Editorial

A significant literature describes efforts to reduce hospital readmissions through improving care transitions. Many approaches have been tried, alone or in combination, targeting different points across the spectrum of discharge activities. These approaches encompass interventions initiated prior to discharge, such as patient education and enhanced discharge planning; bridging interventions, such as transition coaches; and postdischarge interventions, such as home visits or early follow-up appointments. Transitions of care clinics (TOCC) attempt to improve posthospital care by providing dedicated, rapid follow-up for patients after discharge.¹

The impact of care transitions interventions is mixed, with inconsistent results across interventions and contexts. More complex, multipronged, context- and patient-sensitive interventions, however, are more likely to be associated with lower readmission rates.^2,3

In this issue of the journal, Griffin and colleagues⁴ report on their TOCC implementation. Their focus on a high-risk, rural veteran population is different from prior studies, as is their use of in-person or virtual follow-up options. While the authors describe their intervention as a TOCC, their model serves as an organizer for an interprofessional team, including hospitalists, that coordinates multiple activities that complement the postdischarge appointments: identification of high-risk patients, pharmacist-led medication reconciliation, dietary counseling, contingency planning for potential changes, follow-up on pending tests and studies, and coordination of primary care and specialty care appointments. The multipronged, patient-sensitive nature of their intervention makes their positive findings consistent with other care transition literature.

Griffin and colleagues’ reporting of their TOCC experience is worth highlighting, as they present their experience and results in a way that maximizes our ability to learn from their implementation. Unfortunately, reports of improvement initiatives often lack sufficient detail regarding the context or intervention to potentially apply their findings. Griffin and colleagues applied the Revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0) guidelines, a standardized framework for describing improvement initiatives that captures critical contextual and intervention elements.⁵Griffin and colleagues describe their baseline readmission performance and how the TOCC model was relevant to this issue. They describe the context, including their patient population, and their intervention with sufficient detail for us to understand what they actually did. Importantly, Griffin and colleagues clearly delineate the dynamic phases of the implementation, their use of Plan-Do-Study-Act cycles to assess and improve their implementation, and the specific changes they made. The Figure clearly puts their results in the context of their program evolution, and their secondary outcomes support our understanding of program growth. Their use of a committee for ongoing monitoring could be important for ongoing adaptation and sustainability.

There are several limitations worth noting. There may have been subjectivity in teams’ decisions to refer specific patients with lower Care Assessment Need scores. We do not know why patients did not attend TOCC visits, or why they chose virtual vs in-person visits. This study was conducted within the Veterans Affairs system, where program supports, such as tablets for virtual visits and coordination among services, may be easier to implement than in other settings. Despite these limitations, we see that complex, high-risk patients benefit from a multidisciplinary, multipronged approach to care transitions. Moreover, we learned about barriers encountered during TOCC implementation and how these issues were successfully addressed. Finally, their work suggests that telehealth may be an appealing and promising component of care transition programs, but that patients may not choose this modality solely because of geography.

In this era of multisite collaborative studies and analyses of large administrative datasets, Griffin et al⁴ demonstrate that there is still much to learn from a well-done, single-site improvement study.

Funding: Drs Leykum and Penney reported funding from the Department of Veterans Affairs.

A significant literature describes efforts to reduce hospital readmissions through improving care transitions. Many approaches have been tried, alone or in combination, targeting different points across the spectrum of discharge activities. These approaches encompass interventions initiated prior to discharge, such as patient education and enhanced discharge planning; bridging interventions, such as transition coaches; and postdischarge interventions, such as home visits or early follow-up appointments. Transitions of care clinics (TOCC) attempt to improve posthospital care by providing dedicated, rapid follow-up for patients after discharge.¹

The impact of care transitions interventions is mixed, with inconsistent results across interventions and contexts. More complex, multipronged, context- and patient-sensitive interventions, however, are more likely to be associated with lower readmission rates.^2,3

In this issue of the journal, Griffin and colleagues⁴ report on their TOCC implementation. Their focus on a high-risk, rural veteran population is different from prior studies, as is their use of in-person or virtual follow-up options. While the authors describe their intervention as a TOCC, their model serves as an organizer for an interprofessional team, including hospitalists, that coordinates multiple activities that complement the postdischarge appointments: identification of high-risk patients, pharmacist-led medication reconciliation, dietary counseling, contingency planning for potential changes, follow-up on pending tests and studies, and coordination of primary care and specialty care appointments. The multipronged, patient-sensitive nature of their intervention makes their positive findings consistent with other care transition literature.

Griffin and colleagues’ reporting of their TOCC experience is worth highlighting, as they present their experience and results in a way that maximizes our ability to learn from their implementation. Unfortunately, reports of improvement initiatives often lack sufficient detail regarding the context or intervention to potentially apply their findings. Griffin and colleagues applied the Revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0) guidelines, a standardized framework for describing improvement initiatives that captures critical contextual and intervention elements.⁵Griffin and colleagues describe their baseline readmission performance and how the TOCC model was relevant to this issue. They describe the context, including their patient population, and their intervention with sufficient detail for us to understand what they actually did. Importantly, Griffin and colleagues clearly delineate the dynamic phases of the implementation, their use of Plan-Do-Study-Act cycles to assess and improve their implementation, and the specific changes they made. The Figure clearly puts their results in the context of their program evolution, and their secondary outcomes support our understanding of program growth. Their use of a committee for ongoing monitoring could be important for ongoing adaptation and sustainability.

There are several limitations worth noting. There may have been subjectivity in teams’ decisions to refer specific patients with lower Care Assessment Need scores. We do not know why patients did not attend TOCC visits, or why they chose virtual vs in-person visits. This study was conducted within the Veterans Affairs system, where program supports, such as tablets for virtual visits and coordination among services, may be easier to implement than in other settings. Despite these limitations, we see that complex, high-risk patients benefit from a multidisciplinary, multipronged approach to care transitions. Moreover, we learned about barriers encountered during TOCC implementation and how these issues were successfully addressed. Finally, their work suggests that telehealth may be an appealing and promising component of care transition programs, but that patients may not choose this modality solely because of geography.

In this era of multisite collaborative studies and analyses of large administrative datasets, Griffin et al⁴ demonstrate that there is still much to learn from a well-done, single-site improvement study.

Funding: Drs Leykum and Penney reported funding from the Department of Veterans Affairs.

A significant literature describes efforts to reduce hospital readmissions through improving care transitions. Many approaches have been tried, alone or in combination, targeting different points across the spectrum of discharge activities. These approaches encompass interventions initiated prior to discharge, such as patient education and enhanced discharge planning; bridging interventions, such as transition coaches; and postdischarge interventions, such as home visits or early follow-up appointments. Transitions of care clinics (TOCC) attempt to improve posthospital care by providing dedicated, rapid follow-up for patients after discharge.¹

The impact of care transitions interventions is mixed, with inconsistent results across interventions and contexts. More complex, multipronged, context- and patient-sensitive interventions, however, are more likely to be associated with lower readmission rates.^2,3

In this issue of the journal, Griffin and colleagues⁴ report on their TOCC implementation. Their focus on a high-risk, rural veteran population is different from prior studies, as is their use of in-person or virtual follow-up options. While the authors describe their intervention as a TOCC, their model serves as an organizer for an interprofessional team, including hospitalists, that coordinates multiple activities that complement the postdischarge appointments: identification of high-risk patients, pharmacist-led medication reconciliation, dietary counseling, contingency planning for potential changes, follow-up on pending tests and studies, and coordination of primary care and specialty care appointments. The multipronged, patient-sensitive nature of their intervention makes their positive findings consistent with other care transition literature.

Griffin and colleagues’ reporting of their TOCC experience is worth highlighting, as they present their experience and results in a way that maximizes our ability to learn from their implementation. Unfortunately, reports of improvement initiatives often lack sufficient detail regarding the context or intervention to potentially apply their findings. Griffin and colleagues applied the Revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0) guidelines, a standardized framework for describing improvement initiatives that captures critical contextual and intervention elements.⁵Griffin and colleagues describe their baseline readmission performance and how the TOCC model was relevant to this issue. They describe the context, including their patient population, and their intervention with sufficient detail for us to understand what they actually did. Importantly, Griffin and colleagues clearly delineate the dynamic phases of the implementation, their use of Plan-Do-Study-Act cycles to assess and improve their implementation, and the specific changes they made. The Figure clearly puts their results in the context of their program evolution, and their secondary outcomes support our understanding of program growth. Their use of a committee for ongoing monitoring could be important for ongoing adaptation and sustainability.

There are several limitations worth noting. There may have been subjectivity in teams’ decisions to refer specific patients with lower Care Assessment Need scores. We do not know why patients did not attend TOCC visits, or why they chose virtual vs in-person visits. This study was conducted within the Veterans Affairs system, where program supports, such as tablets for virtual visits and coordination among services, may be easier to implement than in other settings. Despite these limitations, we see that complex, high-risk patients benefit from a multidisciplinary, multipronged approach to care transitions. Moreover, we learned about barriers encountered during TOCC implementation and how these issues were successfully addressed. Finally, their work suggests that telehealth may be an appealing and promising component of care transition programs, but that patients may not choose this modality solely because of geography.

In this era of multisite collaborative studies and analyses of large administrative datasets, Griffin et al⁴ demonstrate that there is still much to learn from a well-done, single-site improvement study.

Funding: Drs Leykum and Penney reported funding from the Department of Veterans Affairs.

Adults admitted to general medical-surgical wards who experience in-hospital deterioration have a disproportionate effect on hospital mortality and length of stay.¹ Not long ago, systematic electronic capture of vital signs—arguably the most important predictors of impending deterioration—was restricted to intensive care units (ICUs). Deployment of comprehensive electronic health records (EHRs) and handheld charting tools have made vital signs data more accessible, expanding the possibilities of early detection.

In this issue, Peelen et al² report their scoping review of contemporary EHR-based predictive models for identifying ward patients at risk for deterioration. They identified 22 publications suitable for review. Impressively, some studies report extraordinary statistical performance, with positive predictive values (PPVs) exceeding 50% and with 12- to 24-hour lead times to prepare a clinician response. However, only five algorithms were implemented in an EHR and only three were used clinically. Peelen et al also quantified 48 barriers to and 54 facilitators of the implementation and use of these models. Improved statistical performance (higher PPVs) compared to manually assigned scores were the most important facilitators, while implementation in the context of daily practice (alarm fatigue, integration with existing workflows) were the most important barriers.

These reports invite an obvious question: If the models are this good, why have we not seen more reports of improved patient outcomes? Based on our own recent experience successfully deploying and evaluating the Advance Alert Monitor Program for early detection in a 21-hospital system,³ we suspect that there are several factors at play. Despite the relative computational ease of developing high-performing predictive models, it can be very challenging to create the right dataset (extracting and formatting data, standardizing variable definitions across different EHR builds). Investigators may also underestimate the difficulty of what can be implemented—and sustained—in real-world clinical practice. We encountered substantial difficulty, for example, around alarm fatigue mitigation and the relationship of alerts to end-of-life decisions. Greater attention to implementation is necessary to advance the field.

We suggest that four critical questions be considered when creating in-hospital predictive models. First, what are the statistical characteristics of a model around the likely clinical decision point? Simply having a high C-statistic is insufficient—what matters is the alert’s PPV at a clinically actionable threshold.⁴ Second, workflow burden—how many alerts per day at my hospital—must be measured, including other processes potentially affected by the new system. Third, will the extra work identify a meaningful proportion of the avoidable bad outcomes? Finally, how will model use affect care of patients near the end of life? Alerts for these patients may not make clinical sense and might even interfere with overall care (eg, by triggering an unwanted ICU transfer).

Implementation requires more than data scientists. Consideration must be given to system governance, predictive model maintenance (models can actually decalibrate over time!), and financing (not just the computation side—someone needs to pay for training clinicians and ensuring proper staffing of the clinical response).

Last, rigorous model evaluation must be undertaken. Given the increasing capabilities of comprehensive EHRs, patient-level randomization is becoming more feasible. But even randomized deployments present challenges. Since ward patients are a heterogeneous population, quantifying process-outcome relationships may be difficult. Alternative approaches to quantification of the impact of bundled interventions may need to be considered—not just for initial deployment, but on an ongoing basis. Peelen et al² have effectively summarized the state of published predictive models, which hold the tantalizing possibility of meaningful improvement: saved lives, decreased morbidity. Now, we must work together to address the identified gaps so that, one day, implementation of real-time models is routine, and the promise of in-hospital predictive analytics is fulfilled.

Adults admitted to general medical-surgical wards who experience in-hospital deterioration have a disproportionate effect on hospital mortality and length of stay.¹ Not long ago, systematic electronic capture of vital signs—arguably the most important predictors of impending deterioration—was restricted to intensive care units (ICUs). Deployment of comprehensive electronic health records (EHRs) and handheld charting tools have made vital signs data more accessible, expanding the possibilities of early detection.

In this issue, Peelen et al² report their scoping review of contemporary EHR-based predictive models for identifying ward patients at risk for deterioration. They identified 22 publications suitable for review. Impressively, some studies report extraordinary statistical performance, with positive predictive values (PPVs) exceeding 50% and with 12- to 24-hour lead times to prepare a clinician response. However, only five algorithms were implemented in an EHR and only three were used clinically. Peelen et al also quantified 48 barriers to and 54 facilitators of the implementation and use of these models. Improved statistical performance (higher PPVs) compared to manually assigned scores were the most important facilitators, while implementation in the context of daily practice (alarm fatigue, integration with existing workflows) were the most important barriers.

These reports invite an obvious question: If the models are this good, why have we not seen more reports of improved patient outcomes? Based on our own recent experience successfully deploying and evaluating the Advance Alert Monitor Program for early detection in a 21-hospital system,³ we suspect that there are several factors at play. Despite the relative computational ease of developing high-performing predictive models, it can be very challenging to create the right dataset (extracting and formatting data, standardizing variable definitions across different EHR builds). Investigators may also underestimate the difficulty of what can be implemented—and sustained—in real-world clinical practice. We encountered substantial difficulty, for example, around alarm fatigue mitigation and the relationship of alerts to end-of-life decisions. Greater attention to implementation is necessary to advance the field.

We suggest that four critical questions be considered when creating in-hospital predictive models. First, what are the statistical characteristics of a model around the likely clinical decision point? Simply having a high C-statistic is insufficient—what matters is the alert’s PPV at a clinically actionable threshold.⁴ Second, workflow burden—how many alerts per day at my hospital—must be measured, including other processes potentially affected by the new system. Third, will the extra work identify a meaningful proportion of the avoidable bad outcomes? Finally, how will model use affect care of patients near the end of life? Alerts for these patients may not make clinical sense and might even interfere with overall care (eg, by triggering an unwanted ICU transfer).

Implementation requires more than data scientists. Consideration must be given to system governance, predictive model maintenance (models can actually decalibrate over time!), and financing (not just the computation side—someone needs to pay for training clinicians and ensuring proper staffing of the clinical response).

Last, rigorous model evaluation must be undertaken. Given the increasing capabilities of comprehensive EHRs, patient-level randomization is becoming more feasible. But even randomized deployments present challenges. Since ward patients are a heterogeneous population, quantifying process-outcome relationships may be difficult. Alternative approaches to quantification of the impact of bundled interventions may need to be considered—not just for initial deployment, but on an ongoing basis. Peelen et al² have effectively summarized the state of published predictive models, which hold the tantalizing possibility of meaningful improvement: saved lives, decreased morbidity. Now, we must work together to address the identified gaps so that, one day, implementation of real-time models is routine, and the promise of in-hospital predictive analytics is fulfilled.

Adults admitted to general medical-surgical wards who experience in-hospital deterioration have a disproportionate effect on hospital mortality and length of stay.¹ Not long ago, systematic electronic capture of vital signs—arguably the most important predictors of impending deterioration—was restricted to intensive care units (ICUs). Deployment of comprehensive electronic health records (EHRs) and handheld charting tools have made vital signs data more accessible, expanding the possibilities of early detection.

In this issue, Peelen et al² report their scoping review of contemporary EHR-based predictive models for identifying ward patients at risk for deterioration. They identified 22 publications suitable for review. Impressively, some studies report extraordinary statistical performance, with positive predictive values (PPVs) exceeding 50% and with 12- to 24-hour lead times to prepare a clinician response. However, only five algorithms were implemented in an EHR and only three were used clinically. Peelen et al also quantified 48 barriers to and 54 facilitators of the implementation and use of these models. Improved statistical performance (higher PPVs) compared to manually assigned scores were the most important facilitators, while implementation in the context of daily practice (alarm fatigue, integration with existing workflows) were the most important barriers.

These reports invite an obvious question: If the models are this good, why have we not seen more reports of improved patient outcomes? Based on our own recent experience successfully deploying and evaluating the Advance Alert Monitor Program for early detection in a 21-hospital system,³ we suspect that there are several factors at play. Despite the relative computational ease of developing high-performing predictive models, it can be very challenging to create the right dataset (extracting and formatting data, standardizing variable definitions across different EHR builds). Investigators may also underestimate the difficulty of what can be implemented—and sustained—in real-world clinical practice. We encountered substantial difficulty, for example, around alarm fatigue mitigation and the relationship of alerts to end-of-life decisions. Greater attention to implementation is necessary to advance the field.

We suggest that four critical questions be considered when creating in-hospital predictive models. First, what are the statistical characteristics of a model around the likely clinical decision point? Simply having a high C-statistic is insufficient—what matters is the alert’s PPV at a clinically actionable threshold.⁴ Second, workflow burden—how many alerts per day at my hospital—must be measured, including other processes potentially affected by the new system. Third, will the extra work identify a meaningful proportion of the avoidable bad outcomes? Finally, how will model use affect care of patients near the end of life? Alerts for these patients may not make clinical sense and might even interfere with overall care (eg, by triggering an unwanted ICU transfer).

Implementation requires more than data scientists. Consideration must be given to system governance, predictive model maintenance (models can actually decalibrate over time!), and financing (not just the computation side—someone needs to pay for training clinicians and ensuring proper staffing of the clinical response).

Last, rigorous model evaluation must be undertaken. Given the increasing capabilities of comprehensive EHRs, patient-level randomization is becoming more feasible. But even randomized deployments present challenges. Since ward patients are a heterogeneous population, quantifying process-outcome relationships may be difficult. Alternative approaches to quantification of the impact of bundled interventions may need to be considered—not just for initial deployment, but on an ongoing basis. Peelen et al² have effectively summarized the state of published predictive models, which hold the tantalizing possibility of meaningful improvement: saved lives, decreased morbidity. Now, we must work together to address the identified gaps so that, one day, implementation of real-time models is routine, and the promise of in-hospital predictive analytics is fulfilled.

In 2016, a study was published that continues to shock observers today.¹ Examining 200 medical trainees, researchers reported that an alarming percentage of these individuals held false beliefs about Black bodies, including 22% believing that nerve endings in Black persons are less sensitive than nerve endings in White persons and 63% believing that Black skin is thicker than White skin. Furthermore, the study found that those who held these false beliefs about biological differences between Black and White individuals were also less likely to recommend pain treatment to Black patients in a follow-up case vignette. Two years later, in an evaluation of racial differences in opioid prescribing in the United States published in Epidemiology, one of the authors suggested, “It’s an extremely rare case where racial biases actually protected the population [Black individuals] being discriminated against.”²

These studies provide the background for the analysis by Rambachan et al³ published in this issue of the Journal of Hospital Medicine. The authors examined a diverse cohort of more than 10,000 patients hospitalized on a general medicine service at an academic medical center in San Francisco from 2012 to 2018. Black patients were significantly less likely to receive an opioid prescription at discharge, and when they did, were discharged on opioids for fewer days than White patients. No other racial group experienced such a disparity, with Asian patients more likely to receive opioids at discharge. Whereas these findings align with myriad studies demonstrating racial disparities in opioid prescribing,⁴ the authors focus on patients admitted to a general medicine service, where most hospitalized patients receive medical care daily.

The authors concede that determining the etiology of these disparities was beyond the scope of their study, yet this is the exact question we must answer today. Why should the color of a patient’s skin continue to determine the type, and duration, of care they receive, especially when treating pain? The authors hypothesize that individual factors such as provider bias and systemic factors, including limited guidelines on pain management, may drive the observed racial inequities. This progression from individual- and institutional- to community- and policy-level determinants offers a useful framework for understanding the drivers of disparities in opioid prescribing. It also provides an agenda for future research that can guide us from simply detecting disparities to understanding and eliminating them. Furthermore, it is important to examine care team provider characteristics, including race/ethnicity, years in practice, education level (eg, resident vs attending),⁵ experience with implicit bias training, and differential referral to specialists, such as pain, palliative care, and addiction providers. Factors associated with the facility where a patient is hospitalized also warrant further exploration, including the diversity of medical and nonmedical staff as well as patients.⁶ Examining these factors will allow us to move closer toward implementing effective interventions that eliminate disparities in pain treatment.

The authors begin to provide us with possible levers to pull to address the inequities in opioid prescribing. They suggest provider-level bias training, improved institutional tracking of disparities, and policy-level solutions to address the persistent dearth of diversity in the healthcare workforce. While these broad solutions may address health disparities across the medical field, targeted solutions are needed to directly address inequities in pain treatment. First, we must explore the reasons for disparities in the prevalence, presentation, and management of pain in Black populations. These reasons may include occupational exposures or injuries, psychological stress (often associated with racism), and a disproportionate presence of chronic medical comorbidities. Second, health systems can implement a standardized system for opioid prescribing, supported by pharmacy expertise and considering clinical diagnoses, to reduce subjectivity associated with determining the appropriateness of an opioid prescription. Third, health systems must improve access to addiction, harm reduction, and pain specialty services to effectively manage comorbid conditions in at-risk patients.⁷ Furthermore, we must look beyond traditional measures of healthcare access, such as insurance coverage, to address social determinants of health, such as distance to pharmacy, housing security, employment status, and experience with the criminal justice system, which may influence a patient’s receipt of a prescription. Finally, as a society, we must prioritize early training of healthcare providers, long before the undergraduate and graduate medical education level, to practice medicine without stigmatizing biases and stereotypes related to drug use in communities of color.⁸

The pattern of racial and ethnic disparities in healthcare has been documented for decades, with an ever-increasing depth of the different ways in which minoritized patients are undertreated. Despite this breadth of research, our understanding of the etiology of these inequities and development and implementation of interventions to reduce them remain limited. Rambachan et al³ do a commendable job highlighting further racial disparities in opioid prescribing in hospitalized patients and provide another opportunity to answer the important questions plaguing health care today: Why do these disparities exist and what can be done to address them? The urgency we take towards answering these questions will confirm our commitment to achieving antiracism in medicine and prioritizing health equity. Black lives are depending on it.

In 2016, a study was published that continues to shock observers today.¹ Examining 200 medical trainees, researchers reported that an alarming percentage of these individuals held false beliefs about Black bodies, including 22% believing that nerve endings in Black persons are less sensitive than nerve endings in White persons and 63% believing that Black skin is thicker than White skin. Furthermore, the study found that those who held these false beliefs about biological differences between Black and White individuals were also less likely to recommend pain treatment to Black patients in a follow-up case vignette. Two years later, in an evaluation of racial differences in opioid prescribing in the United States published in Epidemiology, one of the authors suggested, “It’s an extremely rare case where racial biases actually protected the population [Black individuals] being discriminated against.”²

These studies provide the background for the analysis by Rambachan et al³ published in this issue of the Journal of Hospital Medicine. The authors examined a diverse cohort of more than 10,000 patients hospitalized on a general medicine service at an academic medical center in San Francisco from 2012 to 2018. Black patients were significantly less likely to receive an opioid prescription at discharge, and when they did, were discharged on opioids for fewer days than White patients. No other racial group experienced such a disparity, with Asian patients more likely to receive opioids at discharge. Whereas these findings align with myriad studies demonstrating racial disparities in opioid prescribing,⁴ the authors focus on patients admitted to a general medicine service, where most hospitalized patients receive medical care daily.

The authors concede that determining the etiology of these disparities was beyond the scope of their study, yet this is the exact question we must answer today. Why should the color of a patient’s skin continue to determine the type, and duration, of care they receive, especially when treating pain? The authors hypothesize that individual factors such as provider bias and systemic factors, including limited guidelines on pain management, may drive the observed racial inequities. This progression from individual- and institutional- to community- and policy-level determinants offers a useful framework for understanding the drivers of disparities in opioid prescribing. It also provides an agenda for future research that can guide us from simply detecting disparities to understanding and eliminating them. Furthermore, it is important to examine care team provider characteristics, including race/ethnicity, years in practice, education level (eg, resident vs attending),⁵ experience with implicit bias training, and differential referral to specialists, such as pain, palliative care, and addiction providers. Factors associated with the facility where a patient is hospitalized also warrant further exploration, including the diversity of medical and nonmedical staff as well as patients.⁶ Examining these factors will allow us to move closer toward implementing effective interventions that eliminate disparities in pain treatment.

The authors begin to provide us with possible levers to pull to address the inequities in opioid prescribing. They suggest provider-level bias training, improved institutional tracking of disparities, and policy-level solutions to address the persistent dearth of diversity in the healthcare workforce. While these broad solutions may address health disparities across the medical field, targeted solutions are needed to directly address inequities in pain treatment. First, we must explore the reasons for disparities in the prevalence, presentation, and management of pain in Black populations. These reasons may include occupational exposures or injuries, psychological stress (often associated with racism), and a disproportionate presence of chronic medical comorbidities. Second, health systems can implement a standardized system for opioid prescribing, supported by pharmacy expertise and considering clinical diagnoses, to reduce subjectivity associated with determining the appropriateness of an opioid prescription. Third, health systems must improve access to addiction, harm reduction, and pain specialty services to effectively manage comorbid conditions in at-risk patients.⁷ Furthermore, we must look beyond traditional measures of healthcare access, such as insurance coverage, to address social determinants of health, such as distance to pharmacy, housing security, employment status, and experience with the criminal justice system, which may influence a patient’s receipt of a prescription. Finally, as a society, we must prioritize early training of healthcare providers, long before the undergraduate and graduate medical education level, to practice medicine without stigmatizing biases and stereotypes related to drug use in communities of color.⁸

The pattern of racial and ethnic disparities in healthcare has been documented for decades, with an ever-increasing depth of the different ways in which minoritized patients are undertreated. Despite this breadth of research, our understanding of the etiology of these inequities and development and implementation of interventions to reduce them remain limited. Rambachan et al³ do a commendable job highlighting further racial disparities in opioid prescribing in hospitalized patients and provide another opportunity to answer the important questions plaguing health care today: Why do these disparities exist and what can be done to address them? The urgency we take towards answering these questions will confirm our commitment to achieving antiracism in medicine and prioritizing health equity. Black lives are depending on it.

In 2016, a study was published that continues to shock observers today.¹ Examining 200 medical trainees, researchers reported that an alarming percentage of these individuals held false beliefs about Black bodies, including 22% believing that nerve endings in Black persons are less sensitive than nerve endings in White persons and 63% believing that Black skin is thicker than White skin. Furthermore, the study found that those who held these false beliefs about biological differences between Black and White individuals were also less likely to recommend pain treatment to Black patients in a follow-up case vignette. Two years later, in an evaluation of racial differences in opioid prescribing in the United States published in Epidemiology, one of the authors suggested, “It’s an extremely rare case where racial biases actually protected the population [Black individuals] being discriminated against.”²

These studies provide the background for the analysis by Rambachan et al³ published in this issue of the Journal of Hospital Medicine. The authors examined a diverse cohort of more than 10,000 patients hospitalized on a general medicine service at an academic medical center in San Francisco from 2012 to 2018. Black patients were significantly less likely to receive an opioid prescription at discharge, and when they did, were discharged on opioids for fewer days than White patients. No other racial group experienced such a disparity, with Asian patients more likely to receive opioids at discharge. Whereas these findings align with myriad studies demonstrating racial disparities in opioid prescribing,⁴ the authors focus on patients admitted to a general medicine service, where most hospitalized patients receive medical care daily.

The authors concede that determining the etiology of these disparities was beyond the scope of their study, yet this is the exact question we must answer today. Why should the color of a patient’s skin continue to determine the type, and duration, of care they receive, especially when treating pain? The authors hypothesize that individual factors such as provider bias and systemic factors, including limited guidelines on pain management, may drive the observed racial inequities. This progression from individual- and institutional- to community- and policy-level determinants offers a useful framework for understanding the drivers of disparities in opioid prescribing. It also provides an agenda for future research that can guide us from simply detecting disparities to understanding and eliminating them. Furthermore, it is important to examine care team provider characteristics, including race/ethnicity, years in practice, education level (eg, resident vs attending),⁵ experience with implicit bias training, and differential referral to specialists, such as pain, palliative care, and addiction providers. Factors associated with the facility where a patient is hospitalized also warrant further exploration, including the diversity of medical and nonmedical staff as well as patients.⁶ Examining these factors will allow us to move closer toward implementing effective interventions that eliminate disparities in pain treatment.

The authors begin to provide us with possible levers to pull to address the inequities in opioid prescribing. They suggest provider-level bias training, improved institutional tracking of disparities, and policy-level solutions to address the persistent dearth of diversity in the healthcare workforce. While these broad solutions may address health disparities across the medical field, targeted solutions are needed to directly address inequities in pain treatment. First, we must explore the reasons for disparities in the prevalence, presentation, and management of pain in Black populations. These reasons may include occupational exposures or injuries, psychological stress (often associated with racism), and a disproportionate presence of chronic medical comorbidities. Second, health systems can implement a standardized system for opioid prescribing, supported by pharmacy expertise and considering clinical diagnoses, to reduce subjectivity associated with determining the appropriateness of an opioid prescription. Third, health systems must improve access to addiction, harm reduction, and pain specialty services to effectively manage comorbid conditions in at-risk patients.⁷ Furthermore, we must look beyond traditional measures of healthcare access, such as insurance coverage, to address social determinants of health, such as distance to pharmacy, housing security, employment status, and experience with the criminal justice system, which may influence a patient’s receipt of a prescription. Finally, as a society, we must prioritize early training of healthcare providers, long before the undergraduate and graduate medical education level, to practice medicine without stigmatizing biases and stereotypes related to drug use in communities of color.⁸

The pattern of racial and ethnic disparities in healthcare has been documented for decades, with an ever-increasing depth of the different ways in which minoritized patients are undertreated. Despite this breadth of research, our understanding of the etiology of these inequities and development and implementation of interventions to reduce them remain limited. Rambachan et al³ do a commendable job highlighting further racial disparities in opioid prescribing in hospitalized patients and provide another opportunity to answer the important questions plaguing health care today: Why do these disparities exist and what can be done to address them? The urgency we take towards answering these questions will confirm our commitment to achieving antiracism in medicine and prioritizing health equity. Black lives are depending on it.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Hospital medicine procedure services have proliferated in recent years, driven by multiple synergistic factors, including an interest in improving hospital throughput, bolstering resident education, and ensuring full-spectrum practice for hospitalists. These services have become established and have demonstrated their capabilities, further catalyzed by emerging interest—and expertise—in point-of-care ultrasonography by hospitalists.

Most hospital medicine procedure services (HMPSs) focus on performing ultrasound-assisted procedures at bedside, providing purported advantages in convenience, cost, and potentially timing when compared to services performed by interventional radiology. The scope of procedures performed by HPMSs reflects the populations cared for by hospitalists, including paracentesis, thoracentesis, central venous catheter placement, lumbar puncture and, more recently, pigtail chest tube placement.^1,2 Fitting with the early development of HMPSs, initial reports regarding these services centered on optimal development of services and emphasized the question, “Are hospital medicine procedure services able to do [procedure x] as safely as radiology or the primary team?”²

Ensuring safety and quality is fundamental to implementing new workflows; however, it is now clear that HMPSs provide high-quality, safe, patient-centered bedside procedures; these services are no longer novel.³ As HMPSs mature, so too must their evaluation, research, and scholarship. It is no longer enough to document that a HMPS can perform procedures as well as interventional radiology or a standard hospital medicine care team—instead, we must identify how these services affect patient outcomes, improve education, add value, and influence the overall process of care in the hospital.

In this issue of the Journal of Hospital Medicine, Ritter and colleagues⁴ describe an important first step in this maturing field by evaluating how a HMPS affects process outcomes in the context of paracentesis. The faster time from admission to paracentesis observed in the HMPS population compared with radiology services has important implications for patient satisfaction (symptom relief) and morbidity and mortality (time to peritonitis diagnosis). Ritter et al also demonstrated shorter length of stay (LOS) among patients who had paracenteses performed by the HMPS compared with the radiology service; this finding is consistent with previous studies that, while not evaluating a HMPS per se, demonstrated shorter LOS with bedside paracentesis. While there were some limitations, such as the findings representing a single-site experience and group differences that necessitated assessment of multiple confounders (some of which may remain unknown), the authors’ efforts to shift focus toward patient and high-value care outcomes should be applauded.

The evaluation of HMPSs has reached an inflection point. The field must now focus on assessing outcomes. Does the appropriateness of procedures improve when those with internal medicine training are performing the procedures rather than radiologists, who have more focused procedural knowledge but less general medical training? What procedures are not or should not be performed by HMPSs? What does the shift of procedures to HMPSs do to the flow of patients and procedures in interventional radiology, and do other patients indirectly benefit? How should hospital medicine groups and hospitals account for lower work relative value unit productivity of HMPSs compared with other traditional rounding services? In what ways do HMPSs provide cost-effective care compared with alternatives? There has been limited evaluation of cost-savings realized when performing paracentesis at the bedside as opposed to in the interventional radiology suite.⁵

Additionally, most HMPSs are staffed by a small number of hospitalists within a group. It is unclear how a HMPS will affect general hospitalist procedural competence, and whether that even matters. Should we still expect every hospitalist to be able to perform procedures, or are HMPSs a step in the evolution of subspecialties in hospital medicine? Such subspecialties exist already, including perioperative medicine and transitional care specialists.

Now that more HMPSs have been established, the next step in their evolution must go beyond feasibility and safety assessments and toward evaluation of their effectiveness. It has become clear that HMPSs can perform procedures safely, but what can they do better?

Hospital medicine procedure services have proliferated in recent years, driven by multiple synergistic factors, including an interest in improving hospital throughput, bolstering resident education, and ensuring full-spectrum practice for hospitalists. These services have become established and have demonstrated their capabilities, further catalyzed by emerging interest—and expertise—in point-of-care ultrasonography by hospitalists.

Most hospital medicine procedure services (HMPSs) focus on performing ultrasound-assisted procedures at bedside, providing purported advantages in convenience, cost, and potentially timing when compared to services performed by interventional radiology. The scope of procedures performed by HPMSs reflects the populations cared for by hospitalists, including paracentesis, thoracentesis, central venous catheter placement, lumbar puncture and, more recently, pigtail chest tube placement.^1,2 Fitting with the early development of HMPSs, initial reports regarding these services centered on optimal development of services and emphasized the question, “Are hospital medicine procedure services able to do [procedure x] as safely as radiology or the primary team?”²

Ensuring safety and quality is fundamental to implementing new workflows; however, it is now clear that HMPSs provide high-quality, safe, patient-centered bedside procedures; these services are no longer novel.³ As HMPSs mature, so too must their evaluation, research, and scholarship. It is no longer enough to document that a HMPS can perform procedures as well as interventional radiology or a standard hospital medicine care team—instead, we must identify how these services affect patient outcomes, improve education, add value, and influence the overall process of care in the hospital.

In this issue of the Journal of Hospital Medicine, Ritter and colleagues⁴ describe an important first step in this maturing field by evaluating how a HMPS affects process outcomes in the context of paracentesis. The faster time from admission to paracentesis observed in the HMPS population compared with radiology services has important implications for patient satisfaction (symptom relief) and morbidity and mortality (time to peritonitis diagnosis). Ritter et al also demonstrated shorter length of stay (LOS) among patients who had paracenteses performed by the HMPS compared with the radiology service; this finding is consistent with previous studies that, while not evaluating a HMPS per se, demonstrated shorter LOS with bedside paracentesis. While there were some limitations, such as the findings representing a single-site experience and group differences that necessitated assessment of multiple confounders (some of which may remain unknown), the authors’ efforts to shift focus toward patient and high-value care outcomes should be applauded.

The evaluation of HMPSs has reached an inflection point. The field must now focus on assessing outcomes. Does the appropriateness of procedures improve when those with internal medicine training are performing the procedures rather than radiologists, who have more focused procedural knowledge but less general medical training? What procedures are not or should not be performed by HMPSs? What does the shift of procedures to HMPSs do to the flow of patients and procedures in interventional radiology, and do other patients indirectly benefit? How should hospital medicine groups and hospitals account for lower work relative value unit productivity of HMPSs compared with other traditional rounding services? In what ways do HMPSs provide cost-effective care compared with alternatives? There has been limited evaluation of cost-savings realized when performing paracentesis at the bedside as opposed to in the interventional radiology suite.⁵

Additionally, most HMPSs are staffed by a small number of hospitalists within a group. It is unclear how a HMPS will affect general hospitalist procedural competence, and whether that even matters. Should we still expect every hospitalist to be able to perform procedures, or are HMPSs a step in the evolution of subspecialties in hospital medicine? Such subspecialties exist already, including perioperative medicine and transitional care specialists.

Now that more HMPSs have been established, the next step in their evolution must go beyond feasibility and safety assessments and toward evaluation of their effectiveness. It has become clear that HMPSs can perform procedures safely, but what can they do better?

Hospital medicine procedure services have proliferated in recent years, driven by multiple synergistic factors, including an interest in improving hospital throughput, bolstering resident education, and ensuring full-spectrum practice for hospitalists. These services have become established and have demonstrated their capabilities, further catalyzed by emerging interest—and expertise—in point-of-care ultrasonography by hospitalists.

Most hospital medicine procedure services (HMPSs) focus on performing ultrasound-assisted procedures at bedside, providing purported advantages in convenience, cost, and potentially timing when compared to services performed by interventional radiology. The scope of procedures performed by HPMSs reflects the populations cared for by hospitalists, including paracentesis, thoracentesis, central venous catheter placement, lumbar puncture and, more recently, pigtail chest tube placement.^1,2 Fitting with the early development of HMPSs, initial reports regarding these services centered on optimal development of services and emphasized the question, “Are hospital medicine procedure services able to do [procedure x] as safely as radiology or the primary team?”²

Ensuring safety and quality is fundamental to implementing new workflows; however, it is now clear that HMPSs provide high-quality, safe, patient-centered bedside procedures; these services are no longer novel.³ As HMPSs mature, so too must their evaluation, research, and scholarship. It is no longer enough to document that a HMPS can perform procedures as well as interventional radiology or a standard hospital medicine care team—instead, we must identify how these services affect patient outcomes, improve education, add value, and influence the overall process of care in the hospital.

In this issue of the Journal of Hospital Medicine, Ritter and colleagues⁴ describe an important first step in this maturing field by evaluating how a HMPS affects process outcomes in the context of paracentesis. The faster time from admission to paracentesis observed in the HMPS population compared with radiology services has important implications for patient satisfaction (symptom relief) and morbidity and mortality (time to peritonitis diagnosis). Ritter et al also demonstrated shorter length of stay (LOS) among patients who had paracenteses performed by the HMPS compared with the radiology service; this finding is consistent with previous studies that, while not evaluating a HMPS per se, demonstrated shorter LOS with bedside paracentesis. While there were some limitations, such as the findings representing a single-site experience and group differences that necessitated assessment of multiple confounders (some of which may remain unknown), the authors’ efforts to shift focus toward patient and high-value care outcomes should be applauded.

The evaluation of HMPSs has reached an inflection point. The field must now focus on assessing outcomes. Does the appropriateness of procedures improve when those with internal medicine training are performing the procedures rather than radiologists, who have more focused procedural knowledge but less general medical training? What procedures are not or should not be performed by HMPSs? What does the shift of procedures to HMPSs do to the flow of patients and procedures in interventional radiology, and do other patients indirectly benefit? How should hospital medicine groups and hospitals account for lower work relative value unit productivity of HMPSs compared with other traditional rounding services? In what ways do HMPSs provide cost-effective care compared with alternatives? There has been limited evaluation of cost-savings realized when performing paracentesis at the bedside as opposed to in the interventional radiology suite.⁵

Additionally, most HMPSs are staffed by a small number of hospitalists within a group. It is unclear how a HMPS will affect general hospitalist procedural competence, and whether that even matters. Should we still expect every hospitalist to be able to perform procedures, or are HMPSs a step in the evolution of subspecialties in hospital medicine? Such subspecialties exist already, including perioperative medicine and transitional care specialists.

Now that more HMPSs have been established, the next step in their evolution must go beyond feasibility and safety assessments and toward evaluation of their effectiveness. It has become clear that HMPSs can perform procedures safely, but what can they do better?

Throughout North America, hospitalizations and deaths due to SARS-CoV-2 have fallen substantially due to the rapid roll-out of COVID-19 vaccines. Despite this monumental success, however, transmission of the virus will unfortunately persist for the foreseeable future due to a variety of factors, including incomplete population vaccination, emergence of variants, and increased exposures as social and economic activity return to normal.¹ Therefore, it is of critical importance to continue to track the burden of COVID-19 by region. Specifically, the incidence of hospitalizations due to COVID-19 will be a key metric, as highlighted by Tsai et al² in this issue of the Journal of Hospital Medicine.

Tsai et al² explored the challenge of accurately determining the burden of hospitalization due to COVID-19, focusing on the potential for misclassification leading to overestimations. They rigorously evaluated the proportion of overall COVID-19–associated hospitalizations reported to Los Angeles County Department of Public Health that were potentially misclassified as caused by COVID-19 because of incidentally detected virus in patients who were hospitalized for unrelated reasons. In their study, they reviewed medical records from a randomly selected subset of hospital discharges with a clinical diagnosis of COVID-19 to determine whether a clinical diagnosis of COVID-19 was warranted. Among 618 patients, COVID-19 was deemed incidental to the reason for hospitalization in 12% (95% CI, 9%-16%) of admissions.

Incidental viral detection is more common during periods of high case prevalence and when case presentations overlap with nonclassic COVID symptoms.³ Incidental viral detection also occurs when broad testing of asymptomatic patients is instituted prior to admission, procedures, or high-risk medical therapies. Residual postinfectious shedding and false-positive results may further falsely increase case counts. The clinical and infection control implications of detectable virus is further complicated by vaccination, which leads to milder forms of the infection with less capacity for transmission.⁴

Why is establishing an overestimation COVID-19 hospitalization important? First, if misclassification leads to an overestimate of the number of hospitalizations caused by COVID-19, public health restrictions might be increased to protect overloading acute care sites when such measures are unnecessary, resulting in unintended social and economic fallouts.⁵ Second, healthcare resource allocation depends on accurate estimates of disease burden—overestimation of COVID-19–related hospitalization can lead to misallocation of scarce resources, including personnel, equipment, and medication to units or hospitals.⁶ Relatedly, cancelling of “nonurgent” tests, procedures, and clinic visits to reallocate resources to COVID-19–related care delays diagnosis and treatment of potentially serious illnesses. Last, overattributing hospitalizations due to COVID-19, particularly in patients who are now fully vaccinated, may lead researchers to underestimate the efficacy of vaccination efforts on the individual and population level, especially in the era of evolving variant strains.

How could this research change future practice? As the authors astutely state, the purpose of the investigation is not to alter practice on the individual patient level, but rather to help public health officials to make better decisions. One solution (similar to census adjustment) based on future research would be to potentially apply a corrective factor to “adjust” COVID-19 hospitalizations downward to explicitly account for the recognition that some proportion of patients hospitalized with COVID-19 were not actually hospitalized because of COVID-19.

Although vaccination continues to be highly successful at curbing the pandemic, transmission of COVID-19 persists due to gaps in vaccination and emergence of variants. Therefore, continued ongoing vigilance for disease burden, specifically focused on the most vulnerable aspects of the health care system—acute care centers—is critical to informing optimal public health restrictions and resource allocation.

Throughout North America, hospitalizations and deaths due to SARS-CoV-2 have fallen substantially due to the rapid roll-out of COVID-19 vaccines. Despite this monumental success, however, transmission of the virus will unfortunately persist for the foreseeable future due to a variety of factors, including incomplete population vaccination, emergence of variants, and increased exposures as social and economic activity return to normal.¹ Therefore, it is of critical importance to continue to track the burden of COVID-19 by region. Specifically, the incidence of hospitalizations due to COVID-19 will be a key metric, as highlighted by Tsai et al² in this issue of the Journal of Hospital Medicine.

Tsai et al² explored the challenge of accurately determining the burden of hospitalization due to COVID-19, focusing on the potential for misclassification leading to overestimations. They rigorously evaluated the proportion of overall COVID-19–associated hospitalizations reported to Los Angeles County Department of Public Health that were potentially misclassified as caused by COVID-19 because of incidentally detected virus in patients who were hospitalized for unrelated reasons. In their study, they reviewed medical records from a randomly selected subset of hospital discharges with a clinical diagnosis of COVID-19 to determine whether a clinical diagnosis of COVID-19 was warranted. Among 618 patients, COVID-19 was deemed incidental to the reason for hospitalization in 12% (95% CI, 9%-16%) of admissions.

Incidental viral detection is more common during periods of high case prevalence and when case presentations overlap with nonclassic COVID symptoms.³ Incidental viral detection also occurs when broad testing of asymptomatic patients is instituted prior to admission, procedures, or high-risk medical therapies. Residual postinfectious shedding and false-positive results may further falsely increase case counts. The clinical and infection control implications of detectable virus is further complicated by vaccination, which leads to milder forms of the infection with less capacity for transmission.⁴

Why is establishing an overestimation COVID-19 hospitalization important? First, if misclassification leads to an overestimate of the number of hospitalizations caused by COVID-19, public health restrictions might be increased to protect overloading acute care sites when such measures are unnecessary, resulting in unintended social and economic fallouts.⁵ Second, healthcare resource allocation depends on accurate estimates of disease burden—overestimation of COVID-19–related hospitalization can lead to misallocation of scarce resources, including personnel, equipment, and medication to units or hospitals.⁶ Relatedly, cancelling of “nonurgent” tests, procedures, and clinic visits to reallocate resources to COVID-19–related care delays diagnosis and treatment of potentially serious illnesses. Last, overattributing hospitalizations due to COVID-19, particularly in patients who are now fully vaccinated, may lead researchers to underestimate the efficacy of vaccination efforts on the individual and population level, especially in the era of evolving variant strains.

How could this research change future practice? As the authors astutely state, the purpose of the investigation is not to alter practice on the individual patient level, but rather to help public health officials to make better decisions. One solution (similar to census adjustment) based on future research would be to potentially apply a corrective factor to “adjust” COVID-19 hospitalizations downward to explicitly account for the recognition that some proportion of patients hospitalized with COVID-19 were not actually hospitalized because of COVID-19.

Although vaccination continues to be highly successful at curbing the pandemic, transmission of COVID-19 persists due to gaps in vaccination and emergence of variants. Therefore, continued ongoing vigilance for disease burden, specifically focused on the most vulnerable aspects of the health care system—acute care centers—is critical to informing optimal public health restrictions and resource allocation.

Throughout North America, hospitalizations and deaths due to SARS-CoV-2 have fallen substantially due to the rapid roll-out of COVID-19 vaccines. Despite this monumental success, however, transmission of the virus will unfortunately persist for the foreseeable future due to a variety of factors, including incomplete population vaccination, emergence of variants, and increased exposures as social and economic activity return to normal.¹ Therefore, it is of critical importance to continue to track the burden of COVID-19 by region. Specifically, the incidence of hospitalizations due to COVID-19 will be a key metric, as highlighted by Tsai et al² in this issue of the Journal of Hospital Medicine.

Tsai et al² explored the challenge of accurately determining the burden of hospitalization due to COVID-19, focusing on the potential for misclassification leading to overestimations. They rigorously evaluated the proportion of overall COVID-19–associated hospitalizations reported to Los Angeles County Department of Public Health that were potentially misclassified as caused by COVID-19 because of incidentally detected virus in patients who were hospitalized for unrelated reasons. In their study, they reviewed medical records from a randomly selected subset of hospital discharges with a clinical diagnosis of COVID-19 to determine whether a clinical diagnosis of COVID-19 was warranted. Among 618 patients, COVID-19 was deemed incidental to the reason for hospitalization in 12% (95% CI, 9%-16%) of admissions.

Incidental viral detection is more common during periods of high case prevalence and when case presentations overlap with nonclassic COVID symptoms.³ Incidental viral detection also occurs when broad testing of asymptomatic patients is instituted prior to admission, procedures, or high-risk medical therapies. Residual postinfectious shedding and false-positive results may further falsely increase case counts. The clinical and infection control implications of detectable virus is further complicated by vaccination, which leads to milder forms of the infection with less capacity for transmission.⁴

Why is establishing an overestimation COVID-19 hospitalization important? First, if misclassification leads to an overestimate of the number of hospitalizations caused by COVID-19, public health restrictions might be increased to protect overloading acute care sites when such measures are unnecessary, resulting in unintended social and economic fallouts.⁵ Second, healthcare resource allocation depends on accurate estimates of disease burden—overestimation of COVID-19–related hospitalization can lead to misallocation of scarce resources, including personnel, equipment, and medication to units or hospitals.⁶ Relatedly, cancelling of “nonurgent” tests, procedures, and clinic visits to reallocate resources to COVID-19–related care delays diagnosis and treatment of potentially serious illnesses. Last, overattributing hospitalizations due to COVID-19, particularly in patients who are now fully vaccinated, may lead researchers to underestimate the efficacy of vaccination efforts on the individual and population level, especially in the era of evolving variant strains.

How could this research change future practice? As the authors astutely state, the purpose of the investigation is not to alter practice on the individual patient level, but rather to help public health officials to make better decisions. One solution (similar to census adjustment) based on future research would be to potentially apply a corrective factor to “adjust” COVID-19 hospitalizations downward to explicitly account for the recognition that some proportion of patients hospitalized with COVID-19 were not actually hospitalized because of COVID-19.

Although vaccination continues to be highly successful at curbing the pandemic, transmission of COVID-19 persists due to gaps in vaccination and emergence of variants. Therefore, continued ongoing vigilance for disease burden, specifically focused on the most vulnerable aspects of the health care system—acute care centers—is critical to informing optimal public health restrictions and resource allocation.

Centers for Medicare and Medicaid Services (CMS)–promulgated quality metrics continue to attract critics. Physicians decry that many metrics are outside their control, while patient groups are frustrated that metrics lack meaning for beneficiaries. The Hospital Readmissions Reduction Program (HRRP) reduces payments for “excess” 30-day risk-standardized readmissions for six conditions and procedures, and may be less effective in reducing readmissions than previously reported due to intentional and increasing use of hospital observation stays.¹

In this issue, Sheehy et al² report that nearly one in five rehospitalizations were unrecognized because either the index hospitalization or the rehospitalization was an observation stay, highlighting yet another challenge with the HRRP. Limitations of their study include the use of a single year of claims data and the exclusion of Medicare Advantage claims data, as one might expect lower readmission rates in this capitated program. Opportunities for improving the HRRP could consist of updating the HRRP metric to include observation stays and, for surgical hospitalizations, extended-stay surgical recovery, wherein patients may be observed for up to 2 days following a procedure. Unfortunately, despite the HRRP missing nearly one in five readmissions, CMS would likely need additional statutory authority from Congress in order to reinterpret the definition of readmission³ to include observation stays.

Challenges with the HRRP metrics raise broader concerns about the program. For decades, administrators viewed readmissions as a utilization metric, only to have the Affordable Care Act re-designate and define all-cause readmissions as a quality metric. Yet hospitals and health systems control only some factors driving readmission. Readmissions occur for a variety of reasons, including not only poor quality of initial hospital care and inadequate care coordination, but also factors that are beyond the hospital’s purview, such as lack of access to ambulatory services, multiple and severe chronic conditions that progress or remain unresponsive to intervention,⁴ and demographic and social factors such as housing instability, health literacy, or residence in a food desert. These non-hospital factors reside within the domain of other market participants or local, state, and federal government agencies.

Challenges to the utility, validity, and appropriateness of HRRP metrics should remind policymakers of the dangers of over-legislating the details of healthcare policy and the statutory inflexibility that can ensue. Clinical care evolves, and artificial constructs—including payment categories such as observation status—may age poorly over time, exemplified best by the challenges of accessing post-acute care due to the 3-day rule.⁵ Introduced as a statutory requirement in 1967, when the average length of stay was 13.8 days and observation care did not exist as a payment category, the 3-day rule requires Medicare beneficiaries to spend 3 days admitted to the hospital in order to qualify for coverage of post-acute care, creating care gaps for observation stay patients.

Observation care itself is an artificial construct of CMS payment policy. In the Medicare program, observation care falls under Part B, exposing patients to both greater financial responsibility and billing complexity through the engagement of their supplemental insurance, even though those receiving observation care experience the same care as if hospitalized— routine monitoring, nursing care, blood draws, imaging, and diagnostic tests. While CMS requires notification of observation status and explanation of the difference in patient financial responsibility, in clinical practice, patient understanding is limited. Policymakers can support both Medicare beneficiaries and hospitals by reexamining observation care as a payment category.

Sheehy and colleagues’ work simultaneously challenges the face validity of the HRRP and the reasonableness of categorizing some inpatient stays as outpatient care in the hospital—issues that policymakers can and should address.

Centers for Medicare and Medicaid Services (CMS)–promulgated quality metrics continue to attract critics. Physicians decry that many metrics are outside their control, while patient groups are frustrated that metrics lack meaning for beneficiaries. The Hospital Readmissions Reduction Program (HRRP) reduces payments for “excess” 30-day risk-standardized readmissions for six conditions and procedures, and may be less effective in reducing readmissions than previously reported due to intentional and increasing use of hospital observation stays.¹

In this issue, Sheehy et al² report that nearly one in five rehospitalizations were unrecognized because either the index hospitalization or the rehospitalization was an observation stay, highlighting yet another challenge with the HRRP. Limitations of their study include the use of a single year of claims data and the exclusion of Medicare Advantage claims data, as one might expect lower readmission rates in this capitated program. Opportunities for improving the HRRP could consist of updating the HRRP metric to include observation stays and, for surgical hospitalizations, extended-stay surgical recovery, wherein patients may be observed for up to 2 days following a procedure. Unfortunately, despite the HRRP missing nearly one in five readmissions, CMS would likely need additional statutory authority from Congress in order to reinterpret the definition of readmission³ to include observation stays.

Challenges with the HRRP metrics raise broader concerns about the program. For decades, administrators viewed readmissions as a utilization metric, only to have the Affordable Care Act re-designate and define all-cause readmissions as a quality metric. Yet hospitals and health systems control only some factors driving readmission. Readmissions occur for a variety of reasons, including not only poor quality of initial hospital care and inadequate care coordination, but also factors that are beyond the hospital’s purview, such as lack of access to ambulatory services, multiple and severe chronic conditions that progress or remain unresponsive to intervention,⁴ and demographic and social factors such as housing instability, health literacy, or residence in a food desert. These non-hospital factors reside within the domain of other market participants or local, state, and federal government agencies.

Challenges to the utility, validity, and appropriateness of HRRP metrics should remind policymakers of the dangers of over-legislating the details of healthcare policy and the statutory inflexibility that can ensue. Clinical care evolves, and artificial constructs—including payment categories such as observation status—may age poorly over time, exemplified best by the challenges of accessing post-acute care due to the 3-day rule.⁵ Introduced as a statutory requirement in 1967, when the average length of stay was 13.8 days and observation care did not exist as a payment category, the 3-day rule requires Medicare beneficiaries to spend 3 days admitted to the hospital in order to qualify for coverage of post-acute care, creating care gaps for observation stay patients.

Observation care itself is an artificial construct of CMS payment policy. In the Medicare program, observation care falls under Part B, exposing patients to both greater financial responsibility and billing complexity through the engagement of their supplemental insurance, even though those receiving observation care experience the same care as if hospitalized— routine monitoring, nursing care, blood draws, imaging, and diagnostic tests. While CMS requires notification of observation status and explanation of the difference in patient financial responsibility, in clinical practice, patient understanding is limited. Policymakers can support both Medicare beneficiaries and hospitals by reexamining observation care as a payment category.

Sheehy and colleagues’ work simultaneously challenges the face validity of the HRRP and the reasonableness of categorizing some inpatient stays as outpatient care in the hospital—issues that policymakers can and should address.

Centers for Medicare and Medicaid Services (CMS)–promulgated quality metrics continue to attract critics. Physicians decry that many metrics are outside their control, while patient groups are frustrated that metrics lack meaning for beneficiaries. The Hospital Readmissions Reduction Program (HRRP) reduces payments for “excess” 30-day risk-standardized readmissions for six conditions and procedures, and may be less effective in reducing readmissions than previously reported due to intentional and increasing use of hospital observation stays.¹

In this issue, Sheehy et al² report that nearly one in five rehospitalizations were unrecognized because either the index hospitalization or the rehospitalization was an observation stay, highlighting yet another challenge with the HRRP. Limitations of their study include the use of a single year of claims data and the exclusion of Medicare Advantage claims data, as one might expect lower readmission rates in this capitated program. Opportunities for improving the HRRP could consist of updating the HRRP metric to include observation stays and, for surgical hospitalizations, extended-stay surgical recovery, wherein patients may be observed for up to 2 days following a procedure. Unfortunately, despite the HRRP missing nearly one in five readmissions, CMS would likely need additional statutory authority from Congress in order to reinterpret the definition of readmission³ to include observation stays.

Challenges with the HRRP metrics raise broader concerns about the program. For decades, administrators viewed readmissions as a utilization metric, only to have the Affordable Care Act re-designate and define all-cause readmissions as a quality metric. Yet hospitals and health systems control only some factors driving readmission. Readmissions occur for a variety of reasons, including not only poor quality of initial hospital care and inadequate care coordination, but also factors that are beyond the hospital’s purview, such as lack of access to ambulatory services, multiple and severe chronic conditions that progress or remain unresponsive to intervention,⁴ and demographic and social factors such as housing instability, health literacy, or residence in a food desert. These non-hospital factors reside within the domain of other market participants or local, state, and federal government agencies.

Challenges to the utility, validity, and appropriateness of HRRP metrics should remind policymakers of the dangers of over-legislating the details of healthcare policy and the statutory inflexibility that can ensue. Clinical care evolves, and artificial constructs—including payment categories such as observation status—may age poorly over time, exemplified best by the challenges of accessing post-acute care due to the 3-day rule.⁵ Introduced as a statutory requirement in 1967, when the average length of stay was 13.8 days and observation care did not exist as a payment category, the 3-day rule requires Medicare beneficiaries to spend 3 days admitted to the hospital in order to qualify for coverage of post-acute care, creating care gaps for observation stay patients.

Observation care itself is an artificial construct of CMS payment policy. In the Medicare program, observation care falls under Part B, exposing patients to both greater financial responsibility and billing complexity through the engagement of their supplemental insurance, even though those receiving observation care experience the same care as if hospitalized— routine monitoring, nursing care, blood draws, imaging, and diagnostic tests. While CMS requires notification of observation status and explanation of the difference in patient financial responsibility, in clinical practice, patient understanding is limited. Policymakers can support both Medicare beneficiaries and hospitals by reexamining observation care as a payment category.

Sheehy and colleagues’ work simultaneously challenges the face validity of the HRRP and the reasonableness of categorizing some inpatient stays as outpatient care in the hospital—issues that policymakers can and should address.