Editorial

The early phase of the COVID-19 pandemic was an extraordinarily uncertain, yet innovative, time.¹ Few data describe site-level effects of the many adaptations made to deal with surging case numbers, but studies of larger hospital referral regions (HRR) provide important clues.

In this issue of the Journal of Hospital Medicine, Janke et al² describe how availability of hospital resources in a region relate to COVID-19 mortality between March and June 2020.The authors’ findings suggest that, at least for early periods of the pandemic, having more intensive care unit (ICU), hospital bed, or nursing capacity per COVID-19 case was associated with lower mortality, while physician availability was not. Moreover, months later there were no associations between service or physician availability and HRR COVID-19 mortality. The authors observed variations in mortality rates in places commonly thought to have been overwhelmed early in the pandemic (April 2020), as well as in cities (Boston, Philadelphia, Hartford, Detroit, and Camden, New Jersey) that had a less prominent place in the news at that time.

Larger hospitals tend to have the resources necessary to make wholesale changes when preparing for a pandemic wave. Thus, Janke et al’s results may not have fully captured the pandemic’s potential impact in settings with fewer resources or in smaller hospitals, which are currently being overwhelmed.³

The number of cases and hospitalizations in this third wave of COVID-19 continues to rise, and the strain on healthcare resources has been felt across entire regions, making the results of this study even more salient. Hospital outcomes for COVID-19 are sensitive to limitations in physical locations (number of beds, ICU capacity) and nursing capacity. Nurses more often are assigned specifically to a bed or unit, and the number of patients per nurse is limited by state or local statute. Innovations such as COVID-19 field hospitals or redeploying existing beds (eg, converting postanesthesia care units to ICUs) offset physically constrained resources.⁴ On the other hand, lower acuity in this phase of the pandemic (eg, fewer ICU admissions) and shorter lengths of stay may produce higher turnover, producing more workforce stress, regardless of bed availability.

Early work of our COVID-19 collaborative⁵ suggests that the focus on localizing patients to geographic units or teams has given way to strategies that utilize more flexible team and bed-finding approaches. Clinical care has evolved to focus on more aggressive discharge strategies, with remote monitoring and hospital-at-home capabilities. Overall, the pandemic is providing fodder for future studies examining interaction between case volumes, physician and nurse availability, and evolution in clinical care practices. Most critically, it provides an opportunity to study health system flexibility and robustness with a lens that incorporates a view of the hospital and its surroundings as tightly related parts of care delivery. Because if there is one thing the pandemic is teaching us, it is that, more than ever, no hospital can be an island unto itself, and each hospital is part of a larger ecosystem where rising tides are felt throughout.

The early phase of the COVID-19 pandemic was an extraordinarily uncertain, yet innovative, time.¹ Few data describe site-level effects of the many adaptations made to deal with surging case numbers, but studies of larger hospital referral regions (HRR) provide important clues.

In this issue of the Journal of Hospital Medicine, Janke et al² describe how availability of hospital resources in a region relate to COVID-19 mortality between March and June 2020.The authors’ findings suggest that, at least for early periods of the pandemic, having more intensive care unit (ICU), hospital bed, or nursing capacity per COVID-19 case was associated with lower mortality, while physician availability was not. Moreover, months later there were no associations between service or physician availability and HRR COVID-19 mortality. The authors observed variations in mortality rates in places commonly thought to have been overwhelmed early in the pandemic (April 2020), as well as in cities (Boston, Philadelphia, Hartford, Detroit, and Camden, New Jersey) that had a less prominent place in the news at that time.

Larger hospitals tend to have the resources necessary to make wholesale changes when preparing for a pandemic wave. Thus, Janke et al’s results may not have fully captured the pandemic’s potential impact in settings with fewer resources or in smaller hospitals, which are currently being overwhelmed.³

The number of cases and hospitalizations in this third wave of COVID-19 continues to rise, and the strain on healthcare resources has been felt across entire regions, making the results of this study even more salient. Hospital outcomes for COVID-19 are sensitive to limitations in physical locations (number of beds, ICU capacity) and nursing capacity. Nurses more often are assigned specifically to a bed or unit, and the number of patients per nurse is limited by state or local statute. Innovations such as COVID-19 field hospitals or redeploying existing beds (eg, converting postanesthesia care units to ICUs) offset physically constrained resources.⁴ On the other hand, lower acuity in this phase of the pandemic (eg, fewer ICU admissions) and shorter lengths of stay may produce higher turnover, producing more workforce stress, regardless of bed availability.

Early work of our COVID-19 collaborative⁵ suggests that the focus on localizing patients to geographic units or teams has given way to strategies that utilize more flexible team and bed-finding approaches. Clinical care has evolved to focus on more aggressive discharge strategies, with remote monitoring and hospital-at-home capabilities. Overall, the pandemic is providing fodder for future studies examining interaction between case volumes, physician and nurse availability, and evolution in clinical care practices. Most critically, it provides an opportunity to study health system flexibility and robustness with a lens that incorporates a view of the hospital and its surroundings as tightly related parts of care delivery. Because if there is one thing the pandemic is teaching us, it is that, more than ever, no hospital can be an island unto itself, and each hospital is part of a larger ecosystem where rising tides are felt throughout.

The early phase of the COVID-19 pandemic was an extraordinarily uncertain, yet innovative, time.¹ Few data describe site-level effects of the many adaptations made to deal with surging case numbers, but studies of larger hospital referral regions (HRR) provide important clues.

In this issue of the Journal of Hospital Medicine, Janke et al² describe how availability of hospital resources in a region relate to COVID-19 mortality between March and June 2020.The authors’ findings suggest that, at least for early periods of the pandemic, having more intensive care unit (ICU), hospital bed, or nursing capacity per COVID-19 case was associated with lower mortality, while physician availability was not. Moreover, months later there were no associations between service or physician availability and HRR COVID-19 mortality. The authors observed variations in mortality rates in places commonly thought to have been overwhelmed early in the pandemic (April 2020), as well as in cities (Boston, Philadelphia, Hartford, Detroit, and Camden, New Jersey) that had a less prominent place in the news at that time.

Larger hospitals tend to have the resources necessary to make wholesale changes when preparing for a pandemic wave. Thus, Janke et al’s results may not have fully captured the pandemic’s potential impact in settings with fewer resources or in smaller hospitals, which are currently being overwhelmed.³

The number of cases and hospitalizations in this third wave of COVID-19 continues to rise, and the strain on healthcare resources has been felt across entire regions, making the results of this study even more salient. Hospital outcomes for COVID-19 are sensitive to limitations in physical locations (number of beds, ICU capacity) and nursing capacity. Nurses more often are assigned specifically to a bed or unit, and the number of patients per nurse is limited by state or local statute. Innovations such as COVID-19 field hospitals or redeploying existing beds (eg, converting postanesthesia care units to ICUs) offset physically constrained resources.⁴ On the other hand, lower acuity in this phase of the pandemic (eg, fewer ICU admissions) and shorter lengths of stay may produce higher turnover, producing more workforce stress, regardless of bed availability.

Early work of our COVID-19 collaborative⁵ suggests that the focus on localizing patients to geographic units or teams has given way to strategies that utilize more flexible team and bed-finding approaches. Clinical care has evolved to focus on more aggressive discharge strategies, with remote monitoring and hospital-at-home capabilities. Overall, the pandemic is providing fodder for future studies examining interaction between case volumes, physician and nurse availability, and evolution in clinical care practices. Most critically, it provides an opportunity to study health system flexibility and robustness with a lens that incorporates a view of the hospital and its surroundings as tightly related parts of care delivery. Because if there is one thing the pandemic is teaching us, it is that, more than ever, no hospital can be an island unto itself, and each hospital is part of a larger ecosystem where rising tides are felt throughout.

Several studies have examined variation in outcomes of patients with COVID-19, with emphasis on hospital-level factors such as geographic location, workforce and resource availability, and COVID-19 community prevalence.^1,2 Block et al¹ examine variation in COVID-19 mortality across 117 US hospitals, exploring whether COVID-19 admission volume was associated with mortality. While their results suggest that patients admitted to hospitals in the highest quintiles of COVID-19 caseload had higher odds of in-hospital death, the authors were not able to fully adjust for severity of illness, tempering our ability to draw conclusions. However, their finding is consistent with work showing that emergency department crowding and high hospital utilization are associated with excess mortality.

Block et al¹ also found variation within quintiles of COVID-19 burden, suggesting that other hospital-level factors are influencing their performance. In response to the initial surge of COVID-19 in the United States, hospitals and healthcare systems made rapid, often major, adjustments to provide care. Four interdependent components contribute to an effective surge response: system, space, staff, and supplies. Although all four components are important, effective systems are critical. Systems domains include command, or the creation of leadership teams throughout the organization; control, or management, of infrastructure; communication of rapid, comprehensible messages internally and externally; coordination of resources across departments and professions; and continuity of operations.³ Little is known about how well hospitals have implemented these systems components throughout the pandemic, and while Janke et al² examined the association of resources with outcomes, neither their study nor Block et al’s was able to account for other organizational or systems-based aspects of surge response.

Studies that help us understand the organizational factors and care-delivery adaptations associated with better outcomes for patients with COVID-19 are sorely needed, and could provide important insights for organizational adaptation and change more generally. Janke et al² and, in their accompanying editorial, Auerbach and Greysen,⁴ call for “innovative protocols” and “flexibility” to meet the needs of high-demand, novel situations. However, organizations’ ability to innovate and adapt relies on their relationships and teamwork capability.

The relational infrastructure within an organization provides the basis for effective teamwork, facilitating other aspects of an organization’s surge response and ability to adapt. Relationships characterized by trust and mindfulness create a context of psychological safety that encourages sharing new ideas, and enable teams to rapidly make sense of new situations and create shared understandings that facilitate effective action: improvising, adapting, and learning. Trust and psychological safety are especially important during crises, as decision-making tends to evolve toward top-down processes in times of crisis.

Hospitals currently collect few data that speak to relationships and teamwork, limiting our ability to study these vital organizational characteristics and their role in the larger COVID-19 response. Surveys related to patient safety culture or provider wellness and burnout are likely the only data regularly collected by hospitals. Expanding these data to include measures of relational infrastructure will create more robust data not only to conduct research regarding organizational factors that are associated with patient outcomes, but also to allow health systems to improve relationships and teaming as a means of improving outcomes. Examples include relational coordination,⁵ relationships,⁶and learning scales.⁷

The hospitals to which patients are admitted make a difference in patient survival. The study by Block et al¹ highlights the importance of assessing the factors that enable health systems to adapt and innovate so that we can better understand hospital-level variation in outcomes.

Several studies have examined variation in outcomes of patients with COVID-19, with emphasis on hospital-level factors such as geographic location, workforce and resource availability, and COVID-19 community prevalence.^1,2 Block et al¹ examine variation in COVID-19 mortality across 117 US hospitals, exploring whether COVID-19 admission volume was associated with mortality. While their results suggest that patients admitted to hospitals in the highest quintiles of COVID-19 caseload had higher odds of in-hospital death, the authors were not able to fully adjust for severity of illness, tempering our ability to draw conclusions. However, their finding is consistent with work showing that emergency department crowding and high hospital utilization are associated with excess mortality.

Block et al¹ also found variation within quintiles of COVID-19 burden, suggesting that other hospital-level factors are influencing their performance. In response to the initial surge of COVID-19 in the United States, hospitals and healthcare systems made rapid, often major, adjustments to provide care. Four interdependent components contribute to an effective surge response: system, space, staff, and supplies. Although all four components are important, effective systems are critical. Systems domains include command, or the creation of leadership teams throughout the organization; control, or management, of infrastructure; communication of rapid, comprehensible messages internally and externally; coordination of resources across departments and professions; and continuity of operations.³ Little is known about how well hospitals have implemented these systems components throughout the pandemic, and while Janke et al² examined the association of resources with outcomes, neither their study nor Block et al’s was able to account for other organizational or systems-based aspects of surge response.

Studies that help us understand the organizational factors and care-delivery adaptations associated with better outcomes for patients with COVID-19 are sorely needed, and could provide important insights for organizational adaptation and change more generally. Janke et al² and, in their accompanying editorial, Auerbach and Greysen,⁴ call for “innovative protocols” and “flexibility” to meet the needs of high-demand, novel situations. However, organizations’ ability to innovate and adapt relies on their relationships and teamwork capability.

The relational infrastructure within an organization provides the basis for effective teamwork, facilitating other aspects of an organization’s surge response and ability to adapt. Relationships characterized by trust and mindfulness create a context of psychological safety that encourages sharing new ideas, and enable teams to rapidly make sense of new situations and create shared understandings that facilitate effective action: improvising, adapting, and learning. Trust and psychological safety are especially important during crises, as decision-making tends to evolve toward top-down processes in times of crisis.

Hospitals currently collect few data that speak to relationships and teamwork, limiting our ability to study these vital organizational characteristics and their role in the larger COVID-19 response. Surveys related to patient safety culture or provider wellness and burnout are likely the only data regularly collected by hospitals. Expanding these data to include measures of relational infrastructure will create more robust data not only to conduct research regarding organizational factors that are associated with patient outcomes, but also to allow health systems to improve relationships and teaming as a means of improving outcomes. Examples include relational coordination,⁵ relationships,⁶and learning scales.⁷

The hospitals to which patients are admitted make a difference in patient survival. The study by Block et al¹ highlights the importance of assessing the factors that enable health systems to adapt and innovate so that we can better understand hospital-level variation in outcomes.

Several studies have examined variation in outcomes of patients with COVID-19, with emphasis on hospital-level factors such as geographic location, workforce and resource availability, and COVID-19 community prevalence.^1,2 Block et al¹ examine variation in COVID-19 mortality across 117 US hospitals, exploring whether COVID-19 admission volume was associated with mortality. While their results suggest that patients admitted to hospitals in the highest quintiles of COVID-19 caseload had higher odds of in-hospital death, the authors were not able to fully adjust for severity of illness, tempering our ability to draw conclusions. However, their finding is consistent with work showing that emergency department crowding and high hospital utilization are associated with excess mortality.

Block et al¹ also found variation within quintiles of COVID-19 burden, suggesting that other hospital-level factors are influencing their performance. In response to the initial surge of COVID-19 in the United States, hospitals and healthcare systems made rapid, often major, adjustments to provide care. Four interdependent components contribute to an effective surge response: system, space, staff, and supplies. Although all four components are important, effective systems are critical. Systems domains include command, or the creation of leadership teams throughout the organization; control, or management, of infrastructure; communication of rapid, comprehensible messages internally and externally; coordination of resources across departments and professions; and continuity of operations.³ Little is known about how well hospitals have implemented these systems components throughout the pandemic, and while Janke et al² examined the association of resources with outcomes, neither their study nor Block et al’s was able to account for other organizational or systems-based aspects of surge response.

Studies that help us understand the organizational factors and care-delivery adaptations associated with better outcomes for patients with COVID-19 are sorely needed, and could provide important insights for organizational adaptation and change more generally. Janke et al² and, in their accompanying editorial, Auerbach and Greysen,⁴ call for “innovative protocols” and “flexibility” to meet the needs of high-demand, novel situations. However, organizations’ ability to innovate and adapt relies on their relationships and teamwork capability.

The relational infrastructure within an organization provides the basis for effective teamwork, facilitating other aspects of an organization’s surge response and ability to adapt. Relationships characterized by trust and mindfulness create a context of psychological safety that encourages sharing new ideas, and enable teams to rapidly make sense of new situations and create shared understandings that facilitate effective action: improvising, adapting, and learning. Trust and psychological safety are especially important during crises, as decision-making tends to evolve toward top-down processes in times of crisis.

Hospitals currently collect few data that speak to relationships and teamwork, limiting our ability to study these vital organizational characteristics and their role in the larger COVID-19 response. Surveys related to patient safety culture or provider wellness and burnout are likely the only data regularly collected by hospitals. Expanding these data to include measures of relational infrastructure will create more robust data not only to conduct research regarding organizational factors that are associated with patient outcomes, but also to allow health systems to improve relationships and teaming as a means of improving outcomes. Examples include relational coordination,⁵ relationships,⁶and learning scales.⁷

The hospitals to which patients are admitted make a difference in patient survival. The study by Block et al¹ highlights the importance of assessing the factors that enable health systems to adapt and innovate so that we can better understand hospital-level variation in outcomes.

Since the onset of the COVID-19 pandemic, the proclivity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for adults and its relative sparing of pediatric populations has been well characterized. Accordingly, policymakers have devoted significant attention to SARS-CoV-2’s impact on adult hospitals. Less consideration, however, has been given to children’s hospitals, which responded to the pandemic by suspending noncritical care encounters, conserving personal protective equipment, and implementing alternative care models.¹ While important, these strategic decisions may threaten the financial health of children’s hospitals.

In this issue of the Journal of Hospital Medicine, Synhorst et al¹ describe the impact of COVID-19 on US children’s hospitals.The authors utilized the Children’s Hospital Association’s PROSPECT database to compare year-over-year trends in healthcare encounters and hospital charges before and during the COVID-19 pandemic at 26 tertiary hospitals. The analysis focused on the first wave of COVID-19 in the United States from February through June 2020.

The results are staggering. Compared with 2019, the authors found significant decreases in healthcare encounters for all children’s hospitals beginning in March 2020, with a nadir in mid-April (corresponding to the first peak in adult hospitalizations). Inpatient bed days, emergency department (ED) visits, and surgeries decreased by a median of 36%, 65%, and 77%, respectively, per hospital during the nadir. Charges from February 1 to June 30, 2020, decreased by a median 24% per children’s hospital as compared to 2019—corresponding to a median $276 million decrease in charges per hospital. A quarter of hospitals faced more than $400 million in lost charges.¹

Why do these trends matter? Large decreases in utilization and associated charges likely represent significant unmet demand for child healthcare for both acute and chronic disease management. For example, with limited in-person evaluation available at the onset of illness, caregivers are presenting to EDs with sicker children.² With a shift to virtual care, clinicians may miss signs of child abuse from violence in the home—which can escalate during isolation.³ Children with chronic conditions may also be left without surveillance mechanisms, which may partly explain the autumn 2020 surge in acute mental health-related ED presentations.⁴ Furthermore, telemedicine may exacerbate care inequities for vulnerable populations lacking resources and/or English proficiency.

There is also a larger policy perspective to consider in evaluating these data: Because children’s hospitals largely operate in a fee-for-service reimbursement model, they often rely on marginal revenues to support mission-driven programming. In other words, revenue streams from profitable care segments (eg, elective surgeries) often help sustain institutional platforms operating at a loss, such as community safety net programs. Consequently, threats to marginal revenues can place mission-driven programming in jeopardy of being reduced or terminated.

The Synhorst et al¹ study was limited to hospital charges, which likely overestimate revenue losses based on actual reimbursements. Yet, this is the first study to quantify COVID-19’s financial toll on children’s hospitals, and charges offer a reasonable proxy for balance sheet trends. Thus, it is safe to assume that most hospitals incurred substantial losses during the 2020 fiscal year. Unfortunately, as the authors highlight, these losses differentially impacted hospitals based on existing resources¹—so some hospitals were likely forced to cut programs or reduce staff in an effort to return to profitability. In this way, COVID-19 has exposed the fragility of the fee-for-service model that children’s hospitals rely on for both patients and staff.

Children’s hospitals and the services they provide are essential to the health and well-being of children. The critical losses sustained by children’s hospitals due to COVID-19 threaten their ability to promote child health in the near and long term, with the greatest risk to vulnerable populations. Policymakers must act now to preserve these essential services for children.

Since the onset of the COVID-19 pandemic, the proclivity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for adults and its relative sparing of pediatric populations has been well characterized. Accordingly, policymakers have devoted significant attention to SARS-CoV-2’s impact on adult hospitals. Less consideration, however, has been given to children’s hospitals, which responded to the pandemic by suspending noncritical care encounters, conserving personal protective equipment, and implementing alternative care models.¹ While important, these strategic decisions may threaten the financial health of children’s hospitals.

In this issue of the Journal of Hospital Medicine, Synhorst et al¹ describe the impact of COVID-19 on US children’s hospitals.The authors utilized the Children’s Hospital Association’s PROSPECT database to compare year-over-year trends in healthcare encounters and hospital charges before and during the COVID-19 pandemic at 26 tertiary hospitals. The analysis focused on the first wave of COVID-19 in the United States from February through June 2020.

The results are staggering. Compared with 2019, the authors found significant decreases in healthcare encounters for all children’s hospitals beginning in March 2020, with a nadir in mid-April (corresponding to the first peak in adult hospitalizations). Inpatient bed days, emergency department (ED) visits, and surgeries decreased by a median of 36%, 65%, and 77%, respectively, per hospital during the nadir. Charges from February 1 to June 30, 2020, decreased by a median 24% per children’s hospital as compared to 2019—corresponding to a median $276 million decrease in charges per hospital. A quarter of hospitals faced more than $400 million in lost charges.¹

Why do these trends matter? Large decreases in utilization and associated charges likely represent significant unmet demand for child healthcare for both acute and chronic disease management. For example, with limited in-person evaluation available at the onset of illness, caregivers are presenting to EDs with sicker children.² With a shift to virtual care, clinicians may miss signs of child abuse from violence in the home—which can escalate during isolation.³ Children with chronic conditions may also be left without surveillance mechanisms, which may partly explain the autumn 2020 surge in acute mental health-related ED presentations.⁴ Furthermore, telemedicine may exacerbate care inequities for vulnerable populations lacking resources and/or English proficiency.

There is also a larger policy perspective to consider in evaluating these data: Because children’s hospitals largely operate in a fee-for-service reimbursement model, they often rely on marginal revenues to support mission-driven programming. In other words, revenue streams from profitable care segments (eg, elective surgeries) often help sustain institutional platforms operating at a loss, such as community safety net programs. Consequently, threats to marginal revenues can place mission-driven programming in jeopardy of being reduced or terminated.

The Synhorst et al¹ study was limited to hospital charges, which likely overestimate revenue losses based on actual reimbursements. Yet, this is the first study to quantify COVID-19’s financial toll on children’s hospitals, and charges offer a reasonable proxy for balance sheet trends. Thus, it is safe to assume that most hospitals incurred substantial losses during the 2020 fiscal year. Unfortunately, as the authors highlight, these losses differentially impacted hospitals based on existing resources¹—so some hospitals were likely forced to cut programs or reduce staff in an effort to return to profitability. In this way, COVID-19 has exposed the fragility of the fee-for-service model that children’s hospitals rely on for both patients and staff.

Children’s hospitals and the services they provide are essential to the health and well-being of children. The critical losses sustained by children’s hospitals due to COVID-19 threaten their ability to promote child health in the near and long term, with the greatest risk to vulnerable populations. Policymakers must act now to preserve these essential services for children.

Since the onset of the COVID-19 pandemic, the proclivity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for adults and its relative sparing of pediatric populations has been well characterized. Accordingly, policymakers have devoted significant attention to SARS-CoV-2’s impact on adult hospitals. Less consideration, however, has been given to children’s hospitals, which responded to the pandemic by suspending noncritical care encounters, conserving personal protective equipment, and implementing alternative care models.¹ While important, these strategic decisions may threaten the financial health of children’s hospitals.

In this issue of the Journal of Hospital Medicine, Synhorst et al¹ describe the impact of COVID-19 on US children’s hospitals.The authors utilized the Children’s Hospital Association’s PROSPECT database to compare year-over-year trends in healthcare encounters and hospital charges before and during the COVID-19 pandemic at 26 tertiary hospitals. The analysis focused on the first wave of COVID-19 in the United States from February through June 2020.

The results are staggering. Compared with 2019, the authors found significant decreases in healthcare encounters for all children’s hospitals beginning in March 2020, with a nadir in mid-April (corresponding to the first peak in adult hospitalizations). Inpatient bed days, emergency department (ED) visits, and surgeries decreased by a median of 36%, 65%, and 77%, respectively, per hospital during the nadir. Charges from February 1 to June 30, 2020, decreased by a median 24% per children’s hospital as compared to 2019—corresponding to a median $276 million decrease in charges per hospital. A quarter of hospitals faced more than $400 million in lost charges.¹

Why do these trends matter? Large decreases in utilization and associated charges likely represent significant unmet demand for child healthcare for both acute and chronic disease management. For example, with limited in-person evaluation available at the onset of illness, caregivers are presenting to EDs with sicker children.² With a shift to virtual care, clinicians may miss signs of child abuse from violence in the home—which can escalate during isolation.³ Children with chronic conditions may also be left without surveillance mechanisms, which may partly explain the autumn 2020 surge in acute mental health-related ED presentations.⁴ Furthermore, telemedicine may exacerbate care inequities for vulnerable populations lacking resources and/or English proficiency.

There is also a larger policy perspective to consider in evaluating these data: Because children’s hospitals largely operate in a fee-for-service reimbursement model, they often rely on marginal revenues to support mission-driven programming. In other words, revenue streams from profitable care segments (eg, elective surgeries) often help sustain institutional platforms operating at a loss, such as community safety net programs. Consequently, threats to marginal revenues can place mission-driven programming in jeopardy of being reduced or terminated.

The Synhorst et al¹ study was limited to hospital charges, which likely overestimate revenue losses based on actual reimbursements. Yet, this is the first study to quantify COVID-19’s financial toll on children’s hospitals, and charges offer a reasonable proxy for balance sheet trends. Thus, it is safe to assume that most hospitals incurred substantial losses during the 2020 fiscal year. Unfortunately, as the authors highlight, these losses differentially impacted hospitals based on existing resources¹—so some hospitals were likely forced to cut programs or reduce staff in an effort to return to profitability. In this way, COVID-19 has exposed the fragility of the fee-for-service model that children’s hospitals rely on for both patients and staff.

Children’s hospitals and the services they provide are essential to the health and well-being of children. The critical losses sustained by children’s hospitals due to COVID-19 threaten their ability to promote child health in the near and long term, with the greatest risk to vulnerable populations. Policymakers must act now to preserve these essential services for children.

Common themes run through rural communities and their health needs, yet the rurality of our nation is quite diverse. Approximately 97% of the United States is rural, and yet there is no silver bullet to resolve the health disparities that exist between urban and rural America. Differences in economic condition, infrastructure, education, racial diversity, health habits, and job opportunities contribute to the health disparities seen in rural communities.

In this issue of the Journal of Hospital Medicine, Gutierrez and colleagues¹ evaluate the implementation and outcomes of a rural telehospitalist program. In a hub-and-spoke fashion, providers at a large tertiary care hospital utilized telemedicine to round on patients with an onsite advanced practice provider at a 10-bed critical access hospital. Outcomes were examined during pre- and postimplementation periods using quantitative metrics (length of stay [LOS], readmission rate, mortality, and satisfaction) and qualitative measures based on interviews with staff. LOS was reduced in the postimplementation period, with a lower but nonsignificant readmission rate and no difference in mortality. Overall satisfaction was high, although respondents noted significant communication and technology issues. This study helps broaden telemedicine research and opens the conversation on barriers in rural implementation. 

As we increasingly focus on the provision and financing of care aimed at the health of populations, the diverse issues facing rural America remain insufficiently addressed. Probst and colleagues² suggest that population-based health policies are biased toward large urban centers. Innovations to transcend this “structural urbanism” are still fraught with obstacles, as revealed during the 2020 public health emergency (PHE). Telemedicine, once thought the solution to rural health provider shortages, has not escaped structural urbanism. Prior to the PHE, Medicare did not cover many core services delivered via telehealth, including hospitalist service codes. Waivers during the PHE have temporarily opened up reimbursement pathways. Unlike most telehospitalist services, Gutierrez and colleagues did not face these payment barriers in their study. Without claims data to inform payors on the adequacy of such services, health systems remain unable to promote telemedicine as a solution to rural access and cost issues. For example, in 2018, Yu and colleagues³ described the state of telemedicine in Minnesota, again, without claims data to inform supply or demand for hospitalist services.

Beyond payment barriers, technology issues are a challenge to rural telemedicine. Access to affordable, simple, and reliable equipment and broadband internet is key to user satisfaction. In April 2020, the Federal Communications Commission estimated that 18 million Americans had insufficient access to broadband internet.⁴ This number points to the technological hurdles of rural telemedicine. 

Medicine, a highly relationship-based “team sport,” is another barrier to successful implementation. Whether serving as supervisor or primary attending (as noted in the evaluation by JaKa and colleagues⁵), the telehospitalist must be “on stage” for both patients and remote spoke colleagues. In our experience, telehospitalists also practice in-person daytime hospital medicine at spoke sites; this further enhances their relationship and connection with the spoke community and likely contributes to high satisfaction by hub hospitalists and spoke patients and nurses. 

These factors create a clear impetus for further evaluation of rural telemedicine programs. There is an extensive range of program structures to evaluate, from cross-coverage to a 24/7 virtual hospital. Satisfaction is also relative and must contextualize quantitative measurement to historic care models. The execution of a telehospitalist program should align with the goals and objectives of spoke hospitals, as satisfaction will reflect how well hub hospitalists are able to meet those needs. Thus, multicenter studies that examine commercial financial pressures and encompass a variety of patient populations are imperative.

A hub-and-spoke telemedicine program can be a crucial resource for rural hospitals. The foundation of this model revolves around key factors, including reliable financing, access to technology, seamless communication, and engendering satisfaction among providers, staff, and patients. Research must continue for these programs to overcome the financial and structural challenges to their success.

Common themes run through rural communities and their health needs, yet the rurality of our nation is quite diverse. Approximately 97% of the United States is rural, and yet there is no silver bullet to resolve the health disparities that exist between urban and rural America. Differences in economic condition, infrastructure, education, racial diversity, health habits, and job opportunities contribute to the health disparities seen in rural communities.

In this issue of the Journal of Hospital Medicine, Gutierrez and colleagues¹ evaluate the implementation and outcomes of a rural telehospitalist program. In a hub-and-spoke fashion, providers at a large tertiary care hospital utilized telemedicine to round on patients with an onsite advanced practice provider at a 10-bed critical access hospital. Outcomes were examined during pre- and postimplementation periods using quantitative metrics (length of stay [LOS], readmission rate, mortality, and satisfaction) and qualitative measures based on interviews with staff. LOS was reduced in the postimplementation period, with a lower but nonsignificant readmission rate and no difference in mortality. Overall satisfaction was high, although respondents noted significant communication and technology issues. This study helps broaden telemedicine research and opens the conversation on barriers in rural implementation. 

As we increasingly focus on the provision and financing of care aimed at the health of populations, the diverse issues facing rural America remain insufficiently addressed. Probst and colleagues² suggest that population-based health policies are biased toward large urban centers. Innovations to transcend this “structural urbanism” are still fraught with obstacles, as revealed during the 2020 public health emergency (PHE). Telemedicine, once thought the solution to rural health provider shortages, has not escaped structural urbanism. Prior to the PHE, Medicare did not cover many core services delivered via telehealth, including hospitalist service codes. Waivers during the PHE have temporarily opened up reimbursement pathways. Unlike most telehospitalist services, Gutierrez and colleagues did not face these payment barriers in their study. Without claims data to inform payors on the adequacy of such services, health systems remain unable to promote telemedicine as a solution to rural access and cost issues. For example, in 2018, Yu and colleagues³ described the state of telemedicine in Minnesota, again, without claims data to inform supply or demand for hospitalist services.

Beyond payment barriers, technology issues are a challenge to rural telemedicine. Access to affordable, simple, and reliable equipment and broadband internet is key to user satisfaction. In April 2020, the Federal Communications Commission estimated that 18 million Americans had insufficient access to broadband internet.⁴ This number points to the technological hurdles of rural telemedicine. 

Medicine, a highly relationship-based “team sport,” is another barrier to successful implementation. Whether serving as supervisor or primary attending (as noted in the evaluation by JaKa and colleagues⁵), the telehospitalist must be “on stage” for both patients and remote spoke colleagues. In our experience, telehospitalists also practice in-person daytime hospital medicine at spoke sites; this further enhances their relationship and connection with the spoke community and likely contributes to high satisfaction by hub hospitalists and spoke patients and nurses. 

These factors create a clear impetus for further evaluation of rural telemedicine programs. There is an extensive range of program structures to evaluate, from cross-coverage to a 24/7 virtual hospital. Satisfaction is also relative and must contextualize quantitative measurement to historic care models. The execution of a telehospitalist program should align with the goals and objectives of spoke hospitals, as satisfaction will reflect how well hub hospitalists are able to meet those needs. Thus, multicenter studies that examine commercial financial pressures and encompass a variety of patient populations are imperative.

A hub-and-spoke telemedicine program can be a crucial resource for rural hospitals. The foundation of this model revolves around key factors, including reliable financing, access to technology, seamless communication, and engendering satisfaction among providers, staff, and patients. Research must continue for these programs to overcome the financial and structural challenges to their success.

Common themes run through rural communities and their health needs, yet the rurality of our nation is quite diverse. Approximately 97% of the United States is rural, and yet there is no silver bullet to resolve the health disparities that exist between urban and rural America. Differences in economic condition, infrastructure, education, racial diversity, health habits, and job opportunities contribute to the health disparities seen in rural communities.

In this issue of the Journal of Hospital Medicine, Gutierrez and colleagues¹ evaluate the implementation and outcomes of a rural telehospitalist program. In a hub-and-spoke fashion, providers at a large tertiary care hospital utilized telemedicine to round on patients with an onsite advanced practice provider at a 10-bed critical access hospital. Outcomes were examined during pre- and postimplementation periods using quantitative metrics (length of stay [LOS], readmission rate, mortality, and satisfaction) and qualitative measures based on interviews with staff. LOS was reduced in the postimplementation period, with a lower but nonsignificant readmission rate and no difference in mortality. Overall satisfaction was high, although respondents noted significant communication and technology issues. This study helps broaden telemedicine research and opens the conversation on barriers in rural implementation. 

As we increasingly focus on the provision and financing of care aimed at the health of populations, the diverse issues facing rural America remain insufficiently addressed. Probst and colleagues² suggest that population-based health policies are biased toward large urban centers. Innovations to transcend this “structural urbanism” are still fraught with obstacles, as revealed during the 2020 public health emergency (PHE). Telemedicine, once thought the solution to rural health provider shortages, has not escaped structural urbanism. Prior to the PHE, Medicare did not cover many core services delivered via telehealth, including hospitalist service codes. Waivers during the PHE have temporarily opened up reimbursement pathways. Unlike most telehospitalist services, Gutierrez and colleagues did not face these payment barriers in their study. Without claims data to inform payors on the adequacy of such services, health systems remain unable to promote telemedicine as a solution to rural access and cost issues. For example, in 2018, Yu and colleagues³ described the state of telemedicine in Minnesota, again, without claims data to inform supply or demand for hospitalist services.

Beyond payment barriers, technology issues are a challenge to rural telemedicine. Access to affordable, simple, and reliable equipment and broadband internet is key to user satisfaction. In April 2020, the Federal Communications Commission estimated that 18 million Americans had insufficient access to broadband internet.⁴ This number points to the technological hurdles of rural telemedicine. 

Medicine, a highly relationship-based “team sport,” is another barrier to successful implementation. Whether serving as supervisor or primary attending (as noted in the evaluation by JaKa and colleagues⁵), the telehospitalist must be “on stage” for both patients and remote spoke colleagues. In our experience, telehospitalists also practice in-person daytime hospital medicine at spoke sites; this further enhances their relationship and connection with the spoke community and likely contributes to high satisfaction by hub hospitalists and spoke patients and nurses. 

These factors create a clear impetus for further evaluation of rural telemedicine programs. There is an extensive range of program structures to evaluate, from cross-coverage to a 24/7 virtual hospital. Satisfaction is also relative and must contextualize quantitative measurement to historic care models. The execution of a telehospitalist program should align with the goals and objectives of spoke hospitals, as satisfaction will reflect how well hub hospitalists are able to meet those needs. Thus, multicenter studies that examine commercial financial pressures and encompass a variety of patient populations are imperative.

A hub-and-spoke telemedicine program can be a crucial resource for rural hospitals. The foundation of this model revolves around key factors, including reliable financing, access to technology, seamless communication, and engendering satisfaction among providers, staff, and patients. Research must continue for these programs to overcome the financial and structural challenges to their success.

Overutilization and low-value care are important clinical and policy problems. Their measurement is challenging because it requires detailed clinical information. Additionally, there are inherent difficulties in identifying discretionary services likely to be inappropriate or low-value and demonstrating that certain services produce little/no health benefit. Quantifying “ideal” expected testing rates—ones that would reflect minimization of inappropriate/low-value care without excluding essential, high-yield diagnostic services—presents additional challenges. Consequently, of 521 unique measures specified by national measurement programs and professional guidelines, 91.6% targeted underuse, while only 6.5% targeted overuse.¹

The potential for unintended consequences of implementing measures to eliminate overuse are a barrier to incorporating such measures into practice.² For example, measuring, reporting, and penalizing overuse of inappropriate bone scanning may lead to underuse in patients for whom scanning is crucial.² Most overuse measures based on inappropriate or low-value indications relate to imaging and medications.¹ However, there is increasing interest in overutilization measures based on a broad set of health services. Identifying low-value testing or treatments often requires a substantial degree of clinical detail to avoid the damaging inclusion of beneficial services, which may lead to unintended negative outcomes, creating skepticism among clinicians. Ultimately, getting measurement of low-value care wrong would undermine adoption of interventions to reduce overuse.

To reduce low-value care through expansive measures of provider ordering behavior,³ Ellenbogen et al⁴ derived a novel index to identify hospitals with high rates of low-yield diagnostic testing. This index is based on the concept that, in the presence of nonspecific, symptom-based principal diagnoses, a substantial proportion of (apparently) non-diagnostic related studies were probably ordered despite a low pretest probability of serious disease. Since such symptom-based diagnoses reflect the absence of a more specific diagnosis, the examinations observed are markers of physician-driven decisions leading to discretionary utilization likely to be of low-value to patients. This study fills a critical gap in dual measures of appropriateness and yield, rather than simply utilization, to advance the Choosing Wisely campaign.³

Advantages of this overuse index include its derivation from administrative data, obviating the need for electronic health records, and incorporation of diagnostic yield at the inpatient-encounter level. One study selected procedures identifiable solely with claims from a set deemed overused by professional/consumer groups.⁵ However, the yield of physician decisions in specific cases was not measured. In contrast, this novel index is derived from an assessment of diagnostic yield.⁴ Although test results are not known with certainty, the absence of a specific discharge diagnosis serves as a test result proxy. Measurement of diagnostic examination yield at the patient-level (aggregated to the hospital-level) may be applicable across hospitals with varied patient populations, which include large differences in patient and/or family preferences to seek medical attention and engage in shared decision-making. The role that patient preferences play in decisions creates a limitation in this index—while decisions for the candidate diagnostic tests are physician driven, patient demand may be a confounding factor. This index cannot therefore be considered purely a measure of physician-induced intensity of diagnostic services. Patient-reported data would enhance future analyses by more fully capturing all dimensions of care necessary to identify low-value services. Subjective outcomes are critical in completely measuring the aggregate benefits of tests and interventions judged low-value based on objective metrics. Such data would also aid in quantifying the relative contributions of patient and physician preferences in driving discretionary utilization.

Finally, the derived index is restricted to diagnostic decision-making and may not be applicable to treatment-related practice patterns. However, the literature suggests strong correlations between diagnostic and therapeutic intensity. Application of this novel index will play an important role in reducing low-value discretionary utilization.

Overutilization and low-value care are important clinical and policy problems. Their measurement is challenging because it requires detailed clinical information. Additionally, there are inherent difficulties in identifying discretionary services likely to be inappropriate or low-value and demonstrating that certain services produce little/no health benefit. Quantifying “ideal” expected testing rates—ones that would reflect minimization of inappropriate/low-value care without excluding essential, high-yield diagnostic services—presents additional challenges. Consequently, of 521 unique measures specified by national measurement programs and professional guidelines, 91.6% targeted underuse, while only 6.5% targeted overuse.¹

The potential for unintended consequences of implementing measures to eliminate overuse are a barrier to incorporating such measures into practice.² For example, measuring, reporting, and penalizing overuse of inappropriate bone scanning may lead to underuse in patients for whom scanning is crucial.² Most overuse measures based on inappropriate or low-value indications relate to imaging and medications.¹ However, there is increasing interest in overutilization measures based on a broad set of health services. Identifying low-value testing or treatments often requires a substantial degree of clinical detail to avoid the damaging inclusion of beneficial services, which may lead to unintended negative outcomes, creating skepticism among clinicians. Ultimately, getting measurement of low-value care wrong would undermine adoption of interventions to reduce overuse.

To reduce low-value care through expansive measures of provider ordering behavior,³ Ellenbogen et al⁴ derived a novel index to identify hospitals with high rates of low-yield diagnostic testing. This index is based on the concept that, in the presence of nonspecific, symptom-based principal diagnoses, a substantial proportion of (apparently) non-diagnostic related studies were probably ordered despite a low pretest probability of serious disease. Since such symptom-based diagnoses reflect the absence of a more specific diagnosis, the examinations observed are markers of physician-driven decisions leading to discretionary utilization likely to be of low-value to patients. This study fills a critical gap in dual measures of appropriateness and yield, rather than simply utilization, to advance the Choosing Wisely campaign.³

Advantages of this overuse index include its derivation from administrative data, obviating the need for electronic health records, and incorporation of diagnostic yield at the inpatient-encounter level. One study selected procedures identifiable solely with claims from a set deemed overused by professional/consumer groups.⁵ However, the yield of physician decisions in specific cases was not measured. In contrast, this novel index is derived from an assessment of diagnostic yield.⁴ Although test results are not known with certainty, the absence of a specific discharge diagnosis serves as a test result proxy. Measurement of diagnostic examination yield at the patient-level (aggregated to the hospital-level) may be applicable across hospitals with varied patient populations, which include large differences in patient and/or family preferences to seek medical attention and engage in shared decision-making. The role that patient preferences play in decisions creates a limitation in this index—while decisions for the candidate diagnostic tests are physician driven, patient demand may be a confounding factor. This index cannot therefore be considered purely a measure of physician-induced intensity of diagnostic services. Patient-reported data would enhance future analyses by more fully capturing all dimensions of care necessary to identify low-value services. Subjective outcomes are critical in completely measuring the aggregate benefits of tests and interventions judged low-value based on objective metrics. Such data would also aid in quantifying the relative contributions of patient and physician preferences in driving discretionary utilization.

Finally, the derived index is restricted to diagnostic decision-making and may not be applicable to treatment-related practice patterns. However, the literature suggests strong correlations between diagnostic and therapeutic intensity. Application of this novel index will play an important role in reducing low-value discretionary utilization.

Overutilization and low-value care are important clinical and policy problems. Their measurement is challenging because it requires detailed clinical information. Additionally, there are inherent difficulties in identifying discretionary services likely to be inappropriate or low-value and demonstrating that certain services produce little/no health benefit. Quantifying “ideal” expected testing rates—ones that would reflect minimization of inappropriate/low-value care without excluding essential, high-yield diagnostic services—presents additional challenges. Consequently, of 521 unique measures specified by national measurement programs and professional guidelines, 91.6% targeted underuse, while only 6.5% targeted overuse.¹

The potential for unintended consequences of implementing measures to eliminate overuse are a barrier to incorporating such measures into practice.² For example, measuring, reporting, and penalizing overuse of inappropriate bone scanning may lead to underuse in patients for whom scanning is crucial.² Most overuse measures based on inappropriate or low-value indications relate to imaging and medications.¹ However, there is increasing interest in overutilization measures based on a broad set of health services. Identifying low-value testing or treatments often requires a substantial degree of clinical detail to avoid the damaging inclusion of beneficial services, which may lead to unintended negative outcomes, creating skepticism among clinicians. Ultimately, getting measurement of low-value care wrong would undermine adoption of interventions to reduce overuse.

To reduce low-value care through expansive measures of provider ordering behavior,³ Ellenbogen et al⁴ derived a novel index to identify hospitals with high rates of low-yield diagnostic testing. This index is based on the concept that, in the presence of nonspecific, symptom-based principal diagnoses, a substantial proportion of (apparently) non-diagnostic related studies were probably ordered despite a low pretest probability of serious disease. Since such symptom-based diagnoses reflect the absence of a more specific diagnosis, the examinations observed are markers of physician-driven decisions leading to discretionary utilization likely to be of low-value to patients. This study fills a critical gap in dual measures of appropriateness and yield, rather than simply utilization, to advance the Choosing Wisely campaign.³

Advantages of this overuse index include its derivation from administrative data, obviating the need for electronic health records, and incorporation of diagnostic yield at the inpatient-encounter level. One study selected procedures identifiable solely with claims from a set deemed overused by professional/consumer groups.⁵ However, the yield of physician decisions in specific cases was not measured. In contrast, this novel index is derived from an assessment of diagnostic yield.⁴ Although test results are not known with certainty, the absence of a specific discharge diagnosis serves as a test result proxy. Measurement of diagnostic examination yield at the patient-level (aggregated to the hospital-level) may be applicable across hospitals with varied patient populations, which include large differences in patient and/or family preferences to seek medical attention and engage in shared decision-making. The role that patient preferences play in decisions creates a limitation in this index—while decisions for the candidate diagnostic tests are physician driven, patient demand may be a confounding factor. This index cannot therefore be considered purely a measure of physician-induced intensity of diagnostic services. Patient-reported data would enhance future analyses by more fully capturing all dimensions of care necessary to identify low-value services. Subjective outcomes are critical in completely measuring the aggregate benefits of tests and interventions judged low-value based on objective metrics. Such data would also aid in quantifying the relative contributions of patient and physician preferences in driving discretionary utilization.

Finally, the derived index is restricted to diagnostic decision-making and may not be applicable to treatment-related practice patterns. However, the literature suggests strong correlations between diagnostic and therapeutic intensity. Application of this novel index will play an important role in reducing low-value discretionary utilization.

During the early phase of the novel coronavirus disease 2019 (COVID-19) epidemic in the United States, public health strategies focused on “flattening the curve” to ensure that healthcare systems in hard-hit regions had the ability to care for surges of acutely ill patients. Now, COVID-19 cases and hospitalizations are rising sharply throughout the country, and many healthcare systems are facing intense strain due to an influx of patients.

In this issue of JHM, Horwitz et al provide important insights on evolving inpatient care and healthcare system strain for patients with COVID-19. The authors evaluated 5,121 adults hospitalized with SARS-CoV-2 infection at a 3-hospital health system in New York City from March through August 2020,¹ and found that patients hospitalized later during the time period were much younger and had fewer comorbidities. Importantly, the authors observed a marked decline in adjusted in-hospital mortality or hospice rates, from 25.6% in March to 7.6% in August.

What might explain the dramatic improvement in risk-adjusted mortality? The authors’ use of granular data from the electronic health record allowed them to account for temporal changes in demographics and clinical severity of hospitalized patients, indicating that other factors have contributed to the decline in adjusted mortality. One likely explanation is that increasing clinical experience in the management of patients with COVID-19 has resulted in the delivery of better inpatient care, while the use of evidence-based therapies for COVID-19 has also grown. Although important gains have been made in treatment, the care of patients with COVID-19 largely remains supportive. But supportive care requires an adequate number of hospital beds, healthcare staff, and sufficient critical care resources, at minimum.

Healthcare system strain has undoubtedly played a critical role in the outcomes of hospitalized patients. Horwitz et al found that the number of COVID-19 hospitalizations in March and April, when death rates were highest, was more than 10 times greater than in July and August, when death rates were lowest. As noted in the early epidemic in China, COVID-19 death rates partially reflect access to high-quality medical care.² And, in the US, hospitals’ capacity to care for critically ill patients with COVID-19 is an important predictor of death.³

As COVID-19 cases now surge across the country, ensuring that healthcare systems have the resources needed to care for patients will be paramount. Unfortunately, the spread of COVID-19 is exponential, while hospitals’ ability to scale-up surge capacity over a short timeframe is not. Already, reports are emerging across the country of hospitals reaching bed capacity and experiencing shortages of physicians and nurses.

To curtail escalating healthcare system stress in the coming months, we must minimize the cluster-based super-spreading that drives epidemic surges. Approximately 15% to 20% of infected cases account for up to 80% of disease transmission.⁴ Therefore, strategies must address high-risk scenarios that involve crowding, close prolonged contact, and poor ventilation, such as weddings, sporting events, religious gatherings, and indoor dining and bars.

Without adequate testing or tracing capacity during viral surges, employing nonpharmaceutical interventions to mitigate spread is key. Japan, which created the “3 Cs” campaign (avoid close contact, closed spaces, and crowds), utilized a response framework that specifically targeted super-spreading. The US should follow a similar strategy in the coming months to protect healthcare systems, healthcare workers, and most importantly, our patients.

During the early phase of the novel coronavirus disease 2019 (COVID-19) epidemic in the United States, public health strategies focused on “flattening the curve” to ensure that healthcare systems in hard-hit regions had the ability to care for surges of acutely ill patients. Now, COVID-19 cases and hospitalizations are rising sharply throughout the country, and many healthcare systems are facing intense strain due to an influx of patients.

In this issue of JHM, Horwitz et al provide important insights on evolving inpatient care and healthcare system strain for patients with COVID-19. The authors evaluated 5,121 adults hospitalized with SARS-CoV-2 infection at a 3-hospital health system in New York City from March through August 2020,¹ and found that patients hospitalized later during the time period were much younger and had fewer comorbidities. Importantly, the authors observed a marked decline in adjusted in-hospital mortality or hospice rates, from 25.6% in March to 7.6% in August.

What might explain the dramatic improvement in risk-adjusted mortality? The authors’ use of granular data from the electronic health record allowed them to account for temporal changes in demographics and clinical severity of hospitalized patients, indicating that other factors have contributed to the decline in adjusted mortality. One likely explanation is that increasing clinical experience in the management of patients with COVID-19 has resulted in the delivery of better inpatient care, while the use of evidence-based therapies for COVID-19 has also grown. Although important gains have been made in treatment, the care of patients with COVID-19 largely remains supportive. But supportive care requires an adequate number of hospital beds, healthcare staff, and sufficient critical care resources, at minimum.

Healthcare system strain has undoubtedly played a critical role in the outcomes of hospitalized patients. Horwitz et al found that the number of COVID-19 hospitalizations in March and April, when death rates were highest, was more than 10 times greater than in July and August, when death rates were lowest. As noted in the early epidemic in China, COVID-19 death rates partially reflect access to high-quality medical care.² And, in the US, hospitals’ capacity to care for critically ill patients with COVID-19 is an important predictor of death.³

As COVID-19 cases now surge across the country, ensuring that healthcare systems have the resources needed to care for patients will be paramount. Unfortunately, the spread of COVID-19 is exponential, while hospitals’ ability to scale-up surge capacity over a short timeframe is not. Already, reports are emerging across the country of hospitals reaching bed capacity and experiencing shortages of physicians and nurses.

To curtail escalating healthcare system stress in the coming months, we must minimize the cluster-based super-spreading that drives epidemic surges. Approximately 15% to 20% of infected cases account for up to 80% of disease transmission.⁴ Therefore, strategies must address high-risk scenarios that involve crowding, close prolonged contact, and poor ventilation, such as weddings, sporting events, religious gatherings, and indoor dining and bars.

Without adequate testing or tracing capacity during viral surges, employing nonpharmaceutical interventions to mitigate spread is key. Japan, which created the “3 Cs” campaign (avoid close contact, closed spaces, and crowds), utilized a response framework that specifically targeted super-spreading. The US should follow a similar strategy in the coming months to protect healthcare systems, healthcare workers, and most importantly, our patients.

During the early phase of the novel coronavirus disease 2019 (COVID-19) epidemic in the United States, public health strategies focused on “flattening the curve” to ensure that healthcare systems in hard-hit regions had the ability to care for surges of acutely ill patients. Now, COVID-19 cases and hospitalizations are rising sharply throughout the country, and many healthcare systems are facing intense strain due to an influx of patients.

In this issue of JHM, Horwitz et al provide important insights on evolving inpatient care and healthcare system strain for patients with COVID-19. The authors evaluated 5,121 adults hospitalized with SARS-CoV-2 infection at a 3-hospital health system in New York City from March through August 2020,¹ and found that patients hospitalized later during the time period were much younger and had fewer comorbidities. Importantly, the authors observed a marked decline in adjusted in-hospital mortality or hospice rates, from 25.6% in March to 7.6% in August.

What might explain the dramatic improvement in risk-adjusted mortality? The authors’ use of granular data from the electronic health record allowed them to account for temporal changes in demographics and clinical severity of hospitalized patients, indicating that other factors have contributed to the decline in adjusted mortality. One likely explanation is that increasing clinical experience in the management of patients with COVID-19 has resulted in the delivery of better inpatient care, while the use of evidence-based therapies for COVID-19 has also grown. Although important gains have been made in treatment, the care of patients with COVID-19 largely remains supportive. But supportive care requires an adequate number of hospital beds, healthcare staff, and sufficient critical care resources, at minimum.

Healthcare system strain has undoubtedly played a critical role in the outcomes of hospitalized patients. Horwitz et al found that the number of COVID-19 hospitalizations in March and April, when death rates were highest, was more than 10 times greater than in July and August, when death rates were lowest. As noted in the early epidemic in China, COVID-19 death rates partially reflect access to high-quality medical care.² And, in the US, hospitals’ capacity to care for critically ill patients with COVID-19 is an important predictor of death.³

As COVID-19 cases now surge across the country, ensuring that healthcare systems have the resources needed to care for patients will be paramount. Unfortunately, the spread of COVID-19 is exponential, while hospitals’ ability to scale-up surge capacity over a short timeframe is not. Already, reports are emerging across the country of hospitals reaching bed capacity and experiencing shortages of physicians and nurses.

To curtail escalating healthcare system stress in the coming months, we must minimize the cluster-based super-spreading that drives epidemic surges. Approximately 15% to 20% of infected cases account for up to 80% of disease transmission.⁴ Therefore, strategies must address high-risk scenarios that involve crowding, close prolonged contact, and poor ventilation, such as weddings, sporting events, religious gatherings, and indoor dining and bars.

Without adequate testing or tracing capacity during viral surges, employing nonpharmaceutical interventions to mitigate spread is key. Japan, which created the “3 Cs” campaign (avoid close contact, closed spaces, and crowds), utilized a response framework that specifically targeted super-spreading. The US should follow a similar strategy in the coming months to protect healthcare systems, healthcare workers, and most importantly, our patients.