Editorial

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Hospitalists are known as change agents for their fierce patient advocacy and expertise in hospital systems redesign. The field of hospital medicine has claimed numerous successes and the hospitalist model has been embraced by institutions across the country. Yet the lived experiences of hospitalists surveyed by Bhandari et al in this month’s issue of JHM suggest a grim undertone.¹ Hospital medicine is a field with high physician burnout rates, stark gender inequities in pay, leadership, and academic opportunities, and an unacceptably high prevalence of sexual harassment and gender discrimination. Women hospitalists disproportionately bear the brunt of these inequities. All hospitalists, however, can and should be an integral part of the path forward by recognizing the impact of these inequities on colleagues and hospital systems.

The study by Bhandari et al adds to the increasing body of knowledge documenting high levels of sexual harassment and gender discrimination in medicine and highlights important gender differences in these experiences among hospitalists nationally.^1,2 Among 336 respondents across 18 academic institutions, sexual harassment and gender discrimination were both common and highly problematic within the field of hospital medicine, confirming what prior narratives have only anecdotally shared. Both men and women experienced harassment, from patients and colleagues alike, but women endured higher levels compared with men on all the measures studied.¹

Qualitative comments in this study are noteworthy, including one about a hospitalist’s institution allowing potential faculty to be interviewed about plans for pregnancy, childcare, and personal household division of labor. One might argue that this knowledge is necessary for shift-based inpatient work in the context of a worldwide pandemic in which pregnant workers are likely at higher risk of increased morbidity and mortality. It remains illegal, however, to ask such questions, which are representative of the types of characteristics that constitute a toxic workplace environment. Moreover, such practices are particularly problematic given that pregnancy and childbearing for women in medicine come with their own set of well-documented unique challenges.³

The considerable body of research in this field should help guide new research priorities and targets for intervention. Does the experience of sexual harassment impact hospitalists’ intentions to leave their institutions or the career as a whole? Does sexual harassment originating from colleagues or from patients and families affect patient safety or quality of care? Do interventions in other international hospital settings specifically targeting respectfulness translate to American hospitals?⁴ These questions and a host of others merit our attention.

Hospital system leaders should work with hospital medicine leaders to support wholesale institutional cultural transformation. Implementation of antiharassment measures recommended in the 2018 report on sexual harassment from the National Academies of Sciences, Engineering, and Medicine is critical.² This means supporting diverse, inclusive, and respectful environments at all levels within the organization, improving transparency and accountability for how incidents are handled, striving for strong and diverse leadership, providing meaningful support for targets of harassment, measuring prevalence over time, and encouraging professional societies to adopt similar actions. Furthermore, we believe it is critical to adopt a zero-tolerance policy for harassing behaviors and to hold individuals accountable. Encouraging all individuals within health care systems to uphold their ethical obligations to combat harassment and bias on a personal level is important.⁵ If left unaddressed, the unmet needs of those who are subjected to harassment and bias will continue to be problematic for generations to come, with detrimental effects throughout healthcare systems and the broader populations they serve.

Hospitalists are known as change agents for their fierce patient advocacy and expertise in hospital systems redesign. The field of hospital medicine has claimed numerous successes and the hospitalist model has been embraced by institutions across the country. Yet the lived experiences of hospitalists surveyed by Bhandari et al in this month’s issue of JHM suggest a grim undertone.¹ Hospital medicine is a field with high physician burnout rates, stark gender inequities in pay, leadership, and academic opportunities, and an unacceptably high prevalence of sexual harassment and gender discrimination. Women hospitalists disproportionately bear the brunt of these inequities. All hospitalists, however, can and should be an integral part of the path forward by recognizing the impact of these inequities on colleagues and hospital systems.

The study by Bhandari et al adds to the increasing body of knowledge documenting high levels of sexual harassment and gender discrimination in medicine and highlights important gender differences in these experiences among hospitalists nationally.^1,2 Among 336 respondents across 18 academic institutions, sexual harassment and gender discrimination were both common and highly problematic within the field of hospital medicine, confirming what prior narratives have only anecdotally shared. Both men and women experienced harassment, from patients and colleagues alike, but women endured higher levels compared with men on all the measures studied.¹

Qualitative comments in this study are noteworthy, including one about a hospitalist’s institution allowing potential faculty to be interviewed about plans for pregnancy, childcare, and personal household division of labor. One might argue that this knowledge is necessary for shift-based inpatient work in the context of a worldwide pandemic in which pregnant workers are likely at higher risk of increased morbidity and mortality. It remains illegal, however, to ask such questions, which are representative of the types of characteristics that constitute a toxic workplace environment. Moreover, such practices are particularly problematic given that pregnancy and childbearing for women in medicine come with their own set of well-documented unique challenges.³

The considerable body of research in this field should help guide new research priorities and targets for intervention. Does the experience of sexual harassment impact hospitalists’ intentions to leave their institutions or the career as a whole? Does sexual harassment originating from colleagues or from patients and families affect patient safety or quality of care? Do interventions in other international hospital settings specifically targeting respectfulness translate to American hospitals?⁴ These questions and a host of others merit our attention.

Hospital system leaders should work with hospital medicine leaders to support wholesale institutional cultural transformation. Implementation of antiharassment measures recommended in the 2018 report on sexual harassment from the National Academies of Sciences, Engineering, and Medicine is critical.² This means supporting diverse, inclusive, and respectful environments at all levels within the organization, improving transparency and accountability for how incidents are handled, striving for strong and diverse leadership, providing meaningful support for targets of harassment, measuring prevalence over time, and encouraging professional societies to adopt similar actions. Furthermore, we believe it is critical to adopt a zero-tolerance policy for harassing behaviors and to hold individuals accountable. Encouraging all individuals within health care systems to uphold their ethical obligations to combat harassment and bias on a personal level is important.⁵ If left unaddressed, the unmet needs of those who are subjected to harassment and bias will continue to be problematic for generations to come, with detrimental effects throughout healthcare systems and the broader populations they serve.

Hospitalists are known as change agents for their fierce patient advocacy and expertise in hospital systems redesign. The field of hospital medicine has claimed numerous successes and the hospitalist model has been embraced by institutions across the country. Yet the lived experiences of hospitalists surveyed by Bhandari et al in this month’s issue of JHM suggest a grim undertone.¹ Hospital medicine is a field with high physician burnout rates, stark gender inequities in pay, leadership, and academic opportunities, and an unacceptably high prevalence of sexual harassment and gender discrimination. Women hospitalists disproportionately bear the brunt of these inequities. All hospitalists, however, can and should be an integral part of the path forward by recognizing the impact of these inequities on colleagues and hospital systems.

The study by Bhandari et al adds to the increasing body of knowledge documenting high levels of sexual harassment and gender discrimination in medicine and highlights important gender differences in these experiences among hospitalists nationally.^1,2 Among 336 respondents across 18 academic institutions, sexual harassment and gender discrimination were both common and highly problematic within the field of hospital medicine, confirming what prior narratives have only anecdotally shared. Both men and women experienced harassment, from patients and colleagues alike, but women endured higher levels compared with men on all the measures studied.¹

Qualitative comments in this study are noteworthy, including one about a hospitalist’s institution allowing potential faculty to be interviewed about plans for pregnancy, childcare, and personal household division of labor. One might argue that this knowledge is necessary for shift-based inpatient work in the context of a worldwide pandemic in which pregnant workers are likely at higher risk of increased morbidity and mortality. It remains illegal, however, to ask such questions, which are representative of the types of characteristics that constitute a toxic workplace environment. Moreover, such practices are particularly problematic given that pregnancy and childbearing for women in medicine come with their own set of well-documented unique challenges.³

The considerable body of research in this field should help guide new research priorities and targets for intervention. Does the experience of sexual harassment impact hospitalists’ intentions to leave their institutions or the career as a whole? Does sexual harassment originating from colleagues or from patients and families affect patient safety or quality of care? Do interventions in other international hospital settings specifically targeting respectfulness translate to American hospitals?⁴ These questions and a host of others merit our attention.

Hospital system leaders should work with hospital medicine leaders to support wholesale institutional cultural transformation. Implementation of antiharassment measures recommended in the 2018 report on sexual harassment from the National Academies of Sciences, Engineering, and Medicine is critical.² This means supporting diverse, inclusive, and respectful environments at all levels within the organization, improving transparency and accountability for how incidents are handled, striving for strong and diverse leadership, providing meaningful support for targets of harassment, measuring prevalence over time, and encouraging professional societies to adopt similar actions. Furthermore, we believe it is critical to adopt a zero-tolerance policy for harassing behaviors and to hold individuals accountable. Encouraging all individuals within health care systems to uphold their ethical obligations to combat harassment and bias on a personal level is important.⁵ If left unaddressed, the unmet needs of those who are subjected to harassment and bias will continue to be problematic for generations to come, with detrimental effects throughout healthcare systems and the broader populations they serve.

Historically, bacterial infections in hospitalized children were treated with intravenous (IV) antibiotics for the duration of therapy—frequently with placement of a vascular catheter. Risks associated with vascular catheters and the limitations they impose on a child’s quality of life are increasingly being recognized—including thrombi, catheter dislodgement, and secondary infections as catheters provide a portal of entry for bacteria into the bloodstream (ie, catheter-associated bloodstream infections) or along the catheter wall (ie, phlebitis). This potential for harm underscores the importance of transitioning to oral antibiotic therapy whenever possible.

In this issue of the Journal of Hospital Medicine, Cotter et al used an administrative database to investigate opportunities to transition from IV to oral antibiotics for patients across multiple pediatric hospitals.¹ Their novel metric, “percent opportunity,” represents the percent of days that there was the opportunity to transition from IV to oral antibiotics. They found that over 50% of the time, IV antibiotics could have been switched to equivalent oral agents. Furthermore, there was wide variability across institutions in IV-to-oral transitioning practices; 45% of the variation was seemingly attributable to institution-level preferences.

The large sample size and multicenter nature of this study improve its external validity. However, using administrative data to make assumptions about clinical decision-making has limitations. The definition of opportunity days assumes that any day a child receives other enteral medications provides an “opportunity” to prescribe oral antibiotics instead. This does not account for other reasonable indications to continue IV therapy (eg, endocarditis) and may overestimate true opportunities for conversion to oral therapy. Alternatively, their conservative approach of excluding days when a child received both IV and oral antibiotics may underestimate opportunities for oral transition. Regardless of the precision of their estimates, their findings highlight that there is room to improve the culture of transitioning hospitalized children from IV to oral antibiotic therapy.

Admittedly, the evidence for clinically effective conversion to oral therapy in children remains incomplete. Data support oral antibiotics for hospitalized children with pneumonia, cellulitis, pyelonephritis, and osteoarticular infections—even with associated bacteremia.² There is also evidence for successful conversion to oral therapy for complicated appendicitis, retropharyngeal abscesses, mastoiditis, and orbital cellulitis.²

The decision to transition to oral therapy does not need to be delayed until the time of hospital discharge because each additional day of IV therapy poses a cumulative risk. Rather, prescribers should apply a structured approach, such as the “Four Moments of Antibiotic Decision Making,” on a daily basis for every hospitalized child receiving antibiotics to prompt timely decisions about discontinuing IV therapy, narrowing IV therapy, or transitioning from IV to oral antibiotic therapy.³ We applaud Cotter et al for shedding light on an area in need of standardization of care, which could optimize patient outcomes and minimize harm for a large number of children.¹ The “percent opportunity” to switch from IV to oral antibiotic therapy is a promising antibiotic stewardship metric, and its association with clinical outcomes merits further investigation.

Historically, bacterial infections in hospitalized children were treated with intravenous (IV) antibiotics for the duration of therapy—frequently with placement of a vascular catheter. Risks associated with vascular catheters and the limitations they impose on a child’s quality of life are increasingly being recognized—including thrombi, catheter dislodgement, and secondary infections as catheters provide a portal of entry for bacteria into the bloodstream (ie, catheter-associated bloodstream infections) or along the catheter wall (ie, phlebitis). This potential for harm underscores the importance of transitioning to oral antibiotic therapy whenever possible.

In this issue of the Journal of Hospital Medicine, Cotter et al used an administrative database to investigate opportunities to transition from IV to oral antibiotics for patients across multiple pediatric hospitals.¹ Their novel metric, “percent opportunity,” represents the percent of days that there was the opportunity to transition from IV to oral antibiotics. They found that over 50% of the time, IV antibiotics could have been switched to equivalent oral agents. Furthermore, there was wide variability across institutions in IV-to-oral transitioning practices; 45% of the variation was seemingly attributable to institution-level preferences.

The large sample size and multicenter nature of this study improve its external validity. However, using administrative data to make assumptions about clinical decision-making has limitations. The definition of opportunity days assumes that any day a child receives other enteral medications provides an “opportunity” to prescribe oral antibiotics instead. This does not account for other reasonable indications to continue IV therapy (eg, endocarditis) and may overestimate true opportunities for conversion to oral therapy. Alternatively, their conservative approach of excluding days when a child received both IV and oral antibiotics may underestimate opportunities for oral transition. Regardless of the precision of their estimates, their findings highlight that there is room to improve the culture of transitioning hospitalized children from IV to oral antibiotic therapy.

Admittedly, the evidence for clinically effective conversion to oral therapy in children remains incomplete. Data support oral antibiotics for hospitalized children with pneumonia, cellulitis, pyelonephritis, and osteoarticular infections—even with associated bacteremia.² There is also evidence for successful conversion to oral therapy for complicated appendicitis, retropharyngeal abscesses, mastoiditis, and orbital cellulitis.²

The decision to transition to oral therapy does not need to be delayed until the time of hospital discharge because each additional day of IV therapy poses a cumulative risk. Rather, prescribers should apply a structured approach, such as the “Four Moments of Antibiotic Decision Making,” on a daily basis for every hospitalized child receiving antibiotics to prompt timely decisions about discontinuing IV therapy, narrowing IV therapy, or transitioning from IV to oral antibiotic therapy.³ We applaud Cotter et al for shedding light on an area in need of standardization of care, which could optimize patient outcomes and minimize harm for a large number of children.¹ The “percent opportunity” to switch from IV to oral antibiotic therapy is a promising antibiotic stewardship metric, and its association with clinical outcomes merits further investigation.

Historically, bacterial infections in hospitalized children were treated with intravenous (IV) antibiotics for the duration of therapy—frequently with placement of a vascular catheter. Risks associated with vascular catheters and the limitations they impose on a child’s quality of life are increasingly being recognized—including thrombi, catheter dislodgement, and secondary infections as catheters provide a portal of entry for bacteria into the bloodstream (ie, catheter-associated bloodstream infections) or along the catheter wall (ie, phlebitis). This potential for harm underscores the importance of transitioning to oral antibiotic therapy whenever possible.

In this issue of the Journal of Hospital Medicine, Cotter et al used an administrative database to investigate opportunities to transition from IV to oral antibiotics for patients across multiple pediatric hospitals.¹ Their novel metric, “percent opportunity,” represents the percent of days that there was the opportunity to transition from IV to oral antibiotics. They found that over 50% of the time, IV antibiotics could have been switched to equivalent oral agents. Furthermore, there was wide variability across institutions in IV-to-oral transitioning practices; 45% of the variation was seemingly attributable to institution-level preferences.

The large sample size and multicenter nature of this study improve its external validity. However, using administrative data to make assumptions about clinical decision-making has limitations. The definition of opportunity days assumes that any day a child receives other enteral medications provides an “opportunity” to prescribe oral antibiotics instead. This does not account for other reasonable indications to continue IV therapy (eg, endocarditis) and may overestimate true opportunities for conversion to oral therapy. Alternatively, their conservative approach of excluding days when a child received both IV and oral antibiotics may underestimate opportunities for oral transition. Regardless of the precision of their estimates, their findings highlight that there is room to improve the culture of transitioning hospitalized children from IV to oral antibiotic therapy.

Admittedly, the evidence for clinically effective conversion to oral therapy in children remains incomplete. Data support oral antibiotics for hospitalized children with pneumonia, cellulitis, pyelonephritis, and osteoarticular infections—even with associated bacteremia.² There is also evidence for successful conversion to oral therapy for complicated appendicitis, retropharyngeal abscesses, mastoiditis, and orbital cellulitis.²

The decision to transition to oral therapy does not need to be delayed until the time of hospital discharge because each additional day of IV therapy poses a cumulative risk. Rather, prescribers should apply a structured approach, such as the “Four Moments of Antibiotic Decision Making,” on a daily basis for every hospitalized child receiving antibiotics to prompt timely decisions about discontinuing IV therapy, narrowing IV therapy, or transitioning from IV to oral antibiotic therapy.³ We applaud Cotter et al for shedding light on an area in need of standardization of care, which could optimize patient outcomes and minimize harm for a large number of children.¹ The “percent opportunity” to switch from IV to oral antibiotic therapy is a promising antibiotic stewardship metric, and its association with clinical outcomes merits further investigation.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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