Shradha A Kulkarni, MD

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.