your time, having the skillset to reliably turn an idea into “academic currency” is a necessity. Having a plan and following these principles will help you earn that academic coin.

“Our goals can only be reached through the vehicle of a plan. There is no other route to success.”

—Pablo Picasso

Whether it be a research manuscript, quality improvement (QI) initiative, or educational curriculum, busy clinicians often struggle getting projects past the idea stage. Barriers to completion, such as a busy clinical schedule or lack of experience and mentorship, are well known. Importantly, these projects serve as “academic currency” used for promotion and advancement and also create generalizable knowledge, which can help others improve clinical practice or operational processes. Those who are successful in completing their academic project frequently follow a well-structured path. Consider the following principles to get your idea across the finish line:

Find a blueprint. Among most academic projects, whether a research paper, QI project or new curriculum, an underlying formula is commonly applied. Before starting, do your background research. Is there a paper or method that resembles your desired approach? Is there a question or concept that caught your eye? Using a blueprint from existing evidence allows you to identify important structures, phrases, and terms to inform your manuscript. Once you have identified the blueprint, define your project and approach.

Find a mentor. While career mentorship is important for professional growth, you first need a project mentor. Being a project mentor is a smaller ask for a more senior colleague than being a career mentor, and it’s a great way to test-drive a potential long-term working relationship. Moreover, the successful completion of one project can potentially lead to further opportunities, and perhaps even a long-term career mentor.

Take initiative. In business, there is a common adage: “Never bring a problem to your boss without a proposed solution in hand.”¹ In academics, consider: “Never show up with an idea without bringing a proposal.” By bringing a defined proposal to the conversation, your inquiry is more likely to get a response because (a) it is not a blind-ask and (b) it creates a foundation to build on. This is analogous to an early learner presenting their assessment and plan in the clinical setting; you don’t stop at the diagnosis (your idea) without having a plan for how you want to manage it.

Get an accountability partner. Publicly committing to a goal increases the probability of accomplishing your task by 65%, while having an accountability partner increases that by 95%.² An accountability partner serves as a coach to help you accomplish a task. This individual can be a colleague, spouse, or friend and is typically not a part of the project. By leveraging peer pressure, you increase the odds of successfully completing your project.

Carve out dedicated time. The entrepreneur and author Jim Rohn once said, “Discipline is the bridge between goals and accomplishments.”³ To complete a project, you have to make the time to do the work. While many believe that successful writers sit and write for hours on end, many famous writers only wrote for a few hours at a time—but they did so consistently.⁴ Create your routine by setting aside consistent, defined time to work on your project. To extract the most value, select a time of the day in which you work best (eg, early morning). Then, set a timer for 30 minutes and write—or work.

Because you are a busy clinician with constant demands on your time, having the skillset to reliably turn an idea into “academic currency” is a necessity. Having a plan and following these principles will help you earn that academic coin.

“Our goals can only be reached through the vehicle of a plan. There is no other route to success.”

—Pablo Picasso

Whether it be a research manuscript, quality improvement (QI) initiative, or educational curriculum, busy clinicians often struggle getting projects past the idea stage. Barriers to completion, such as a busy clinical schedule or lack of experience and mentorship, are well known. Importantly, these projects serve as “academic currency” used for promotion and advancement and also create generalizable knowledge, which can help others improve clinical practice or operational processes. Those who are successful in completing their academic project frequently follow a well-structured path. Consider the following principles to get your idea across the finish line:

Find a blueprint. Among most academic projects, whether a research paper, QI project or new curriculum, an underlying formula is commonly applied. Before starting, do your background research. Is there a paper or method that resembles your desired approach? Is there a question or concept that caught your eye? Using a blueprint from existing evidence allows you to identify important structures, phrases, and terms to inform your manuscript. Once you have identified the blueprint, define your project and approach.

Find a mentor. While career mentorship is important for professional growth, you first need a project mentor. Being a project mentor is a smaller ask for a more senior colleague than being a career mentor, and it’s a great way to test-drive a potential long-term working relationship. Moreover, the successful completion of one project can potentially lead to further opportunities, and perhaps even a long-term career mentor.

Take initiative. In business, there is a common adage: “Never bring a problem to your boss without a proposed solution in hand.”¹ In academics, consider: “Never show up with an idea without bringing a proposal.” By bringing a defined proposal to the conversation, your inquiry is more likely to get a response because (a) it is not a blind-ask and (b) it creates a foundation to build on. This is analogous to an early learner presenting their assessment and plan in the clinical setting; you don’t stop at the diagnosis (your idea) without having a plan for how you want to manage it.

Get an accountability partner. Publicly committing to a goal increases the probability of accomplishing your task by 65%, while having an accountability partner increases that by 95%.² An accountability partner serves as a coach to help you accomplish a task. This individual can be a colleague, spouse, or friend and is typically not a part of the project. By leveraging peer pressure, you increase the odds of successfully completing your project.

Carve out dedicated time. The entrepreneur and author Jim Rohn once said, “Discipline is the bridge between goals and accomplishments.”³ To complete a project, you have to make the time to do the work. While many believe that successful writers sit and write for hours on end, many famous writers only wrote for a few hours at a time—but they did so consistently.⁴ Create your routine by setting aside consistent, defined time to work on your project. To extract the most value, select a time of the day in which you work best (eg, early morning). Then, set a timer for 30 minutes and write—or work.

Because you are a busy clinician with constant demands on your time, having the skillset to reliably turn an idea into “academic currency” is a necessity. Having a plan and following these principles will help you earn that academic coin.

We enter the new year still in the midst of the coronavirus disease 2019 (COVID-19) pandemic and remain humbled by its impact. It is remarkable how much, and how little, has changed. Hospitalists in the early days of the COVID-19 pandemic were struggling. We were caring for patients who were suffering and dying from a new and mysterious disease. There weren’t enough tests (or, if there were tests, there weren’t swabs).¹ We were using protocols for managing respiratory failure that, we would learn later, may not have been the best for improving outcomes. Rumors of unproven therapies came from everywhere: our patients, our colleagues, and even the highest realms of the federal government. We also knew very little about how best to protect ourselves. In many cases, we did not have enough personal protective equipment (PPE). There were no face shields, or “zoom rounds,” or even awareness that we probably shouldn’t sit in the tiny conference room (maskless) discussing patients with the large team of doctors, nurses, respiratory therapists, and social workers.

Perhaps worst of all, we were haunted. We were alarmed by the large numbers of young patients who were ill, and our elderly patients, many of whom we knew and had cared for many times, had suddenly just stopped showing up.² In our free moments, we worried about them; maybe they were afraid to come to the hospital, maybe they were home sick with COVID-19, or maybe they had died alone. And children, initially thought to be spared the most serious consequences of COVID-19, started coming to the hospital with a rare but severe new COVID-19-associated complication, termed multisystem inflammatory syndrome in children (MIS-C). We had to learn to manage yet another manifestation of COVID-19, largely through trial and error.

And, of course, clinical care was only one of our many responsibilities. We were also busy hunting for ventilators, setting up makeshift medical wards and intensive care units, revamping medical education, and scouring the literature for any information to help guide patient care. We worried about getting sick ourselves and bringing the disease home to our families. Our impatience grew as day after day there was no (and still is no) coordinated federal response.

A glimmer of hope slowly emerged. Our colleagues designed and rapidly evaluated respiratory protocols and provided early evidence about the strategies (eg, proning) that were associated with improved outcomes.³ Researchers began to generate knowledge and move us beyond rumors regarding potential therapies. We cheered as our administrators concocted unusual strategies to remedy the PPE and testing shortages.⁴

At the Journal of Hospital Medicine, we were faced with another challenge: How would we describe the chaos and the challenges of being a physician during the COVID-19 era? How would we document the way our colleagues were rising to the challenge and identifying opportunities to rethink hospital care in the United States?

In April, we began to receive a deluge of personal essays from frontline physicians about their experiences with COVID-19. Generally, medical journals publish and disseminate original, high-impact research. Personal essays rarely fit this model. Given the unprecedented circumstances, however, we decided these essays could help chronicle an important moment in medical history. In our May 2020 issue, we published only these essays. We continue to publish them online almost daily.

Some of the essays described how the healthcare system—previously thought to be hyperspecialized, profit-driven, and resistant to change—pivoted within days, as hospitalist physicians trained other physicians to “unspecialize” and pediatricians began to care for adults in an otherwise overwhelmed hospital system.^5,6 Another essay focused on the need to trust that medical students who had graduated early would be able to function as physicians.⁷ And yet another essay expressed concern about the widespread use of unproven therapies in hospitalized patients. “Even in times of global pandemic, we need to consider potential harms and adverse consequences of novel treatments,’’ the physicians wrote. “Sometimes inaction is preferable to action.”⁸

Several essays reflected on the impact of the pandemic on healthcare disparities, suggesting that the pandemic had made (the well-known but often ignored) differences in health outcomes between White patients and racial minorities more obvious. Still another essay reflected on the intersection between structural racism, poor access to care, and interpersonal racism, describing the grief caused by losses of Black lives to both police violence and COVID-19.⁹

There also were personal stories of hardship and survival. One hospitalist physician with asthma described coughing as ``the new leprosy.”¹⁰ She wrote, “This is a particularly unpropitious time in history to be a Chinese-American doctor who can’t stop coughing.”

There were drawbacks to our decision to focus on personal essays. Although it was clear even before the pandemic, COVID-19 has highlighted that a path for quick dissemination of original peer-reviewed research is needed. If existing medical journals do not fill that role, websites that publish and disseminate non–peer-reviewed work (aka, “preprints”) will become the preferred method for distribution of high-impact, timely original research.¹¹ The journal’s pivot to reviewing and publishing personal essays may have kept us from improving our approach to rapid peer review and dissemination. In those early days, however, there was no peer-reviewed work to publish, but there was an intense desire (from our members and physicians generally) for information and stories from the front lines. In a way, the essays we published were early “case reports,” that hypothesized about how we might rethink healthcare delivery in pandemic conditions.

Furthermore, our decision to solicit and publish personal essays addressing shortcomings of the federal response and consequences of the pandemic meant that the Journal of Hospital Medicine became part of the pandemic’s political discourse. As editors, we have historically kept the journal away from political arguments or endorsements. In this case, however, we decided that even if some of the opinions were political, they were an appropriate response to the widespread anti-science rhetoric endorsed by the current administration. The resultant erosion of trust in public health has undoubtedly contributed to persistence of the pandemic.¹² A stance against masks, for example, rejects the recommendations of nearly all scientists in favor of (a selfish and problematic idea of) “self-determination.” Those who proclaim that such a mandate infringes on their personal freedom reject evidence-based recommendations of scientists and disregard public health strategies meant to protect everyone.

As we reflect on the past year, our most important lesson may be that our previous emphasis on publishing high-impact original research likely missed important personal and professional insights, insights that could have changed practice, improved patient experience, and reduced physician burnout. Anecdotes are not scientific evidence, but we have discovered their incredible power to help us learn, empathize, commiserate, and survive. Hospitals learned that they must adapt in the moment, a notion that runs counter to the notoriously slow pace of change in paradigms of healthcare. Hospitalists learned to “find their battle buddies” to ward off isolation and to cherish their teams in the face of overwhelming trauma, an approach requiring empathy, humility, and compassion.¹³ We won’t soon forget that, when things were most dire, it was stories—not data—that gave us hope. We look forward to 2021 with great optimism. New vaccines and new federal leaders who value and respect science give us hope that the end of the pandemic is in sight. We are indebted to all frontline workers who have transformed care delivery and remained courageous in the face of great personal risk. As a journal, we will continue, as one scientist noted, to use our “platform for advocacy, unabashedly.”¹⁴

We enter the new year still in the midst of the coronavirus disease 2019 (COVID-19) pandemic and remain humbled by its impact. It is remarkable how much, and how little, has changed. Hospitalists in the early days of the COVID-19 pandemic were struggling. We were caring for patients who were suffering and dying from a new and mysterious disease. There weren’t enough tests (or, if there were tests, there weren’t swabs).¹ We were using protocols for managing respiratory failure that, we would learn later, may not have been the best for improving outcomes. Rumors of unproven therapies came from everywhere: our patients, our colleagues, and even the highest realms of the federal government. We also knew very little about how best to protect ourselves. In many cases, we did not have enough personal protective equipment (PPE). There were no face shields, or “zoom rounds,” or even awareness that we probably shouldn’t sit in the tiny conference room (maskless) discussing patients with the large team of doctors, nurses, respiratory therapists, and social workers.

Perhaps worst of all, we were haunted. We were alarmed by the large numbers of young patients who were ill, and our elderly patients, many of whom we knew and had cared for many times, had suddenly just stopped showing up.² In our free moments, we worried about them; maybe they were afraid to come to the hospital, maybe they were home sick with COVID-19, or maybe they had died alone. And children, initially thought to be spared the most serious consequences of COVID-19, started coming to the hospital with a rare but severe new COVID-19-associated complication, termed multisystem inflammatory syndrome in children (MIS-C). We had to learn to manage yet another manifestation of COVID-19, largely through trial and error.

And, of course, clinical care was only one of our many responsibilities. We were also busy hunting for ventilators, setting up makeshift medical wards and intensive care units, revamping medical education, and scouring the literature for any information to help guide patient care. We worried about getting sick ourselves and bringing the disease home to our families. Our impatience grew as day after day there was no (and still is no) coordinated federal response.

A glimmer of hope slowly emerged. Our colleagues designed and rapidly evaluated respiratory protocols and provided early evidence about the strategies (eg, proning) that were associated with improved outcomes.³ Researchers began to generate knowledge and move us beyond rumors regarding potential therapies. We cheered as our administrators concocted unusual strategies to remedy the PPE and testing shortages.⁴

At the Journal of Hospital Medicine, we were faced with another challenge: How would we describe the chaos and the challenges of being a physician during the COVID-19 era? How would we document the way our colleagues were rising to the challenge and identifying opportunities to rethink hospital care in the United States?

In April, we began to receive a deluge of personal essays from frontline physicians about their experiences with COVID-19. Generally, medical journals publish and disseminate original, high-impact research. Personal essays rarely fit this model. Given the unprecedented circumstances, however, we decided these essays could help chronicle an important moment in medical history. In our May 2020 issue, we published only these essays. We continue to publish them online almost daily.

Some of the essays described how the healthcare system—previously thought to be hyperspecialized, profit-driven, and resistant to change—pivoted within days, as hospitalist physicians trained other physicians to “unspecialize” and pediatricians began to care for adults in an otherwise overwhelmed hospital system.^5,6 Another essay focused on the need to trust that medical students who had graduated early would be able to function as physicians.⁷ And yet another essay expressed concern about the widespread use of unproven therapies in hospitalized patients. “Even in times of global pandemic, we need to consider potential harms and adverse consequences of novel treatments,’’ the physicians wrote. “Sometimes inaction is preferable to action.”⁸

Several essays reflected on the impact of the pandemic on healthcare disparities, suggesting that the pandemic had made (the well-known but often ignored) differences in health outcomes between White patients and racial minorities more obvious. Still another essay reflected on the intersection between structural racism, poor access to care, and interpersonal racism, describing the grief caused by losses of Black lives to both police violence and COVID-19.⁹

There also were personal stories of hardship and survival. One hospitalist physician with asthma described coughing as ``the new leprosy.”¹⁰ She wrote, “This is a particularly unpropitious time in history to be a Chinese-American doctor who can’t stop coughing.”

There were drawbacks to our decision to focus on personal essays. Although it was clear even before the pandemic, COVID-19 has highlighted that a path for quick dissemination of original peer-reviewed research is needed. If existing medical journals do not fill that role, websites that publish and disseminate non–peer-reviewed work (aka, “preprints”) will become the preferred method for distribution of high-impact, timely original research.¹¹ The journal’s pivot to reviewing and publishing personal essays may have kept us from improving our approach to rapid peer review and dissemination. In those early days, however, there was no peer-reviewed work to publish, but there was an intense desire (from our members and physicians generally) for information and stories from the front lines. In a way, the essays we published were early “case reports,” that hypothesized about how we might rethink healthcare delivery in pandemic conditions.

Furthermore, our decision to solicit and publish personal essays addressing shortcomings of the federal response and consequences of the pandemic meant that the Journal of Hospital Medicine became part of the pandemic’s political discourse. As editors, we have historically kept the journal away from political arguments or endorsements. In this case, however, we decided that even if some of the opinions were political, they were an appropriate response to the widespread anti-science rhetoric endorsed by the current administration. The resultant erosion of trust in public health has undoubtedly contributed to persistence of the pandemic.¹² A stance against masks, for example, rejects the recommendations of nearly all scientists in favor of (a selfish and problematic idea of) “self-determination.” Those who proclaim that such a mandate infringes on their personal freedom reject evidence-based recommendations of scientists and disregard public health strategies meant to protect everyone.

As we reflect on the past year, our most important lesson may be that our previous emphasis on publishing high-impact original research likely missed important personal and professional insights, insights that could have changed practice, improved patient experience, and reduced physician burnout. Anecdotes are not scientific evidence, but we have discovered their incredible power to help us learn, empathize, commiserate, and survive. Hospitals learned that they must adapt in the moment, a notion that runs counter to the notoriously slow pace of change in paradigms of healthcare. Hospitalists learned to “find their battle buddies” to ward off isolation and to cherish their teams in the face of overwhelming trauma, an approach requiring empathy, humility, and compassion.¹³ We won’t soon forget that, when things were most dire, it was stories—not data—that gave us hope. We look forward to 2021 with great optimism. New vaccines and new federal leaders who value and respect science give us hope that the end of the pandemic is in sight. We are indebted to all frontline workers who have transformed care delivery and remained courageous in the face of great personal risk. As a journal, we will continue, as one scientist noted, to use our “platform for advocacy, unabashedly.”¹⁴

We enter the new year still in the midst of the coronavirus disease 2019 (COVID-19) pandemic and remain humbled by its impact. It is remarkable how much, and how little, has changed. Hospitalists in the early days of the COVID-19 pandemic were struggling. We were caring for patients who were suffering and dying from a new and mysterious disease. There weren’t enough tests (or, if there were tests, there weren’t swabs).¹ We were using protocols for managing respiratory failure that, we would learn later, may not have been the best for improving outcomes. Rumors of unproven therapies came from everywhere: our patients, our colleagues, and even the highest realms of the federal government. We also knew very little about how best to protect ourselves. In many cases, we did not have enough personal protective equipment (PPE). There were no face shields, or “zoom rounds,” or even awareness that we probably shouldn’t sit in the tiny conference room (maskless) discussing patients with the large team of doctors, nurses, respiratory therapists, and social workers.

Perhaps worst of all, we were haunted. We were alarmed by the large numbers of young patients who were ill, and our elderly patients, many of whom we knew and had cared for many times, had suddenly just stopped showing up.² In our free moments, we worried about them; maybe they were afraid to come to the hospital, maybe they were home sick with COVID-19, or maybe they had died alone. And children, initially thought to be spared the most serious consequences of COVID-19, started coming to the hospital with a rare but severe new COVID-19-associated complication, termed multisystem inflammatory syndrome in children (MIS-C). We had to learn to manage yet another manifestation of COVID-19, largely through trial and error.

And, of course, clinical care was only one of our many responsibilities. We were also busy hunting for ventilators, setting up makeshift medical wards and intensive care units, revamping medical education, and scouring the literature for any information to help guide patient care. We worried about getting sick ourselves and bringing the disease home to our families. Our impatience grew as day after day there was no (and still is no) coordinated federal response.

A glimmer of hope slowly emerged. Our colleagues designed and rapidly evaluated respiratory protocols and provided early evidence about the strategies (eg, proning) that were associated with improved outcomes.³ Researchers began to generate knowledge and move us beyond rumors regarding potential therapies. We cheered as our administrators concocted unusual strategies to remedy the PPE and testing shortages.⁴

At the Journal of Hospital Medicine, we were faced with another challenge: How would we describe the chaos and the challenges of being a physician during the COVID-19 era? How would we document the way our colleagues were rising to the challenge and identifying opportunities to rethink hospital care in the United States?

In April, we began to receive a deluge of personal essays from frontline physicians about their experiences with COVID-19. Generally, medical journals publish and disseminate original, high-impact research. Personal essays rarely fit this model. Given the unprecedented circumstances, however, we decided these essays could help chronicle an important moment in medical history. In our May 2020 issue, we published only these essays. We continue to publish them online almost daily.

Some of the essays described how the healthcare system—previously thought to be hyperspecialized, profit-driven, and resistant to change—pivoted within days, as hospitalist physicians trained other physicians to “unspecialize” and pediatricians began to care for adults in an otherwise overwhelmed hospital system.^5,6 Another essay focused on the need to trust that medical students who had graduated early would be able to function as physicians.⁷ And yet another essay expressed concern about the widespread use of unproven therapies in hospitalized patients. “Even in times of global pandemic, we need to consider potential harms and adverse consequences of novel treatments,’’ the physicians wrote. “Sometimes inaction is preferable to action.”⁸

Several essays reflected on the impact of the pandemic on healthcare disparities, suggesting that the pandemic had made (the well-known but often ignored) differences in health outcomes between White patients and racial minorities more obvious. Still another essay reflected on the intersection between structural racism, poor access to care, and interpersonal racism, describing the grief caused by losses of Black lives to both police violence and COVID-19.⁹

There also were personal stories of hardship and survival. One hospitalist physician with asthma described coughing as ``the new leprosy.”¹⁰ She wrote, “This is a particularly unpropitious time in history to be a Chinese-American doctor who can’t stop coughing.”

There were drawbacks to our decision to focus on personal essays. Although it was clear even before the pandemic, COVID-19 has highlighted that a path for quick dissemination of original peer-reviewed research is needed. If existing medical journals do not fill that role, websites that publish and disseminate non–peer-reviewed work (aka, “preprints”) will become the preferred method for distribution of high-impact, timely original research.¹¹ The journal’s pivot to reviewing and publishing personal essays may have kept us from improving our approach to rapid peer review and dissemination. In those early days, however, there was no peer-reviewed work to publish, but there was an intense desire (from our members and physicians generally) for information and stories from the front lines. In a way, the essays we published were early “case reports,” that hypothesized about how we might rethink healthcare delivery in pandemic conditions.

Furthermore, our decision to solicit and publish personal essays addressing shortcomings of the federal response and consequences of the pandemic meant that the Journal of Hospital Medicine became part of the pandemic’s political discourse. As editors, we have historically kept the journal away from political arguments or endorsements. In this case, however, we decided that even if some of the opinions were political, they were an appropriate response to the widespread anti-science rhetoric endorsed by the current administration. The resultant erosion of trust in public health has undoubtedly contributed to persistence of the pandemic.¹² A stance against masks, for example, rejects the recommendations of nearly all scientists in favor of (a selfish and problematic idea of) “self-determination.” Those who proclaim that such a mandate infringes on their personal freedom reject evidence-based recommendations of scientists and disregard public health strategies meant to protect everyone.

As we reflect on the past year, our most important lesson may be that our previous emphasis on publishing high-impact original research likely missed important personal and professional insights, insights that could have changed practice, improved patient experience, and reduced physician burnout. Anecdotes are not scientific evidence, but we have discovered their incredible power to help us learn, empathize, commiserate, and survive. Hospitals learned that they must adapt in the moment, a notion that runs counter to the notoriously slow pace of change in paradigms of healthcare. Hospitalists learned to “find their battle buddies” to ward off isolation and to cherish their teams in the face of overwhelming trauma, an approach requiring empathy, humility, and compassion.¹³ We won’t soon forget that, when things were most dire, it was stories—not data—that gave us hope. We look forward to 2021 with great optimism. New vaccines and new federal leaders who value and respect science give us hope that the end of the pandemic is in sight. We are indebted to all frontline workers who have transformed care delivery and remained courageous in the face of great personal risk. As a journal, we will continue, as one scientist noted, to use our “platform for advocacy, unabashedly.”¹⁴

Hospitalists have played a central role in the massive response to the coronavirus disease 2019 (COVID-19) pandemic by creating innovative staffing models, rapidly learning about the disease and teaching others, and working closely with hospital executive leadership to create surge capacity.¹ Some hospitals and regions have weathered an initial storm and are now experiencing a slower influx of COVID-19 patients, while others are now seeing a surge, which is expected to persist for the foreseeable future—the marathon has begun.² We have entered a new COVID-19 reality: disrupted care models, harsh financial consequences,³ and uncertainty about which adaptations should be preserved and for how long. Common operational challenges will define the new normal. In this Perspective, we share strategies to address these challenges, focusing on three emerging themes: realigning staffing to patient volumes, safely managing space limitations, and navigating the financial ramifications of COVID-19 for hospital medicine groups.

Hospital medicine groups face uncertainty about future patient volumes and their characteristics. It is unclear when, how, or even whether hospital medicine groups should return to “normal” pre-COVID staffing models. The following principles can guide staffing decisions.

First, maintain nonhospitalist backup pools and define triggers to activate these providers. Despite the impulse to return to prior staffing models, this recovery period provides an opportunity for leaders to create transparent activation protocols and provide additional training to enable seamless backup. In preparation for a surge, our hospital medicine group quickly assembled an emergency staffing pool composed of advanced practice providers, primary care providers, medicine subspecialists, and surgeons who were prepared to temporarily assume unfamiliar roles. Thankfully, we were able to manage our COVID-19 patients without much emergency hospitalist staffing, but for other hospitals with larger community outbreaks, the emergency backup workforce proved invaluable.

Second, use appropriate safeguards and delegate certain aspects of COVID-related care to other healthcare team members. As staff are deployed and redeployed, consider how inter­professional team members can be reintegrated into evaluation and triage protocols. For example, registered nurses can determine appropriate isolation precautions for patients with COVID and patients under investigation.

Third, consider hospital-specific specialty care patterns when planning for COVID-19 redeployment to ensure access to equally critical, nonelective services. For example, Level 1 trauma centers may expect seasonal increases in trauma patient volumes, so consider staffing trauma teams (including surgeons, anesthesiologists, and operating room staff) for their usual roles to prevent critical coverage gaps. Concurrently, hospital medicine consulting and comanagement teams must also be available to support the trauma service. These staffing needs affect who will be available for redeployment for future COVID-related care.

As the number of COVID cases increased, numerous hospitals created geographic “hot zones” with defined cold (uncontaminated), warm (transitional), and hot (contaminated) areas by either partitioning off a section of an acute care medical ward or repurposing an entire ward as a COVID-19 unit, and similar zones were made in intensive care units. Hot zones required significant early investments to change infrastructure, including equipping rooms for negative pressurization with HEPA filtration towers and training staff on safety protocols for entering these spaces, performing necessary patient care, and exiting. Ultimately, these investments proved worthwhile and allowed for decreased personal protective equipment (PPE) use, as well as improved efficiency and staff safety. However, as hospitals ramp up non-COVID care, deciding how to best reconfigure or downsize these hot zones has become challenging.

With time to regroup, the newly experienced end users of hot zones—hospitalists, other staff who worked in these spaces, and patients—must be included in discussions with engineers, architects, and administrators regarding future construction. Hot zone plans should specifically address how physical separation of COVID and non-COVID patients will be maintained while providing safe and efficient care. With elective surgeries increasing and non-COVID patients returning to hospitals, leaders must consider the psychological effects that seeing hospital staff doffing PPE and crossing an invisible barrier to a ‘‘cold” area of the floor has on patients and their families. It is important to maintain hot zones in areas that can dynamically flex to accommodate waves of the current and future pandemics, especially because hospitals may be asked to care for patients from overwhelmed distant sites even if the pandemic is locally controlled. We are experimenting with modifications to hospital traffic patterns including “no pass through” zones, one-way hallways, and separate entries and exits to clinical floors for COVID and non-COVID patients. With vigilant adherence to infection prevention guidelines and PPE use, we have not seen hospital-­acquired infections with this model of care.

Modifying space and flow patterns also enables clustered care for COVID patients, which allows for the temporary use of modular teams.⁴ This tactic may be especially useful during surge periods, during which PPE conservation is paramount and isolating cohorts of providers provides an extra layer of safety. In the longer run, however, isolating providers from their peers risks worsening morale and increasing burnout.

The path forward must ensure safety but also allow for a financially sustainable balance of COVID and non-COVID care. To prepare for surges, health systems canceled elective surgeries and other services that generate essential revenue. At both private and public hospitals, systemwide measures have been taken to mitigate these financial losses. These measures have included salary, retirement, and continuing medical education benefit reductions for physicians and senior leadership; limits to physician hiring and recruitment; leaner operations with systemwide expense reductions; and mandatory and voluntary staff furloughs. The frontline hospital staff, including physicians, nurses, technologists, and food and environmental service workers, who have made great sacrifices during this pandemic, may also now be facing significant personal financial consequences.

The following recommendations are offered from the perspective that crisis creates opportunity for hospital medicine leaders grappling with budget shortfalls.

First, maximize budget transparency by explicitly defining the principles and priorities that govern budget decisions, which allows hospitalist group members to understand how the organization determines budget cuts. For example, stating that a key priority is to minimize staff layoffs makes consequent salary reductions more understandable.

Second, solicit hospital medicine group members’ input on these shared challenges and invite their help in identifying and prioritizing potential cost-saving or cost-cutting measures.

Third, highlight hospitalists’ nonfiscal contributions, especially in terms of crisis leadership, to continue engagement with executive leaders.⁵ This may include a dialogue about the disproportionate influence of work relative value unit production on salary and about how to create compensation systems that can also recognize crisis readiness as an important feature of sustainability and quality care. The next pandemic surge may be weeks or months away, and hospitalists will again need to be leaders in the response.

Fourth, use this crisis to foster fiscal innovation and accelerate participation in value improvement work, such as redesigning pay-for-performance metrics. Financially strapped institutions will value hospitalists who are good financial stewards. For example, leverage hospitalist expertise in progression of care to facilitate timely disposition of COVID patients, thereby minimizing costly extended hospitalizations.

Lastly, hospital medicine groups must match staffing to patient volume to the extent possible. Approximately two-thirds of hospitalist groups entered this crisis already understaffed and partially reliant on moonlighters,⁶ which allowed some variation of labor expenses to match lower patient volume. During the recovery phase, hospital volumes may either be significantly below or above baseline; many patients are understandably avoiding hospitals due to fear of COVID. However, delayed care may create a different kind of peak demand for services. For hospitalists, uncertainty about expected clinical roles, COVID vs non-COVID patient mix, and patient volume can be stressful. We recommend sustained, frequent communication about census trends and how shifts will be covered to ensure adequate, long-term staffing. Maintaining trust and morale will be equally, if not more, important in the next phase.

As we settle into the marathon, hospital medicine leadership must balance competing priorities with increasing finesse. Our hospital medicine group has benefited from continually discussing operational challenges and refining our strategies as we plan for what is ahead. We have highlighted three mission-critical themes and recommend that hospital and hospital medicine group leaders remain mindful of these challenges and potential strategies. Each of our four academic hospitals has considered similar trade-offs and will proceed along slightly different trajectories to meet unique needs. Looking to the future, we anticipate additional challenges requiring greater ongoing attention alongside those already identified. These include mitigating provider burnout, optimizing resident and student education, and maintaining scholarly work as COVID unpredictably waxes and wanes. By accumulating confidence and wisdom about post-COVID hospital medicine group functions, we hope to provide hospitalists with the energy to keep the pace in the next phase of the marathon.

Hospitalists have played a central role in the massive response to the coronavirus disease 2019 (COVID-19) pandemic by creating innovative staffing models, rapidly learning about the disease and teaching others, and working closely with hospital executive leadership to create surge capacity.¹ Some hospitals and regions have weathered an initial storm and are now experiencing a slower influx of COVID-19 patients, while others are now seeing a surge, which is expected to persist for the foreseeable future—the marathon has begun.² We have entered a new COVID-19 reality: disrupted care models, harsh financial consequences,³ and uncertainty about which adaptations should be preserved and for how long. Common operational challenges will define the new normal. In this Perspective, we share strategies to address these challenges, focusing on three emerging themes: realigning staffing to patient volumes, safely managing space limitations, and navigating the financial ramifications of COVID-19 for hospital medicine groups.

Hospital medicine groups face uncertainty about future patient volumes and their characteristics. It is unclear when, how, or even whether hospital medicine groups should return to “normal” pre-COVID staffing models. The following principles can guide staffing decisions.

First, maintain nonhospitalist backup pools and define triggers to activate these providers. Despite the impulse to return to prior staffing models, this recovery period provides an opportunity for leaders to create transparent activation protocols and provide additional training to enable seamless backup. In preparation for a surge, our hospital medicine group quickly assembled an emergency staffing pool composed of advanced practice providers, primary care providers, medicine subspecialists, and surgeons who were prepared to temporarily assume unfamiliar roles. Thankfully, we were able to manage our COVID-19 patients without much emergency hospitalist staffing, but for other hospitals with larger community outbreaks, the emergency backup workforce proved invaluable.

Second, use appropriate safeguards and delegate certain aspects of COVID-related care to other healthcare team members. As staff are deployed and redeployed, consider how inter­professional team members can be reintegrated into evaluation and triage protocols. For example, registered nurses can determine appropriate isolation precautions for patients with COVID and patients under investigation.

Third, consider hospital-specific specialty care patterns when planning for COVID-19 redeployment to ensure access to equally critical, nonelective services. For example, Level 1 trauma centers may expect seasonal increases in trauma patient volumes, so consider staffing trauma teams (including surgeons, anesthesiologists, and operating room staff) for their usual roles to prevent critical coverage gaps. Concurrently, hospital medicine consulting and comanagement teams must also be available to support the trauma service. These staffing needs affect who will be available for redeployment for future COVID-related care.

As the number of COVID cases increased, numerous hospitals created geographic “hot zones” with defined cold (uncontaminated), warm (transitional), and hot (contaminated) areas by either partitioning off a section of an acute care medical ward or repurposing an entire ward as a COVID-19 unit, and similar zones were made in intensive care units. Hot zones required significant early investments to change infrastructure, including equipping rooms for negative pressurization with HEPA filtration towers and training staff on safety protocols for entering these spaces, performing necessary patient care, and exiting. Ultimately, these investments proved worthwhile and allowed for decreased personal protective equipment (PPE) use, as well as improved efficiency and staff safety. However, as hospitals ramp up non-COVID care, deciding how to best reconfigure or downsize these hot zones has become challenging.

With time to regroup, the newly experienced end users of hot zones—hospitalists, other staff who worked in these spaces, and patients—must be included in discussions with engineers, architects, and administrators regarding future construction. Hot zone plans should specifically address how physical separation of COVID and non-COVID patients will be maintained while providing safe and efficient care. With elective surgeries increasing and non-COVID patients returning to hospitals, leaders must consider the psychological effects that seeing hospital staff doffing PPE and crossing an invisible barrier to a ‘‘cold” area of the floor has on patients and their families. It is important to maintain hot zones in areas that can dynamically flex to accommodate waves of the current and future pandemics, especially because hospitals may be asked to care for patients from overwhelmed distant sites even if the pandemic is locally controlled. We are experimenting with modifications to hospital traffic patterns including “no pass through” zones, one-way hallways, and separate entries and exits to clinical floors for COVID and non-COVID patients. With vigilant adherence to infection prevention guidelines and PPE use, we have not seen hospital-­acquired infections with this model of care.

Modifying space and flow patterns also enables clustered care for COVID patients, which allows for the temporary use of modular teams.⁴ This tactic may be especially useful during surge periods, during which PPE conservation is paramount and isolating cohorts of providers provides an extra layer of safety. In the longer run, however, isolating providers from their peers risks worsening morale and increasing burnout.

The path forward must ensure safety but also allow for a financially sustainable balance of COVID and non-COVID care. To prepare for surges, health systems canceled elective surgeries and other services that generate essential revenue. At both private and public hospitals, systemwide measures have been taken to mitigate these financial losses. These measures have included salary, retirement, and continuing medical education benefit reductions for physicians and senior leadership; limits to physician hiring and recruitment; leaner operations with systemwide expense reductions; and mandatory and voluntary staff furloughs. The frontline hospital staff, including physicians, nurses, technologists, and food and environmental service workers, who have made great sacrifices during this pandemic, may also now be facing significant personal financial consequences.

The following recommendations are offered from the perspective that crisis creates opportunity for hospital medicine leaders grappling with budget shortfalls.

First, maximize budget transparency by explicitly defining the principles and priorities that govern budget decisions, which allows hospitalist group members to understand how the organization determines budget cuts. For example, stating that a key priority is to minimize staff layoffs makes consequent salary reductions more understandable.

Second, solicit hospital medicine group members’ input on these shared challenges and invite their help in identifying and prioritizing potential cost-saving or cost-cutting measures.

Third, highlight hospitalists’ nonfiscal contributions, especially in terms of crisis leadership, to continue engagement with executive leaders.⁵ This may include a dialogue about the disproportionate influence of work relative value unit production on salary and about how to create compensation systems that can also recognize crisis readiness as an important feature of sustainability and quality care. The next pandemic surge may be weeks or months away, and hospitalists will again need to be leaders in the response.

Fourth, use this crisis to foster fiscal innovation and accelerate participation in value improvement work, such as redesigning pay-for-performance metrics. Financially strapped institutions will value hospitalists who are good financial stewards. For example, leverage hospitalist expertise in progression of care to facilitate timely disposition of COVID patients, thereby minimizing costly extended hospitalizations.

Lastly, hospital medicine groups must match staffing to patient volume to the extent possible. Approximately two-thirds of hospitalist groups entered this crisis already understaffed and partially reliant on moonlighters,⁶ which allowed some variation of labor expenses to match lower patient volume. During the recovery phase, hospital volumes may either be significantly below or above baseline; many patients are understandably avoiding hospitals due to fear of COVID. However, delayed care may create a different kind of peak demand for services. For hospitalists, uncertainty about expected clinical roles, COVID vs non-COVID patient mix, and patient volume can be stressful. We recommend sustained, frequent communication about census trends and how shifts will be covered to ensure adequate, long-term staffing. Maintaining trust and morale will be equally, if not more, important in the next phase.

As we settle into the marathon, hospital medicine leadership must balance competing priorities with increasing finesse. Our hospital medicine group has benefited from continually discussing operational challenges and refining our strategies as we plan for what is ahead. We have highlighted three mission-critical themes and recommend that hospital and hospital medicine group leaders remain mindful of these challenges and potential strategies. Each of our four academic hospitals has considered similar trade-offs and will proceed along slightly different trajectories to meet unique needs. Looking to the future, we anticipate additional challenges requiring greater ongoing attention alongside those already identified. These include mitigating provider burnout, optimizing resident and student education, and maintaining scholarly work as COVID unpredictably waxes and wanes. By accumulating confidence and wisdom about post-COVID hospital medicine group functions, we hope to provide hospitalists with the energy to keep the pace in the next phase of the marathon.

Hospitalists have played a central role in the massive response to the coronavirus disease 2019 (COVID-19) pandemic by creating innovative staffing models, rapidly learning about the disease and teaching others, and working closely with hospital executive leadership to create surge capacity.¹ Some hospitals and regions have weathered an initial storm and are now experiencing a slower influx of COVID-19 patients, while others are now seeing a surge, which is expected to persist for the foreseeable future—the marathon has begun.² We have entered a new COVID-19 reality: disrupted care models, harsh financial consequences,³ and uncertainty about which adaptations should be preserved and for how long. Common operational challenges will define the new normal. In this Perspective, we share strategies to address these challenges, focusing on three emerging themes: realigning staffing to patient volumes, safely managing space limitations, and navigating the financial ramifications of COVID-19 for hospital medicine groups.

Hospital medicine groups face uncertainty about future patient volumes and their characteristics. It is unclear when, how, or even whether hospital medicine groups should return to “normal” pre-COVID staffing models. The following principles can guide staffing decisions.

First, maintain nonhospitalist backup pools and define triggers to activate these providers. Despite the impulse to return to prior staffing models, this recovery period provides an opportunity for leaders to create transparent activation protocols and provide additional training to enable seamless backup. In preparation for a surge, our hospital medicine group quickly assembled an emergency staffing pool composed of advanced practice providers, primary care providers, medicine subspecialists, and surgeons who were prepared to temporarily assume unfamiliar roles. Thankfully, we were able to manage our COVID-19 patients without much emergency hospitalist staffing, but for other hospitals with larger community outbreaks, the emergency backup workforce proved invaluable.

Second, use appropriate safeguards and delegate certain aspects of COVID-related care to other healthcare team members. As staff are deployed and redeployed, consider how inter­professional team members can be reintegrated into evaluation and triage protocols. For example, registered nurses can determine appropriate isolation precautions for patients with COVID and patients under investigation.

Third, consider hospital-specific specialty care patterns when planning for COVID-19 redeployment to ensure access to equally critical, nonelective services. For example, Level 1 trauma centers may expect seasonal increases in trauma patient volumes, so consider staffing trauma teams (including surgeons, anesthesiologists, and operating room staff) for their usual roles to prevent critical coverage gaps. Concurrently, hospital medicine consulting and comanagement teams must also be available to support the trauma service. These staffing needs affect who will be available for redeployment for future COVID-related care.

As the number of COVID cases increased, numerous hospitals created geographic “hot zones” with defined cold (uncontaminated), warm (transitional), and hot (contaminated) areas by either partitioning off a section of an acute care medical ward or repurposing an entire ward as a COVID-19 unit, and similar zones were made in intensive care units. Hot zones required significant early investments to change infrastructure, including equipping rooms for negative pressurization with HEPA filtration towers and training staff on safety protocols for entering these spaces, performing necessary patient care, and exiting. Ultimately, these investments proved worthwhile and allowed for decreased personal protective equipment (PPE) use, as well as improved efficiency and staff safety. However, as hospitals ramp up non-COVID care, deciding how to best reconfigure or downsize these hot zones has become challenging.

With time to regroup, the newly experienced end users of hot zones—hospitalists, other staff who worked in these spaces, and patients—must be included in discussions with engineers, architects, and administrators regarding future construction. Hot zone plans should specifically address how physical separation of COVID and non-COVID patients will be maintained while providing safe and efficient care. With elective surgeries increasing and non-COVID patients returning to hospitals, leaders must consider the psychological effects that seeing hospital staff doffing PPE and crossing an invisible barrier to a ‘‘cold” area of the floor has on patients and their families. It is important to maintain hot zones in areas that can dynamically flex to accommodate waves of the current and future pandemics, especially because hospitals may be asked to care for patients from overwhelmed distant sites even if the pandemic is locally controlled. We are experimenting with modifications to hospital traffic patterns including “no pass through” zones, one-way hallways, and separate entries and exits to clinical floors for COVID and non-COVID patients. With vigilant adherence to infection prevention guidelines and PPE use, we have not seen hospital-­acquired infections with this model of care.

Modifying space and flow patterns also enables clustered care for COVID patients, which allows for the temporary use of modular teams.⁴ This tactic may be especially useful during surge periods, during which PPE conservation is paramount and isolating cohorts of providers provides an extra layer of safety. In the longer run, however, isolating providers from their peers risks worsening morale and increasing burnout.

The path forward must ensure safety but also allow for a financially sustainable balance of COVID and non-COVID care. To prepare for surges, health systems canceled elective surgeries and other services that generate essential revenue. At both private and public hospitals, systemwide measures have been taken to mitigate these financial losses. These measures have included salary, retirement, and continuing medical education benefit reductions for physicians and senior leadership; limits to physician hiring and recruitment; leaner operations with systemwide expense reductions; and mandatory and voluntary staff furloughs. The frontline hospital staff, including physicians, nurses, technologists, and food and environmental service workers, who have made great sacrifices during this pandemic, may also now be facing significant personal financial consequences.

The following recommendations are offered from the perspective that crisis creates opportunity for hospital medicine leaders grappling with budget shortfalls.

First, maximize budget transparency by explicitly defining the principles and priorities that govern budget decisions, which allows hospitalist group members to understand how the organization determines budget cuts. For example, stating that a key priority is to minimize staff layoffs makes consequent salary reductions more understandable.

Second, solicit hospital medicine group members’ input on these shared challenges and invite their help in identifying and prioritizing potential cost-saving or cost-cutting measures.

Third, highlight hospitalists’ nonfiscal contributions, especially in terms of crisis leadership, to continue engagement with executive leaders.⁵ This may include a dialogue about the disproportionate influence of work relative value unit production on salary and about how to create compensation systems that can also recognize crisis readiness as an important feature of sustainability and quality care. The next pandemic surge may be weeks or months away, and hospitalists will again need to be leaders in the response.

Fourth, use this crisis to foster fiscal innovation and accelerate participation in value improvement work, such as redesigning pay-for-performance metrics. Financially strapped institutions will value hospitalists who are good financial stewards. For example, leverage hospitalist expertise in progression of care to facilitate timely disposition of COVID patients, thereby minimizing costly extended hospitalizations.

Lastly, hospital medicine groups must match staffing to patient volume to the extent possible. Approximately two-thirds of hospitalist groups entered this crisis already understaffed and partially reliant on moonlighters,⁶ which allowed some variation of labor expenses to match lower patient volume. During the recovery phase, hospital volumes may either be significantly below or above baseline; many patients are understandably avoiding hospitals due to fear of COVID. However, delayed care may create a different kind of peak demand for services. For hospitalists, uncertainty about expected clinical roles, COVID vs non-COVID patient mix, and patient volume can be stressful. We recommend sustained, frequent communication about census trends and how shifts will be covered to ensure adequate, long-term staffing. Maintaining trust and morale will be equally, if not more, important in the next phase.

As we settle into the marathon, hospital medicine leadership must balance competing priorities with increasing finesse. Our hospital medicine group has benefited from continually discussing operational challenges and refining our strategies as we plan for what is ahead. We have highlighted three mission-critical themes and recommend that hospital and hospital medicine group leaders remain mindful of these challenges and potential strategies. Each of our four academic hospitals has considered similar trade-offs and will proceed along slightly different trajectories to meet unique needs. Looking to the future, we anticipate additional challenges requiring greater ongoing attention alongside those already identified. These include mitigating provider burnout, optimizing resident and student education, and maintaining scholarly work as COVID unpredictably waxes and wanes. By accumulating confidence and wisdom about post-COVID hospital medicine group functions, we hope to provide hospitalists with the energy to keep the pace in the next phase of the marathon.