Mph

People with serious mental illness (SMI) are at substantial risk for premature mortality, dying on average 10 to 20 years earlier than others.¹ The reasons for this disparity are complex; however, the high prevalence of chronic disease and physical comorbidities in the SMI population have been identified as prominent factors.² Engagement and reengagement in care, including primary care for medical comorbidities, can mitigate these mortality risks.^2-4 Among veterans with SMI lost to follow-up care for more than 12 months, those not successfully reengaged in care were more likely to die compared with those reengaged in care.^2,3

Given this evidence, health care systems, including the US Department of Veterans Affairs (VA), have looked to better engage these patients in care. These efforts have included mental health population health management, colocation of mental health with primary care, designation of primary care teams specializing in SMI, and integration of mental health and primary care services for patients experiencing homelessness.^5-8

As part of a national approach to encourage locally driven quality improvement (QI), the VA compiles performance metrics for each facility, across a gamut of care settings, conditions, and veteran populations.⁹ Quarterly facility report cards, with longitudinal data and cross-facility comparisons, enable facilities to identify targets for QI and track improvement progress. One metric reports on the proportion of enrolled veterans with SMI who have primary care engagement, defined as having an assigned primary care practitioner (PCP) and a primary care visit in the prior 12 months.

In support of a QI initiative at the VA Greater Los Angeles Healthcare System (VAGLAHS), we sought to describe promising practices being utilized by VA facilities with higher levels of primary care engagement among their veterans with SMI populations.

Methods

We conducted semistructured telephone interviews with a purposeful sample of key informants at VA facilities with high levels of engagement in primary care among veterans with SMI. All project components were conducted by an interdisciplinary team, which included a medical anthropologist (JM), a mental health physician (PR), an internal medicine physician (KC), and other health services researchers (JB, AG). Because the primary objective of the project was QI, this project was designated as nonresearch by the VAGLAHS Institutional Review Board.

The VA Facility Complexity Model classifies facilities into 5 tiers: 1a (most complex), 1b, 1c, 2, and 3 (least complex), based on patient care volume, patient risk, complexity of clinical programs, and size of research and teaching programs. We sampled informants at VA facilities with complexity ratings of 1a or 1b with better than median scores for primary care engagement of veterans with SMI based on report cards from January 2019 to March 2019. To increase the likelihood of identifying lessons that can generalize to the VAGLAHS with its large population of veterans experiencing homelessness, we selected facilities serving populations consisting of more than 1000 veterans experiencing homelessness.

At each selected facility, we first aimed to interview mental health leaders responsible for quality measurement and improvement identified from a national VA database. We then used snowball sampling to identify other informants at these VA facilities who were knowledgeable about relevant processes. Potential interviewees were contacted via email.

Interviews

The interview guide was developed by the interdisciplinary team and based on published literature about strategies for engaging patients with SMI in care. Interview guide questions focused on local practice arrangements, panel management, population health practices, and quality measurement and improvement efforts for engaging veterans with SMI in primary care (Appendix). Interviews were conducted by telephone, from May 2019 through July 2019, by experienced qualitative interviewers (JM, JB). Interviewees were assured confidentiality of their responses.

Interview audio recordings were used to generate detailed notes (AG). Structured summaries were prepared from these notes, using a template based on the interview guide. We organized these summaries into matrices for analysis, grouping summarized points by interview domains to facilitate comparison across interviews.^10-11 Our team reviewed and discussed the matrices, and iteratively identified and defined themes to identify the common engagement approaches and the nature of the connections between mental health and primary care. To ensure rigor, findings were checked by the senior qualitative lead (JM).

Results

The median SMI engagement score—defined as the proportion of veterans with SMI who have had a primary care visit in the prior 12 months and who have an assigned PCP—was 75.6% across 1a and 1b VA facilities. We identified 16 VA facilities that had a median or higher score and more than 1000 enrolled veterans experiencing homelessness. From these16 facilities, we emailed 31 potential interviewees, 14 of whom were identified from a VA database and 17 referred by other interviewees. In total, we interviewed 18 key informants across 11 (69%) facilities, including chiefs of psychology and mental health services, PCPs with mental health expertise, QI specialists, a psychosocial rehabilitation leader, and a local recovery coordinator, who helps veterans with SMI access recovery-oriented services. Characteristics of the facilities and interviewees are shown in Table 1. Interviews lasted a mean 35 (range, 26-50) minutes.

Engagement Approaches

The strategies used to engage veterans with SMI were heterogenous, with no single strategy common across all facilities. However, we identified 2 categories of engagement approaches: targeted outreach and routine practices.

Targeted outreach strategies included deliberate, systematic approaches to reach veterans with SMI outside of regularly scheduled visits. These strategies were designed to be proactive, often prioritizing veterans at risk of disengaging from care. Designated VA care team members identified and reached out to veterans well before 12 months had passed since their prior visit (the VA definition of disengagement from care); visits included any care at VA, including, but not exclusively, primary care. Table 2 describes the key components of targeted outreach strategies: (1) identifying veterans’ last visit; (2) prioritizing which veterans to outreach to; and (3) assigning responsibility and reaching out. A key defining feature of targeted outreach is that veterans were identified and prioritized for outreach independent from any visits with mental health or other VA services.

At some facilities, mental health support staff were able to directly schedule primary care appointments, which was identified as an important enabling factor in promoting mental health patient engagement in primary care. Some interviewees seemed to take for granted the idea that mental health practitioners would help engage patients in primary care—suggesting that these practices had perhaps become a cultural norm within their facility. However, some interviewees identified clear strategies for making these practices a consistent part of care—for example, by designing a protocol for initial mental health assessments to include a routine check for primary care engagement.

Mental Health/Primary Care Connections

Interviewees characterized the nature of the connections between mental health and primary care at their facilities. Nearly all interviewees described that their medical centers had extensive ties, formal and informal, between mental health and primary care.

Formal ties may include the reverse integration care model, in which primary care services are embedded in mental health settings. Interviewees at sites with programs based on this model noted that these programs enabled warm hand-offs from mental health to primary care and suggested that it can foster integration between primary care and mental health care for patients with SMI. However, the size, scope, and structure of these programs varied, sometimes serving a small proportion of a facility’s population of SMI patients. Other examples of formal ties included written agreements, establishing frequent, regular meetings between mental health and primary care leadership and front-line staff, and giving mental health clerks the ability to directly schedule primary care appointments.

Discussion

VA facilities with high levels of primary care engagement among veterans with SMI used extensive engagement strategies, including a diverse array of targeted outreach and routine practices. In both approaches, intentional organizational structural and process decisions, as well as formal and informal ties between mental health and primary care, established and supported them. In addition, organizational cultural factors were especially relevant to routine practice strategies.

To enable targeted outreach, a bevy of organizational resources, both local and national were required. Large accountable care organizations and integrated delivery systems, like the VA, are often better able to create dashboards and other informational resources for population health management compared with smaller, less integrated health care systems. Though these resources are difficult to create in fragmented systems, comparable tools have been explored by multiple state health departments.¹² Our findings suggest that these data tools, though resource intensive to develop, may enable facilities to be more methodical and reliable in conducting outreach to vulnerable patients.

In contrast to targeted outreach, routine practices depend less on population health management resources and more on cultural norms. Such norms are notoriously difficult to change, but intentional structural decisions like embedding primary care engagement in mental health protocols may signal that primary care engagement is an important and legitimate consideration for mental health care.¹³

We identified extensive and heterogenous connections between mental health and primary care in our sample of VA facilities with high engagement of patients with SMI in primary care. A growing body of literature on relational coordination studies the factors that contribute to organizational siloing and mechanisms for breaking down those silos so work can be coordinated across boundaries (eg, the organizational boundary between mental health and primary care).¹⁴ Coordinating care across these boundaries, through good relational coordination practices has been shown to improve outcomes in health care and other sectors. Notably, VA facilities in our sample had several of the defining characteristics of good relational coordination: relationships between mental health and primary care that include shared goals, shared knowledge, and mutual respect, all reinforced by frequent communication structured around problem solving.¹⁵ The relational coordination literature also offers a way to identify evidence-based interventions for facilitating relational coordination in places where it is lacking, for example, with information systems, boundary-spanning individuals, facility design, and formal conflict resolution.¹⁵ Future work might explore how relational coordination can be further used to optimize mental health and primary care connections to keep veterans with SMI engaged in care.

Our approach of interviewing informants in higher-performing facilities draws heavily on the idea of positive deviance, which holds that information on what works in health care is available from organizations that already are demonstrating “consistently exceptional performance.”¹⁶ This approach works best when high performance and organizational characteristics are observable for a large number of facilities, and when high-performing facilities are willing to share their strategies. These features allow investigators to identify promising practices and hypotheses that can then be empirically tested and compared. Such testing, including assessing for unintended consequences, is needed for the approaches we identified. Research is also needed to assess for factors that would promote the implementation of effective strategies.

Limitations

As a QI project seeking to identify promising practices, our interviews were limited to 18 key informants across 11 VA facilities with high engagement of care among veterans with SMI. No inferences can be made that these practices are directly related to this high level of engagement, nor the differential impact of different practices. Future work is needed to assess for these relationships. We also did not interview veterans to understand their perspectives on these strategies, which is an additional important topic for future work. In addition, these interviews were performed before the start of the COVID-19 pandemic. Further work is needed to understand how these strategies may have been modified in response to changes in practice. The shift to care from in-person to virtual services may have impacted both clinical interactions with veterans, as well as between clinicians.

Conclusions

Interviews with key informants demonstrate that while engaging and retaining veterans with SMI in primary care is vital, it also requires intentional and potentially resource-intensive practices, including targeted outreach and routine engagement strategies embedded into mental health visits. These promising practices can provide valuable insights for both VA and community health care systems providing care to patients with SMI.

Acknowledgments

We thank Gracielle J. Tan, MD for administrative assistance in preparing this manuscript.

People with serious mental illness (SMI) are at substantial risk for premature mortality, dying on average 10 to 20 years earlier than others.¹ The reasons for this disparity are complex; however, the high prevalence of chronic disease and physical comorbidities in the SMI population have been identified as prominent factors.² Engagement and reengagement in care, including primary care for medical comorbidities, can mitigate these mortality risks.^2-4 Among veterans with SMI lost to follow-up care for more than 12 months, those not successfully reengaged in care were more likely to die compared with those reengaged in care.^2,3

Given this evidence, health care systems, including the US Department of Veterans Affairs (VA), have looked to better engage these patients in care. These efforts have included mental health population health management, colocation of mental health with primary care, designation of primary care teams specializing in SMI, and integration of mental health and primary care services for patients experiencing homelessness.^5-8

As part of a national approach to encourage locally driven quality improvement (QI), the VA compiles performance metrics for each facility, across a gamut of care settings, conditions, and veteran populations.⁹ Quarterly facility report cards, with longitudinal data and cross-facility comparisons, enable facilities to identify targets for QI and track improvement progress. One metric reports on the proportion of enrolled veterans with SMI who have primary care engagement, defined as having an assigned primary care practitioner (PCP) and a primary care visit in the prior 12 months.

In support of a QI initiative at the VA Greater Los Angeles Healthcare System (VAGLAHS), we sought to describe promising practices being utilized by VA facilities with higher levels of primary care engagement among their veterans with SMI populations.

Methods

We conducted semistructured telephone interviews with a purposeful sample of key informants at VA facilities with high levels of engagement in primary care among veterans with SMI. All project components were conducted by an interdisciplinary team, which included a medical anthropologist (JM), a mental health physician (PR), an internal medicine physician (KC), and other health services researchers (JB, AG). Because the primary objective of the project was QI, this project was designated as nonresearch by the VAGLAHS Institutional Review Board.

The VA Facility Complexity Model classifies facilities into 5 tiers: 1a (most complex), 1b, 1c, 2, and 3 (least complex), based on patient care volume, patient risk, complexity of clinical programs, and size of research and teaching programs. We sampled informants at VA facilities with complexity ratings of 1a or 1b with better than median scores for primary care engagement of veterans with SMI based on report cards from January 2019 to March 2019. To increase the likelihood of identifying lessons that can generalize to the VAGLAHS with its large population of veterans experiencing homelessness, we selected facilities serving populations consisting of more than 1000 veterans experiencing homelessness.

At each selected facility, we first aimed to interview mental health leaders responsible for quality measurement and improvement identified from a national VA database. We then used snowball sampling to identify other informants at these VA facilities who were knowledgeable about relevant processes. Potential interviewees were contacted via email.

Interviews

The interview guide was developed by the interdisciplinary team and based on published literature about strategies for engaging patients with SMI in care. Interview guide questions focused on local practice arrangements, panel management, population health practices, and quality measurement and improvement efforts for engaging veterans with SMI in primary care (Appendix). Interviews were conducted by telephone, from May 2019 through July 2019, by experienced qualitative interviewers (JM, JB). Interviewees were assured confidentiality of their responses.

Interview audio recordings were used to generate detailed notes (AG). Structured summaries were prepared from these notes, using a template based on the interview guide. We organized these summaries into matrices for analysis, grouping summarized points by interview domains to facilitate comparison across interviews.^10-11 Our team reviewed and discussed the matrices, and iteratively identified and defined themes to identify the common engagement approaches and the nature of the connections between mental health and primary care. To ensure rigor, findings were checked by the senior qualitative lead (JM).

Results

The median SMI engagement score—defined as the proportion of veterans with SMI who have had a primary care visit in the prior 12 months and who have an assigned PCP—was 75.6% across 1a and 1b VA facilities. We identified 16 VA facilities that had a median or higher score and more than 1000 enrolled veterans experiencing homelessness. From these16 facilities, we emailed 31 potential interviewees, 14 of whom were identified from a VA database and 17 referred by other interviewees. In total, we interviewed 18 key informants across 11 (69%) facilities, including chiefs of psychology and mental health services, PCPs with mental health expertise, QI specialists, a psychosocial rehabilitation leader, and a local recovery coordinator, who helps veterans with SMI access recovery-oriented services. Characteristics of the facilities and interviewees are shown in Table 1. Interviews lasted a mean 35 (range, 26-50) minutes.

Engagement Approaches

The strategies used to engage veterans with SMI were heterogenous, with no single strategy common across all facilities. However, we identified 2 categories of engagement approaches: targeted outreach and routine practices.

Targeted outreach strategies included deliberate, systematic approaches to reach veterans with SMI outside of regularly scheduled visits. These strategies were designed to be proactive, often prioritizing veterans at risk of disengaging from care. Designated VA care team members identified and reached out to veterans well before 12 months had passed since their prior visit (the VA definition of disengagement from care); visits included any care at VA, including, but not exclusively, primary care. Table 2 describes the key components of targeted outreach strategies: (1) identifying veterans’ last visit; (2) prioritizing which veterans to outreach to; and (3) assigning responsibility and reaching out. A key defining feature of targeted outreach is that veterans were identified and prioritized for outreach independent from any visits with mental health or other VA services.

At some facilities, mental health support staff were able to directly schedule primary care appointments, which was identified as an important enabling factor in promoting mental health patient engagement in primary care. Some interviewees seemed to take for granted the idea that mental health practitioners would help engage patients in primary care—suggesting that these practices had perhaps become a cultural norm within their facility. However, some interviewees identified clear strategies for making these practices a consistent part of care—for example, by designing a protocol for initial mental health assessments to include a routine check for primary care engagement.

Mental Health/Primary Care Connections

Interviewees characterized the nature of the connections between mental health and primary care at their facilities. Nearly all interviewees described that their medical centers had extensive ties, formal and informal, between mental health and primary care.

Formal ties may include the reverse integration care model, in which primary care services are embedded in mental health settings. Interviewees at sites with programs based on this model noted that these programs enabled warm hand-offs from mental health to primary care and suggested that it can foster integration between primary care and mental health care for patients with SMI. However, the size, scope, and structure of these programs varied, sometimes serving a small proportion of a facility’s population of SMI patients. Other examples of formal ties included written agreements, establishing frequent, regular meetings between mental health and primary care leadership and front-line staff, and giving mental health clerks the ability to directly schedule primary care appointments.

Discussion

VA facilities with high levels of primary care engagement among veterans with SMI used extensive engagement strategies, including a diverse array of targeted outreach and routine practices. In both approaches, intentional organizational structural and process decisions, as well as formal and informal ties between mental health and primary care, established and supported them. In addition, organizational cultural factors were especially relevant to routine practice strategies.

To enable targeted outreach, a bevy of organizational resources, both local and national were required. Large accountable care organizations and integrated delivery systems, like the VA, are often better able to create dashboards and other informational resources for population health management compared with smaller, less integrated health care systems. Though these resources are difficult to create in fragmented systems, comparable tools have been explored by multiple state health departments.¹² Our findings suggest that these data tools, though resource intensive to develop, may enable facilities to be more methodical and reliable in conducting outreach to vulnerable patients.

In contrast to targeted outreach, routine practices depend less on population health management resources and more on cultural norms. Such norms are notoriously difficult to change, but intentional structural decisions like embedding primary care engagement in mental health protocols may signal that primary care engagement is an important and legitimate consideration for mental health care.¹³

We identified extensive and heterogenous connections between mental health and primary care in our sample of VA facilities with high engagement of patients with SMI in primary care. A growing body of literature on relational coordination studies the factors that contribute to organizational siloing and mechanisms for breaking down those silos so work can be coordinated across boundaries (eg, the organizational boundary between mental health and primary care).¹⁴ Coordinating care across these boundaries, through good relational coordination practices has been shown to improve outcomes in health care and other sectors. Notably, VA facilities in our sample had several of the defining characteristics of good relational coordination: relationships between mental health and primary care that include shared goals, shared knowledge, and mutual respect, all reinforced by frequent communication structured around problem solving.¹⁵ The relational coordination literature also offers a way to identify evidence-based interventions for facilitating relational coordination in places where it is lacking, for example, with information systems, boundary-spanning individuals, facility design, and formal conflict resolution.¹⁵ Future work might explore how relational coordination can be further used to optimize mental health and primary care connections to keep veterans with SMI engaged in care.

Our approach of interviewing informants in higher-performing facilities draws heavily on the idea of positive deviance, which holds that information on what works in health care is available from organizations that already are demonstrating “consistently exceptional performance.”¹⁶ This approach works best when high performance and organizational characteristics are observable for a large number of facilities, and when high-performing facilities are willing to share their strategies. These features allow investigators to identify promising practices and hypotheses that can then be empirically tested and compared. Such testing, including assessing for unintended consequences, is needed for the approaches we identified. Research is also needed to assess for factors that would promote the implementation of effective strategies.

Limitations

As a QI project seeking to identify promising practices, our interviews were limited to 18 key informants across 11 VA facilities with high engagement of care among veterans with SMI. No inferences can be made that these practices are directly related to this high level of engagement, nor the differential impact of different practices. Future work is needed to assess for these relationships. We also did not interview veterans to understand their perspectives on these strategies, which is an additional important topic for future work. In addition, these interviews were performed before the start of the COVID-19 pandemic. Further work is needed to understand how these strategies may have been modified in response to changes in practice. The shift to care from in-person to virtual services may have impacted both clinical interactions with veterans, as well as between clinicians.

Conclusions

Interviews with key informants demonstrate that while engaging and retaining veterans with SMI in primary care is vital, it also requires intentional and potentially resource-intensive practices, including targeted outreach and routine engagement strategies embedded into mental health visits. These promising practices can provide valuable insights for both VA and community health care systems providing care to patients with SMI.

Acknowledgments

We thank Gracielle J. Tan, MD for administrative assistance in preparing this manuscript.

People with serious mental illness (SMI) are at substantial risk for premature mortality, dying on average 10 to 20 years earlier than others.¹ The reasons for this disparity are complex; however, the high prevalence of chronic disease and physical comorbidities in the SMI population have been identified as prominent factors.² Engagement and reengagement in care, including primary care for medical comorbidities, can mitigate these mortality risks.^2-4 Among veterans with SMI lost to follow-up care for more than 12 months, those not successfully reengaged in care were more likely to die compared with those reengaged in care.^2,3

Given this evidence, health care systems, including the US Department of Veterans Affairs (VA), have looked to better engage these patients in care. These efforts have included mental health population health management, colocation of mental health with primary care, designation of primary care teams specializing in SMI, and integration of mental health and primary care services for patients experiencing homelessness.^5-8

As part of a national approach to encourage locally driven quality improvement (QI), the VA compiles performance metrics for each facility, across a gamut of care settings, conditions, and veteran populations.⁹ Quarterly facility report cards, with longitudinal data and cross-facility comparisons, enable facilities to identify targets for QI and track improvement progress. One metric reports on the proportion of enrolled veterans with SMI who have primary care engagement, defined as having an assigned primary care practitioner (PCP) and a primary care visit in the prior 12 months.

In support of a QI initiative at the VA Greater Los Angeles Healthcare System (VAGLAHS), we sought to describe promising practices being utilized by VA facilities with higher levels of primary care engagement among their veterans with SMI populations.

Methods

We conducted semistructured telephone interviews with a purposeful sample of key informants at VA facilities with high levels of engagement in primary care among veterans with SMI. All project components were conducted by an interdisciplinary team, which included a medical anthropologist (JM), a mental health physician (PR), an internal medicine physician (KC), and other health services researchers (JB, AG). Because the primary objective of the project was QI, this project was designated as nonresearch by the VAGLAHS Institutional Review Board.

The VA Facility Complexity Model classifies facilities into 5 tiers: 1a (most complex), 1b, 1c, 2, and 3 (least complex), based on patient care volume, patient risk, complexity of clinical programs, and size of research and teaching programs. We sampled informants at VA facilities with complexity ratings of 1a or 1b with better than median scores for primary care engagement of veterans with SMI based on report cards from January 2019 to March 2019. To increase the likelihood of identifying lessons that can generalize to the VAGLAHS with its large population of veterans experiencing homelessness, we selected facilities serving populations consisting of more than 1000 veterans experiencing homelessness.

At each selected facility, we first aimed to interview mental health leaders responsible for quality measurement and improvement identified from a national VA database. We then used snowball sampling to identify other informants at these VA facilities who were knowledgeable about relevant processes. Potential interviewees were contacted via email.

Interviews

The interview guide was developed by the interdisciplinary team and based on published literature about strategies for engaging patients with SMI in care. Interview guide questions focused on local practice arrangements, panel management, population health practices, and quality measurement and improvement efforts for engaging veterans with SMI in primary care (Appendix). Interviews were conducted by telephone, from May 2019 through July 2019, by experienced qualitative interviewers (JM, JB). Interviewees were assured confidentiality of their responses.

Interview audio recordings were used to generate detailed notes (AG). Structured summaries were prepared from these notes, using a template based on the interview guide. We organized these summaries into matrices for analysis, grouping summarized points by interview domains to facilitate comparison across interviews.^10-11 Our team reviewed and discussed the matrices, and iteratively identified and defined themes to identify the common engagement approaches and the nature of the connections between mental health and primary care. To ensure rigor, findings were checked by the senior qualitative lead (JM).

Results

The median SMI engagement score—defined as the proportion of veterans with SMI who have had a primary care visit in the prior 12 months and who have an assigned PCP—was 75.6% across 1a and 1b VA facilities. We identified 16 VA facilities that had a median or higher score and more than 1000 enrolled veterans experiencing homelessness. From these16 facilities, we emailed 31 potential interviewees, 14 of whom were identified from a VA database and 17 referred by other interviewees. In total, we interviewed 18 key informants across 11 (69%) facilities, including chiefs of psychology and mental health services, PCPs with mental health expertise, QI specialists, a psychosocial rehabilitation leader, and a local recovery coordinator, who helps veterans with SMI access recovery-oriented services. Characteristics of the facilities and interviewees are shown in Table 1. Interviews lasted a mean 35 (range, 26-50) minutes.

Engagement Approaches

The strategies used to engage veterans with SMI were heterogenous, with no single strategy common across all facilities. However, we identified 2 categories of engagement approaches: targeted outreach and routine practices.

Targeted outreach strategies included deliberate, systematic approaches to reach veterans with SMI outside of regularly scheduled visits. These strategies were designed to be proactive, often prioritizing veterans at risk of disengaging from care. Designated VA care team members identified and reached out to veterans well before 12 months had passed since their prior visit (the VA definition of disengagement from care); visits included any care at VA, including, but not exclusively, primary care. Table 2 describes the key components of targeted outreach strategies: (1) identifying veterans’ last visit; (2) prioritizing which veterans to outreach to; and (3) assigning responsibility and reaching out. A key defining feature of targeted outreach is that veterans were identified and prioritized for outreach independent from any visits with mental health or other VA services.

At some facilities, mental health support staff were able to directly schedule primary care appointments, which was identified as an important enabling factor in promoting mental health patient engagement in primary care. Some interviewees seemed to take for granted the idea that mental health practitioners would help engage patients in primary care—suggesting that these practices had perhaps become a cultural norm within their facility. However, some interviewees identified clear strategies for making these practices a consistent part of care—for example, by designing a protocol for initial mental health assessments to include a routine check for primary care engagement.

Mental Health/Primary Care Connections

Interviewees characterized the nature of the connections between mental health and primary care at their facilities. Nearly all interviewees described that their medical centers had extensive ties, formal and informal, between mental health and primary care.

Formal ties may include the reverse integration care model, in which primary care services are embedded in mental health settings. Interviewees at sites with programs based on this model noted that these programs enabled warm hand-offs from mental health to primary care and suggested that it can foster integration between primary care and mental health care for patients with SMI. However, the size, scope, and structure of these programs varied, sometimes serving a small proportion of a facility’s population of SMI patients. Other examples of formal ties included written agreements, establishing frequent, regular meetings between mental health and primary care leadership and front-line staff, and giving mental health clerks the ability to directly schedule primary care appointments.

Discussion

VA facilities with high levels of primary care engagement among veterans with SMI used extensive engagement strategies, including a diverse array of targeted outreach and routine practices. In both approaches, intentional organizational structural and process decisions, as well as formal and informal ties between mental health and primary care, established and supported them. In addition, organizational cultural factors were especially relevant to routine practice strategies.

To enable targeted outreach, a bevy of organizational resources, both local and national were required. Large accountable care organizations and integrated delivery systems, like the VA, are often better able to create dashboards and other informational resources for population health management compared with smaller, less integrated health care systems. Though these resources are difficult to create in fragmented systems, comparable tools have been explored by multiple state health departments.¹² Our findings suggest that these data tools, though resource intensive to develop, may enable facilities to be more methodical and reliable in conducting outreach to vulnerable patients.

In contrast to targeted outreach, routine practices depend less on population health management resources and more on cultural norms. Such norms are notoriously difficult to change, but intentional structural decisions like embedding primary care engagement in mental health protocols may signal that primary care engagement is an important and legitimate consideration for mental health care.¹³

We identified extensive and heterogenous connections between mental health and primary care in our sample of VA facilities with high engagement of patients with SMI in primary care. A growing body of literature on relational coordination studies the factors that contribute to organizational siloing and mechanisms for breaking down those silos so work can be coordinated across boundaries (eg, the organizational boundary between mental health and primary care).¹⁴ Coordinating care across these boundaries, through good relational coordination practices has been shown to improve outcomes in health care and other sectors. Notably, VA facilities in our sample had several of the defining characteristics of good relational coordination: relationships between mental health and primary care that include shared goals, shared knowledge, and mutual respect, all reinforced by frequent communication structured around problem solving.¹⁵ The relational coordination literature also offers a way to identify evidence-based interventions for facilitating relational coordination in places where it is lacking, for example, with information systems, boundary-spanning individuals, facility design, and formal conflict resolution.¹⁵ Future work might explore how relational coordination can be further used to optimize mental health and primary care connections to keep veterans with SMI engaged in care.

Our approach of interviewing informants in higher-performing facilities draws heavily on the idea of positive deviance, which holds that information on what works in health care is available from organizations that already are demonstrating “consistently exceptional performance.”¹⁶ This approach works best when high performance and organizational characteristics are observable for a large number of facilities, and when high-performing facilities are willing to share their strategies. These features allow investigators to identify promising practices and hypotheses that can then be empirically tested and compared. Such testing, including assessing for unintended consequences, is needed for the approaches we identified. Research is also needed to assess for factors that would promote the implementation of effective strategies.

Limitations

As a QI project seeking to identify promising practices, our interviews were limited to 18 key informants across 11 VA facilities with high engagement of care among veterans with SMI. No inferences can be made that these practices are directly related to this high level of engagement, nor the differential impact of different practices. Future work is needed to assess for these relationships. We also did not interview veterans to understand their perspectives on these strategies, which is an additional important topic for future work. In addition, these interviews were performed before the start of the COVID-19 pandemic. Further work is needed to understand how these strategies may have been modified in response to changes in practice. The shift to care from in-person to virtual services may have impacted both clinical interactions with veterans, as well as between clinicians.

Conclusions

Interviews with key informants demonstrate that while engaging and retaining veterans with SMI in primary care is vital, it also requires intentional and potentially resource-intensive practices, including targeted outreach and routine engagement strategies embedded into mental health visits. These promising practices can provide valuable insights for both VA and community health care systems providing care to patients with SMI.

Acknowledgments

We thank Gracielle J. Tan, MD for administrative assistance in preparing this manuscript.

Many of the 85 hospitals in Los Angeles County (LAC) routinely test patients for SARS-CoV-2, the virus that causes COVID-19, upon admission to the hospital.¹ However, not all SARS-CoV-2 detections represent acute COVID-19 for at least two reasons. First, the SARS-CoV-2 real-time polymerase chain reaction (RT-PCR) assay can report a false-positive result.² Second, approximately 40% to 45% of persons with SARS-CoV-2 infection are asymptomatic, and RT-PCR tests can remain positive more than 2 months after an individual recovers from COVID-19; thus, SARS-CoV-2 detected on admission might represent shedding of nonviable virus from a prior unrecognized or undiagnosed infection.^1,3

Public health policymakers closely monitor the rate of COVID-19 hospitalizations because it informs decisions to impose or relax COVID-19 control measures. However, the percentage of hospitalizations misclassified as COVID-19–associated because of incidentally detected SARS-CoV-2 (ie, COVID-19 was not a primary or contributing cause of hospitalization) is unknown. Therefore, we sought to determine the percentage of hospitalizations in LAC classified as having COVID-19 that might have had incidental SARS-CoV-2 detection.

The state of California requires healthcare providers to report all COVID-19 cases and clinical laboratories to report all SARS-CoV-2 diagnostic test results. Hospitals in LAC are mandated to report daily lists of all persons hospitalized with suspected or confirmed COVID-19 to the LAC Department of Public Health (DPH) COVID-19 Hospital Electronic Surveillance System (CHESS).⁴ Hospitals provide daily data to CHESS containing information about patients in their facilities with COVID-19. We conducted a cross-sectional retrospective study by selecting a random set of medical records from CHESS for review.

We began regularly and systematically reviewing medical records of patients in CHESS discharged after August 1, 2020, as part of LAC DPH surveillance to characterize persons experiencing severe COVID-19, defined as illness requiring hospitalization. For severe COVID-19 surveillance, we randomly selected 45 discharged patients per week from CHESS in August 2020 and 50 discharged patients per week between September and October 2020. To ensure that the sample represented the overall age distribution of patients in CHESS, we ordered patients by birth date and selected every k record, where k represented the interval between patients needed to meet the target for the week. Before random sample selection, several free text fields from the CHESS dataset were queried to identify and remove patients who were not LAC residents, were seen in the emergency department but not admitted, were hospitalized for <1 day, were discharged from a non-acute care hospital, or if the hospital-reported patient did not have a positive SARS-CoV-2 test. We then requested full medical records for these patients from the respective hospitals. After we received the medical records, a team of four nurses independently reviewed the medical charts and excluded patients who did not meet the above listed exclusion criteria; patients were excluded at two points—during the automated query and again by manual review.

In addition, severe COVID-19 surveillance was intended to characterize primary admissions for COVID-19, defined as having a documented positive SARS-CoV-2 result within 10 days of symptom onset or hospital admission and no prior hospitalization for COVID-19. The date of the first positive result was validated by locating the positive SARS-COV-2 result in the patient’s medical record and/or the LAC COVID surveillance database; the patient was excluded from analysis if a positive SARS-CoV-2 result could not be found. Excluded discharges were not replaced by a new randomly selected patient. Instead, we oversampled the number of weekly charts to request with a goal of having 40 to 45 charts per week that met inclusion criteria for abstraction.

For this analysis, we examined medical records abstracted for discharges occurring between August 1 and October 31, 2020. We categorized hospitalizations into one of the following: (1) “likely COVID-19–associated” if the patient had a clinical or radiographic diagnosis of pneumonia or acute respiratory distress syndrome or measured fever (>100.4 °F) with new cough or shortness of breath; (2) “not COVID-19–associated” if patient was admitted primarily for a traumatic or accidental injury, acute psychiatric illness, or full-term uncomplicated delivery, or was tested preoperatively for an elective procedure in the absence of other acute medical illnesses (other causes were considered on a case-by-case basis based on the consensus of the chart abstraction team); and (3) “potentially COVID-19–associated” for all other hospitalizations not meeting criteria for the other two categories. We considered the identification of SARS-CoV-2 in patients classified as “not COVID-19–associated” to be incidental to the reason for hospitalization. When the medical records reviewer classified a hospitalization as “not COVID-19–associated,” the primary reason for hospitalization was entered into a tracking database and no further data were collected.

Descriptive statistics and all analyses were conducted using SAS version 9.4 (SAS Institute). Confidence limits (CL) were calculated using the proc freq CL option in SAS. Chi-square analysis was conducted to determine whether trends in hospitalization categories changed over time. Statistical significance was set at P < .05.

Of the 13,813 hospital discharges reported to CHESS from August to October 2020, 3,182 (23%) records were not eligible for inclusion in the random selection sample for the following reasons: 1,765 (13%) patients reported by hospitals did not have a positive COVID-19 test, 734 (5%) discharges were for non-LAC residents, 636 (5%) patients had a length of hospital stay <1 day, and 47 (<1%) discharges were from a non-acute care hospital. From the 10,631 discharges in CHESS meeting preliminary inclusion criteria from August 1 to October 31, 2020, we randomly selected 618 discharges for medical record review. Of the 618 discharges, 504 (85%) medical records were available for review as of November 30, 2020. After review of the 504 medical records, an additional 158 were excluded because 83 (13%) had a first documented positive SARS-CoV-2 test that was >10 days from hospital admission or symptom onset, 34 (6%) were previously hospitalized for COVID-19, 29 (5%) had an emergency department visit only, 6 (1%) were discharged from a non-acute care hospital, and 6 (1%) were non-LAC residents. We reviewed medical records for 346 (56%) of the 618 hospitalizations that met our inclusion criteria.

The demographic characteristics of patients included in our sample were similar to those of the overall patient population in CHESS (Table 1). Most patients in our final study population were male (54%), older than 50 years (66%), and Hispanic (60%); the median length of hospital stay for survivors was 5 days (first quartile–third quartile: 3 to 8 days).

Our analysis indicates that 71% (95% CL, 66%-75%) of hospital discharges were “likely COVID-19-associated”; 12% (CL, 9%-16%) were “not COVID-19–associated” and, therefore, had incidentally detected SARS-CoV-2; and 17% were “potentially COVID-19–associated” (CL, 13%-21%). The percentage of hospitalizations classified as “likely,” “potentially,” and “not COVID-19–associated” did not change from month-to-month during the study period (P = .81). Full-term delivery was the most common reason for hospitalization among patients with incidentally detected SARS-CoV-2 (Table 2).

The primary public health objective of the COVID-19 pandemic response has been to prevent overwhelming the healthcare system by slowing disease transmission. LAC DPH closely monitors the daily number of hospitalized COVID-19 patients, defined as hospitalization of a person with an associated positive SARS-CoV-2 result. However, increasing community transmission of SARS-CoV-2 can complicate interpretation of hospitalization data because it is likely that some patients with incidentally detected, nonviable virus will be misclassified as having COVID-19. Overestimating the burden of COVID-19–associated hospitalizations may lead public health policymakers to impose more restrictive control measures or remove restrictions more slowly. Results from this study can inform policymakers about the potential magnitude of overestimating COVID-19–associated hospitalizations.

Our results indicate that SARS-CoV-2 detection might be incidental (ie, “not COVID-19–associated”) in approximately one of eight persons hospitalized with COVID-19 in LAC. We likely underestimated the percentage of hospitalizations with incidental SARS-CoV-2 detection because our definition of “not COVID-19–associated” hospitalizations was intended to be specific for identifying patients who had no clear reason for SARS-CoV-2 testing except a presumed hospital policy of testing on admission or preoperatively. In addition, several patients classified as having a “potentially COVID-19–associated” hospitalization also had a primary reason for admission that currently does not have a clear link to COVID-19 (eg, Bell’s palsy and pelvic inflammatory disease). Although our sample size was relatively small, it was representative of all potential COVID-19 hospitalizations in LAC over a 3-month period.

CONCLUSION

Detection of SARS-CoV-2 in a person with a clinical presentation that is not compatible with COVID-19 can complicate initial clinical management because it is unclear if the result represents presymptomatic or asymptomatic infection, prolonged shedding of nonviable virus, or a false-positive result. Considering the consequences of missing a true infection, such as transmission to other staff or patients, healthcare providers are obligated to treat the test result as a real infection. Therefore, our results are not applicable to patient-level clinical management decisions, but highlight the need for policymakers and emergency preparedness personnel to consider that hospital-reported data might overestimate the burden of COVID-19 hospitalizations when making decisions that rely on hospitalization data as a metric. Additional research is needed to develop methods for correcting hospitalization data to account for patients in whom incidentally detected SARS-CoV-2 was not a direct or contributing cause of hospitalization. Adjusting COVID-19–associated hospitalization rates to account for incidental SARS-CoV-2 detection could allow for optimal resource planning by public health policymakers.

Many of the 85 hospitals in Los Angeles County (LAC) routinely test patients for SARS-CoV-2, the virus that causes COVID-19, upon admission to the hospital.¹ However, not all SARS-CoV-2 detections represent acute COVID-19 for at least two reasons. First, the SARS-CoV-2 real-time polymerase chain reaction (RT-PCR) assay can report a false-positive result.² Second, approximately 40% to 45% of persons with SARS-CoV-2 infection are asymptomatic, and RT-PCR tests can remain positive more than 2 months after an individual recovers from COVID-19; thus, SARS-CoV-2 detected on admission might represent shedding of nonviable virus from a prior unrecognized or undiagnosed infection.^1,3

Public health policymakers closely monitor the rate of COVID-19 hospitalizations because it informs decisions to impose or relax COVID-19 control measures. However, the percentage of hospitalizations misclassified as COVID-19–associated because of incidentally detected SARS-CoV-2 (ie, COVID-19 was not a primary or contributing cause of hospitalization) is unknown. Therefore, we sought to determine the percentage of hospitalizations in LAC classified as having COVID-19 that might have had incidental SARS-CoV-2 detection.

The state of California requires healthcare providers to report all COVID-19 cases and clinical laboratories to report all SARS-CoV-2 diagnostic test results. Hospitals in LAC are mandated to report daily lists of all persons hospitalized with suspected or confirmed COVID-19 to the LAC Department of Public Health (DPH) COVID-19 Hospital Electronic Surveillance System (CHESS).⁴ Hospitals provide daily data to CHESS containing information about patients in their facilities with COVID-19. We conducted a cross-sectional retrospective study by selecting a random set of medical records from CHESS for review.

We began regularly and systematically reviewing medical records of patients in CHESS discharged after August 1, 2020, as part of LAC DPH surveillance to characterize persons experiencing severe COVID-19, defined as illness requiring hospitalization. For severe COVID-19 surveillance, we randomly selected 45 discharged patients per week from CHESS in August 2020 and 50 discharged patients per week between September and October 2020. To ensure that the sample represented the overall age distribution of patients in CHESS, we ordered patients by birth date and selected every k record, where k represented the interval between patients needed to meet the target for the week. Before random sample selection, several free text fields from the CHESS dataset were queried to identify and remove patients who were not LAC residents, were seen in the emergency department but not admitted, were hospitalized for <1 day, were discharged from a non-acute care hospital, or if the hospital-reported patient did not have a positive SARS-CoV-2 test. We then requested full medical records for these patients from the respective hospitals. After we received the medical records, a team of four nurses independently reviewed the medical charts and excluded patients who did not meet the above listed exclusion criteria; patients were excluded at two points—during the automated query and again by manual review.

In addition, severe COVID-19 surveillance was intended to characterize primary admissions for COVID-19, defined as having a documented positive SARS-CoV-2 result within 10 days of symptom onset or hospital admission and no prior hospitalization for COVID-19. The date of the first positive result was validated by locating the positive SARS-COV-2 result in the patient’s medical record and/or the LAC COVID surveillance database; the patient was excluded from analysis if a positive SARS-CoV-2 result could not be found. Excluded discharges were not replaced by a new randomly selected patient. Instead, we oversampled the number of weekly charts to request with a goal of having 40 to 45 charts per week that met inclusion criteria for abstraction.

For this analysis, we examined medical records abstracted for discharges occurring between August 1 and October 31, 2020. We categorized hospitalizations into one of the following: (1) “likely COVID-19–associated” if the patient had a clinical or radiographic diagnosis of pneumonia or acute respiratory distress syndrome or measured fever (>100.4 °F) with new cough or shortness of breath; (2) “not COVID-19–associated” if patient was admitted primarily for a traumatic or accidental injury, acute psychiatric illness, or full-term uncomplicated delivery, or was tested preoperatively for an elective procedure in the absence of other acute medical illnesses (other causes were considered on a case-by-case basis based on the consensus of the chart abstraction team); and (3) “potentially COVID-19–associated” for all other hospitalizations not meeting criteria for the other two categories. We considered the identification of SARS-CoV-2 in patients classified as “not COVID-19–associated” to be incidental to the reason for hospitalization. When the medical records reviewer classified a hospitalization as “not COVID-19–associated,” the primary reason for hospitalization was entered into a tracking database and no further data were collected.

Descriptive statistics and all analyses were conducted using SAS version 9.4 (SAS Institute). Confidence limits (CL) were calculated using the proc freq CL option in SAS. Chi-square analysis was conducted to determine whether trends in hospitalization categories changed over time. Statistical significance was set at P < .05.

Of the 13,813 hospital discharges reported to CHESS from August to October 2020, 3,182 (23%) records were not eligible for inclusion in the random selection sample for the following reasons: 1,765 (13%) patients reported by hospitals did not have a positive COVID-19 test, 734 (5%) discharges were for non-LAC residents, 636 (5%) patients had a length of hospital stay <1 day, and 47 (<1%) discharges were from a non-acute care hospital. From the 10,631 discharges in CHESS meeting preliminary inclusion criteria from August 1 to October 31, 2020, we randomly selected 618 discharges for medical record review. Of the 618 discharges, 504 (85%) medical records were available for review as of November 30, 2020. After review of the 504 medical records, an additional 158 were excluded because 83 (13%) had a first documented positive SARS-CoV-2 test that was >10 days from hospital admission or symptom onset, 34 (6%) were previously hospitalized for COVID-19, 29 (5%) had an emergency department visit only, 6 (1%) were discharged from a non-acute care hospital, and 6 (1%) were non-LAC residents. We reviewed medical records for 346 (56%) of the 618 hospitalizations that met our inclusion criteria.

The demographic characteristics of patients included in our sample were similar to those of the overall patient population in CHESS (Table 1). Most patients in our final study population were male (54%), older than 50 years (66%), and Hispanic (60%); the median length of hospital stay for survivors was 5 days (first quartile–third quartile: 3 to 8 days).

Our analysis indicates that 71% (95% CL, 66%-75%) of hospital discharges were “likely COVID-19-associated”; 12% (CL, 9%-16%) were “not COVID-19–associated” and, therefore, had incidentally detected SARS-CoV-2; and 17% were “potentially COVID-19–associated” (CL, 13%-21%). The percentage of hospitalizations classified as “likely,” “potentially,” and “not COVID-19–associated” did not change from month-to-month during the study period (P = .81). Full-term delivery was the most common reason for hospitalization among patients with incidentally detected SARS-CoV-2 (Table 2).

The primary public health objective of the COVID-19 pandemic response has been to prevent overwhelming the healthcare system by slowing disease transmission. LAC DPH closely monitors the daily number of hospitalized COVID-19 patients, defined as hospitalization of a person with an associated positive SARS-CoV-2 result. However, increasing community transmission of SARS-CoV-2 can complicate interpretation of hospitalization data because it is likely that some patients with incidentally detected, nonviable virus will be misclassified as having COVID-19. Overestimating the burden of COVID-19–associated hospitalizations may lead public health policymakers to impose more restrictive control measures or remove restrictions more slowly. Results from this study can inform policymakers about the potential magnitude of overestimating COVID-19–associated hospitalizations.

Our results indicate that SARS-CoV-2 detection might be incidental (ie, “not COVID-19–associated”) in approximately one of eight persons hospitalized with COVID-19 in LAC. We likely underestimated the percentage of hospitalizations with incidental SARS-CoV-2 detection because our definition of “not COVID-19–associated” hospitalizations was intended to be specific for identifying patients who had no clear reason for SARS-CoV-2 testing except a presumed hospital policy of testing on admission or preoperatively. In addition, several patients classified as having a “potentially COVID-19–associated” hospitalization also had a primary reason for admission that currently does not have a clear link to COVID-19 (eg, Bell’s palsy and pelvic inflammatory disease). Although our sample size was relatively small, it was representative of all potential COVID-19 hospitalizations in LAC over a 3-month period.

CONCLUSION

Detection of SARS-CoV-2 in a person with a clinical presentation that is not compatible with COVID-19 can complicate initial clinical management because it is unclear if the result represents presymptomatic or asymptomatic infection, prolonged shedding of nonviable virus, or a false-positive result. Considering the consequences of missing a true infection, such as transmission to other staff or patients, healthcare providers are obligated to treat the test result as a real infection. Therefore, our results are not applicable to patient-level clinical management decisions, but highlight the need for policymakers and emergency preparedness personnel to consider that hospital-reported data might overestimate the burden of COVID-19 hospitalizations when making decisions that rely on hospitalization data as a metric. Additional research is needed to develop methods for correcting hospitalization data to account for patients in whom incidentally detected SARS-CoV-2 was not a direct or contributing cause of hospitalization. Adjusting COVID-19–associated hospitalization rates to account for incidental SARS-CoV-2 detection could allow for optimal resource planning by public health policymakers.

Many of the 85 hospitals in Los Angeles County (LAC) routinely test patients for SARS-CoV-2, the virus that causes COVID-19, upon admission to the hospital.¹ However, not all SARS-CoV-2 detections represent acute COVID-19 for at least two reasons. First, the SARS-CoV-2 real-time polymerase chain reaction (RT-PCR) assay can report a false-positive result.² Second, approximately 40% to 45% of persons with SARS-CoV-2 infection are asymptomatic, and RT-PCR tests can remain positive more than 2 months after an individual recovers from COVID-19; thus, SARS-CoV-2 detected on admission might represent shedding of nonviable virus from a prior unrecognized or undiagnosed infection.^1,3

Public health policymakers closely monitor the rate of COVID-19 hospitalizations because it informs decisions to impose or relax COVID-19 control measures. However, the percentage of hospitalizations misclassified as COVID-19–associated because of incidentally detected SARS-CoV-2 (ie, COVID-19 was not a primary or contributing cause of hospitalization) is unknown. Therefore, we sought to determine the percentage of hospitalizations in LAC classified as having COVID-19 that might have had incidental SARS-CoV-2 detection.

The state of California requires healthcare providers to report all COVID-19 cases and clinical laboratories to report all SARS-CoV-2 diagnostic test results. Hospitals in LAC are mandated to report daily lists of all persons hospitalized with suspected or confirmed COVID-19 to the LAC Department of Public Health (DPH) COVID-19 Hospital Electronic Surveillance System (CHESS).⁴ Hospitals provide daily data to CHESS containing information about patients in their facilities with COVID-19. We conducted a cross-sectional retrospective study by selecting a random set of medical records from CHESS for review.

We began regularly and systematically reviewing medical records of patients in CHESS discharged after August 1, 2020, as part of LAC DPH surveillance to characterize persons experiencing severe COVID-19, defined as illness requiring hospitalization. For severe COVID-19 surveillance, we randomly selected 45 discharged patients per week from CHESS in August 2020 and 50 discharged patients per week between September and October 2020. To ensure that the sample represented the overall age distribution of patients in CHESS, we ordered patients by birth date and selected every k record, where k represented the interval between patients needed to meet the target for the week. Before random sample selection, several free text fields from the CHESS dataset were queried to identify and remove patients who were not LAC residents, were seen in the emergency department but not admitted, were hospitalized for <1 day, were discharged from a non-acute care hospital, or if the hospital-reported patient did not have a positive SARS-CoV-2 test. We then requested full medical records for these patients from the respective hospitals. After we received the medical records, a team of four nurses independently reviewed the medical charts and excluded patients who did not meet the above listed exclusion criteria; patients were excluded at two points—during the automated query and again by manual review.

In addition, severe COVID-19 surveillance was intended to characterize primary admissions for COVID-19, defined as having a documented positive SARS-CoV-2 result within 10 days of symptom onset or hospital admission and no prior hospitalization for COVID-19. The date of the first positive result was validated by locating the positive SARS-COV-2 result in the patient’s medical record and/or the LAC COVID surveillance database; the patient was excluded from analysis if a positive SARS-CoV-2 result could not be found. Excluded discharges were not replaced by a new randomly selected patient. Instead, we oversampled the number of weekly charts to request with a goal of having 40 to 45 charts per week that met inclusion criteria for abstraction.

For this analysis, we examined medical records abstracted for discharges occurring between August 1 and October 31, 2020. We categorized hospitalizations into one of the following: (1) “likely COVID-19–associated” if the patient had a clinical or radiographic diagnosis of pneumonia or acute respiratory distress syndrome or measured fever (>100.4 °F) with new cough or shortness of breath; (2) “not COVID-19–associated” if patient was admitted primarily for a traumatic or accidental injury, acute psychiatric illness, or full-term uncomplicated delivery, or was tested preoperatively for an elective procedure in the absence of other acute medical illnesses (other causes were considered on a case-by-case basis based on the consensus of the chart abstraction team); and (3) “potentially COVID-19–associated” for all other hospitalizations not meeting criteria for the other two categories. We considered the identification of SARS-CoV-2 in patients classified as “not COVID-19–associated” to be incidental to the reason for hospitalization. When the medical records reviewer classified a hospitalization as “not COVID-19–associated,” the primary reason for hospitalization was entered into a tracking database and no further data were collected.

Descriptive statistics and all analyses were conducted using SAS version 9.4 (SAS Institute). Confidence limits (CL) were calculated using the proc freq CL option in SAS. Chi-square analysis was conducted to determine whether trends in hospitalization categories changed over time. Statistical significance was set at P < .05.

Of the 13,813 hospital discharges reported to CHESS from August to October 2020, 3,182 (23%) records were not eligible for inclusion in the random selection sample for the following reasons: 1,765 (13%) patients reported by hospitals did not have a positive COVID-19 test, 734 (5%) discharges were for non-LAC residents, 636 (5%) patients had a length of hospital stay <1 day, and 47 (<1%) discharges were from a non-acute care hospital. From the 10,631 discharges in CHESS meeting preliminary inclusion criteria from August 1 to October 31, 2020, we randomly selected 618 discharges for medical record review. Of the 618 discharges, 504 (85%) medical records were available for review as of November 30, 2020. After review of the 504 medical records, an additional 158 were excluded because 83 (13%) had a first documented positive SARS-CoV-2 test that was >10 days from hospital admission or symptom onset, 34 (6%) were previously hospitalized for COVID-19, 29 (5%) had an emergency department visit only, 6 (1%) were discharged from a non-acute care hospital, and 6 (1%) were non-LAC residents. We reviewed medical records for 346 (56%) of the 618 hospitalizations that met our inclusion criteria.

The demographic characteristics of patients included in our sample were similar to those of the overall patient population in CHESS (Table 1). Most patients in our final study population were male (54%), older than 50 years (66%), and Hispanic (60%); the median length of hospital stay for survivors was 5 days (first quartile–third quartile: 3 to 8 days).

Our analysis indicates that 71% (95% CL, 66%-75%) of hospital discharges were “likely COVID-19-associated”; 12% (CL, 9%-16%) were “not COVID-19–associated” and, therefore, had incidentally detected SARS-CoV-2; and 17% were “potentially COVID-19–associated” (CL, 13%-21%). The percentage of hospitalizations classified as “likely,” “potentially,” and “not COVID-19–associated” did not change from month-to-month during the study period (P = .81). Full-term delivery was the most common reason for hospitalization among patients with incidentally detected SARS-CoV-2 (Table 2).

The primary public health objective of the COVID-19 pandemic response has been to prevent overwhelming the healthcare system by slowing disease transmission. LAC DPH closely monitors the daily number of hospitalized COVID-19 patients, defined as hospitalization of a person with an associated positive SARS-CoV-2 result. However, increasing community transmission of SARS-CoV-2 can complicate interpretation of hospitalization data because it is likely that some patients with incidentally detected, nonviable virus will be misclassified as having COVID-19. Overestimating the burden of COVID-19–associated hospitalizations may lead public health policymakers to impose more restrictive control measures or remove restrictions more slowly. Results from this study can inform policymakers about the potential magnitude of overestimating COVID-19–associated hospitalizations.

Our results indicate that SARS-CoV-2 detection might be incidental (ie, “not COVID-19–associated”) in approximately one of eight persons hospitalized with COVID-19 in LAC. We likely underestimated the percentage of hospitalizations with incidental SARS-CoV-2 detection because our definition of “not COVID-19–associated” hospitalizations was intended to be specific for identifying patients who had no clear reason for SARS-CoV-2 testing except a presumed hospital policy of testing on admission or preoperatively. In addition, several patients classified as having a “potentially COVID-19–associated” hospitalization also had a primary reason for admission that currently does not have a clear link to COVID-19 (eg, Bell’s palsy and pelvic inflammatory disease). Although our sample size was relatively small, it was representative of all potential COVID-19 hospitalizations in LAC over a 3-month period.

CONCLUSION

Detection of SARS-CoV-2 in a person with a clinical presentation that is not compatible with COVID-19 can complicate initial clinical management because it is unclear if the result represents presymptomatic or asymptomatic infection, prolonged shedding of nonviable virus, or a false-positive result. Considering the consequences of missing a true infection, such as transmission to other staff or patients, healthcare providers are obligated to treat the test result as a real infection. Therefore, our results are not applicable to patient-level clinical management decisions, but highlight the need for policymakers and emergency preparedness personnel to consider that hospital-reported data might overestimate the burden of COVID-19 hospitalizations when making decisions that rely on hospitalization data as a metric. Additional research is needed to develop methods for correcting hospitalization data to account for patients in whom incidentally detected SARS-CoV-2 was not a direct or contributing cause of hospitalization. Adjusting COVID-19–associated hospitalization rates to account for incidental SARS-CoV-2 detection could allow for optimal resource planning by public health policymakers.

People often do not receive the kind of care they want at the end of their lives.^1,2 Although most people say they do not wish to have aggressive interventions if they are dying,^3-5 nearly one in five dies in the hospital and one in seven dies in the intensive care unit (ICU), where aggressive care is usually provided.⁶ Coming demographic shifts will put this phenomenon in relief. The US Census Bureau estimates the number of people over age 85 will balloon to 20 million by 2050.⁷

A proposed strategy for reducing this mismatch is to expand shared decision making for people facing life-sustaining treatment decisions.^8-10 Patient decision aids are tools that help people make informed healthcare decisions in light of their values and preferences, facilitating shared decision making.^8,11 Decision aids can take many forms: paper-based, audio/video-based, or online. They can be intended for the clinical encounter (used in partnership with a physician, nurse, or other clinician), independent patient use, or peer-to-peer use.⁸ In a 2017 review, Stacey and colleagues found that patient decision aids improve knowledge, clarify values, encourage more active decision making, and improve risk perception, across a variety of treatment and screening decisions.¹² They also concluded that decision aids might help people make decisions that are more aligned with their values, without affecting health outcomes negatively. ¹²

The number of available patient decision aids for people making life-sustaining treatment choices during serious illness near death is currently unknown. A 2014 review of all advanced care planning decision aids, including those for people who are healthy and people who are seriously ill, found 16 published studies in the peer-reviewed literature that tested patient decision aids for advanced care planning, but they did not systematically search the Internet and query key informants.¹³

Given the frequency of serious illness and death in hospital settings, awareness of potentially useful tools, their quality, and their use may be of interest to practicing hospitalists. This awareness may inform their decision making around whether or not to use decision aids in their own practice.

Study Aims and Design

With our systematic environmental scan, we aimed to identify all decision aids available to seriously ill people near death facing choices about life-sustaining treatments, developed by both academic researchers and private organizations. We set out to articulate their quality and the degree to which they are used.

Protocol

We developed four research questions to address our study objectives. Our questions were as follows: (1) What English-language patient decision aids are available? (2) What are the characteristics of these patient decision aids? (3) What is the quality of these patient decision aids, including readability? (4) What organizations use these patient decision aids in routine care (exploratory)? ^14-16 See protocol: doi: 10.1007/s40271-017-0268-2.¹⁷

Decision Aid Search Strategy

We searched for patient decision aids among published systematic reviews, Internet search results (Google.com), and app stores (Google Play and Apple App Store). To identify previously published systematic reviews, we searched MEDLINE via PubMed, with the date range from inception to 2017. We chose not to include other academic databases because the unit of observation for this environmental scan was the decision aids themselves, not the published articles. Additionally, we were aware of systematic reviews concerning this issue and felt that adding additional databases would not appreciably improve our likelihood of identifying eligible decision aids. We conducted searches using Google.com on November 30, 2016, and January 26, 2017, and included the first 100 search results. We also contacted shared decision-making and palliative care experts using a previously established list, via an online survey and one-on-one interviews between April 17, 2017, and August 30, 2017.

Published Reviews

Using a search strategy developed with a librarian, we identified reviews of decision aids that met our inclusion criteria using the MEDLINE database.¹⁷ The primary reviewer (CHS) examined the results of the search, identifying reviews appropriate for further investigation and the secondary reviewer (KP) extracted patient decision aids potentially eligible for our study. See Appendix Table 1 and our published protocol.¹⁷ Notably, given that the decision aids themselves, not published articles, were the unit of observation for our environmental scan, we did not perform dual coding on the MEDLINE extraction.

Google and App Stores

Two reviewers (CHS and MAD) performed the Google and application screening, including both the Apple App Store and Google Play.¹⁷ Using Google Advanced Search, we ran the queries detailed in Appendix Table 2. We disabled cookies and limited our search to English.

The primary reviewer ran each Google search and app store search, archiving the first 100 results of Google searches and first 50 results of app store searches.¹⁸ Then, the primary reviewer opened each page and scanned for patient decision aids or references to patient decision aids, marking those that met our inclusion criteria, those that might meet our inclusion criteria with further research, and those that were not appropriate. We documented specific reasons for exclusion. The secondary reviewer assessed a randomly-selected, 10% subsample. We calculated interrater reliability using a Cohen’s Kappa statistic.

Key Informants

To identify decision aids that did not appear in our online search, we surveyed 187 key informants who work in or study issues related to aging, death and dying and shared decision making.¹⁹ We developed a questionnaire for these informants and deployed it using the online survey software Qualtrics (see Appendix 1. Key Informant Survey). We used a snowball approach, asking participants for other individuals they thought we should speak with about other relevant decision aids. We corresponded with individuals who suggested decision aids that were not already in our decision aid database.

Decision Aid Selection Criteria

We included patient decision aids designed to help seriously ill people near death or their caregivers make decisions about life-sustaining treatments. See Appendix Table 1 for an explanation of terms. We saved decision aids that met our inclusion criteria in an online database, organizing them by target user or index decision(s). When identified decision aids were unavailable online, we e-mailed developers three times to ask for access to the decision aid. If after three queries, we did not receive access to the decision aid, we excluded the tool from our review. Similarly, if developers explicitly refused to participate in the study, we excluded them.

Once we banked and organized the decision aids, one reviewer (KP) systematically collected information about decision aid characteristics using a data collection form (see Appendix 2. Table 3). The data we collected for decision aids from all sources included (1) the index decision, (2) secondary decision(s), (3) the disease/condition, (4) availability (whether the decision aids are available publicly or proprietorially), and (5) use, ie, whether we learned anything about routine use in clinical environments.

Decision Aid Quality Grading Methods

At least two or three reviewers (C.H.S., K.P., M.A.D.), independently assessed the quality of each included patient decision aid, using the NQF standards. Before assessing the quality of each decision aid, we tested an NQF quality assessment form on five decision aids. We subsequently added specificity to the NQF quality criteria for this review. At least two of three reviewers (CHS, KP, MAD) assessed the quality of all included patient decision aids. We calculated interrater reliability using both Cohen’s Kappa statistic for individual quality categories and Spearman’s correlations for overall scores.

Notably, one of the NQF items concerns plain language. We assessed plain language using average readability scores, generated via Readable.io. If readability scores were below seventh-grade level, we considered them plain language. When we could not assess readability using an average score, ie, in the case of video decision aids, the researchers made a qualitative judgment about the plain language criteria.

Statistical Analysis

Our primary outcome was the number and variety of decision aids available for seriously ill individuals near death facing choices about life-sustaining treatments. Secondary outcomes included the quality, actual availability, and use of the available decision aids. We used Stata 13 to synthesize our results. We also reported overall quality and use. We conducted subgroup analyses, including quality, availability, and use of decision aids by category.

Decision Aid Selection Process

We identified 608 links with information about potential decision aids from our Google search. The two raters had substantial interrater reliability according to Cohen’s Kappa statistic (K = 0.64).²⁰ We did not detect any possible decision aids with our app store searches. We identified 31 studies from our MEDLINE search with information about potential decision aids eligible for inclusion. We received 60 responses to our expert survey from the 187 administered (a 32% response rate).

Altogether, we identified 105 potential decision aids from these sources. We excluded 22/105 potential decision aids from our analysis because they were not publicly accessible, and we could not successfully obtain them from the developers. It remains unknown whether these tools would have qualified for inclusion in our review. We excluded 55/105 tools for not meeting one of the following criteria: 1) not being decision aids according to the NQF criteria 2) not concerning life-sustaining treatments 3) not being targeted at people with serious illness near death. A majority of decision aids for life-sustaining treatment decisions are intended for people who do not yet have an advanced serious illness or are not near death. There were 27 decision aids in our final review (Figure 1).

Characteristics of Included Decision Aids

Of the 27 decision aids we included in our review, 14 (52%) were tailored to seriously ill individuals with specific conditions. Eleven decision aids (41%) concerned specific life-sustaining treatments. Two decision aids concerned general treatment approaches, such as life-sustaining care versus palliative care (Table 1).

The decision aids were of variable length and approach. Some were text only, while others were image heavy. The mean length of decision aids was 19 pages, while the median length was 10 pages. Included decision aids offered interventions meant to return patients to health, as well as palliative interventions and comfort care.

Notably, most of the decision aids we included in our review (25 decision aids; 93%) were freely available online. Three (11%) were not. Seventeen (63%) decision aids were developed in the U.S., eight (30%) in Canada, two (7%) in Australia, and one (4%) in the Netherlands (in Dutch, translated using Google Translate). Additionally, there were 22 potentially eligible decision aids that we could not access to review and therefore could not include.

Quality of Included Decision Aids

The overall correlation of scores between the two reviewers was high (0.85). Agreement was high for both reviewers for all categories (balanced 90%, K = 0.0; outcome probabilities 86%, K = 0.7; publication date 93%, K = 0.8; update policy 93%, K = 0.7; funding sources 96%, K = 0.8), except the category concerning the rigor of the decision aid development process (66%, K = 0.2) and the evidence sources used (79%, K = 0.6) categories.

The quality of the decision aids was high in some categories. Of 27 decision aids, most presented options in a balanced way (24, 89%) and identified funding sources (23, 85%). They also reported publication dates most of the time (19, 70%). Readability of the included decision aids was mixed. The average readability grade level was 7.5, with a low score of 4.1 and a high score of 10.7. Eleven decision aids (41%) had readability levels less than seventh grade (Table 2). Thirteen had plain language, including video decision aids that we agreed used plain language.

The decision aids also had consistently low scores in some categories. Of 27, only 11 listed their evidence sources (41%), 11 reported a rigorous evidence-synthesis method (41%), six stated their competing interests (22%), and three offered an update policy (11%). There were no notable differences in the quality of the decision aids in each of the three category types (condition-specific, treatment-specific, general).

Use of Included and Excluded Decision Aids (exploratory)

We received 60 of 187 responses to our key informant survey. We asked every respondent if they were aware of any relevant decision aids. Of the 60 respondents, 45 (75%) said they were aware of decision aids, but only 38 (63%) offered the names of potential tools. Twenty-six respondents (43%) said they were aware of institutions that used the decision aids in routine and sustained care. Twenty-four respondents (40%) offered names of organizations, but most of the suggestions concerned decision aids that did not qualify for inclusion in our review or care that was not routine or sustained. In this preliminary use estimation, we found evidence for the use of only three decision aids or similar tools in routine care, two of which we included in our review.

We found many decision aids of varying quality for people with serious illnesses facing decisions about life-sustaining treatments. Most available decision aids are customized for people with particular diseases or conditions, like cancer or heart failure, with few generalized tools. This may make it difficult for practicing clinicians to find tools that are appropriate for their patients. It could also contribute to the gap between their availability and use in routine care, which is an essential but exploratory finding of this systematic environmental scan. Even if seriously ill people or those who cared for them wanted to obtain and use a decision aid independently, a large proportion of them are not publicly accessible.

Concerning the quality of decision aids, they were usually balanced and listed their funding sources, but other quality areas we often missing concerning their development, content, and disclosures. These deficiencies may affect the trustworthiness of decision aids, which may make practicing clinicians less likely to use them in hospital settings. Reporting of outcome probabilities was particularly weak. Reporting outcome probabilities in ways that people who are ill and their relatives can understand, especially during times of heightened emotion, is critically important. Therefore, it is a cause for concern that the available decision aids often neglect to use evidence-based techniques for conveying outcome information.

Our work built on Butler and colleagues’ “state of the science” review in 2014.¹³ Focusing specifically on proximal life-sustaining treatment decisions, we found many more decision aids by expanding our search beyond the peer-reviewed literature to include the Internet and experts.¹³ We also identified an important gap worthy of further exploration between the decision aids available and their usage in real-world clinical environments.

Our review confirms that implementation of decision aids in routine care is a continued challenge, especially for seriously ill people facing life-sustaining treatment decisions.⁵³ Why tools that are efficacious in controlled trial environments have failed to gain acceptance in real-world settings remains unanswered for this population.⁵⁴ For decision aids in general, researchers have reported barriers concerning clinician awareness, perception, and comfort, as well as usability issues.^55,56 Additionally, systems-level barriers exist, like culture and priorities, difficulty incorporating decision aids into the workflow, resistance from parties who favor other interventions, and the costs associated with implementation.⁵⁶ There may also be particular barriers related to the topics of death and dying.A strength of this work is thatwe applied the rigor of the systematic review method to the environmental scan, a newer method that answers different questions, such as “How many?”, “How much?”, and “How often?” We hope our use of the word systematic will reinforce perception among the scientific community that the environmental scan method is thorough, valid and worthwhile. We believe this method unearthed more decision aids than a traditional systematic review limited to the academic literature would have revealed. Another strength of our review was the rigor of screening and assessment.

A limitation of our work is the challenge of defining serious illness. We worked with palliative care physicians to make these judgments as grounded in clinical practice as possible. The preliminary nature and selection of experts for our sustained—use survey are limitations as well. Despite our efforts to conduct a comprehensive review of a vast environment of tools, we may have missed some decision aids that met our inclusion criteria. An additional limitation of our work is that due to the exploratory nature of our sustained-use survey, we cannot determine with accuracy how often these tools are used, although we have provided the first preliminary assessment of use, to our knowledge.

The gap between prolific patient decision aid development and real-world usage is puzzling. It is possible that using a tool at all is inappropriate for the complex, emotionally-laden decision-making process associated with death and dying. Alternatively, the tools may be inappropriate for serious illness, due to their design, their content, or some other characteristics. Perhaps the existing tools are too tailored for specific conditions and interventions―less appropriate for generalized use. Indeed, only two decision aids included in our final review addressed general care pathways, like life-sustaining care, palliative care, and hospice care. The others were highly specific, concerning particular diseases like kidney disease and particular interventions, like CPR. We know that most people die with comultimorbidities, meaning such specificity may paradoxically make it more difficult for individuals and their families to identify with the content in the materials.^57,58 Without having data from real-world use, we cannot know whether any particular tool is suited or helpful for hospital practice.

It is essential for practicing hospitalists to know whether patient decision aids are appropriate for use in routine care. We hope that our review will help clinicians and health systems find appropriate tools to use with their patients. We also believe there should be mechanisms for providing feedback on whether decision aids are feasible and acceptable to hospitalized people and their caregivers and to practicing hospitalists and what leads to their sustained implementation.^55,56 This can be explored with on-the-ground observational research or through health system quality improvement efforts.

Acknowledgments

Pamela J. Bagley provided search strategy support. Meredith MacMartin provided clinical counsel. Amber Barnato provided comments and insight as an advisor and a new member of Catherine’s Ph.D. committee.

Author contributions

Catherine H. Saunders designed the study, with support from Marie-Anne Durand, Glyn Elwyn, and Kathryn Kirkland. Catherine H. Saunders conducted all screening, with support from Marie-Anne Durand. Khusbu Patel managed the inventory of decision aids. Catherine H. Saunders designed and distributed the key informant survey, with support from Marie-Anne Durand. Hyunkyung Kang and Catherine H. Saunders managed follow-up with key informants. Khusbu Patel and Catherine H. Saunders conducted the decision aid quality review. Catherine H. Saunders, Marie-Anne Durand, and Kathryn Kirkland screened decision aids to determine appropriateness for people with serious illness. Catherine H. Saunders drafted the manuscript, and all authors reviewed and approved it.

Ethical approval

The Dartmouth College Committee for the Protection of Human Subjects designated this project as exempt from further review. All survey participants confirmed their consent via an online form.

Disclosures

Ms. Saunders, Ms. Patel, Ms. Kang, and Dr. Kirkland have nothing to disclose. Dr. Elwyn reports personal fees from ACCESS Community Health Network, personal fees from EBSCO Health, personal fees from Chicago (Federally Qualified Medical Centers), outside the submitted work, and as Director of &think LLC, which owns the registered trademark for OptionGrids(TM) patient decision aids. He owns copyright in measures of shared decision making and care integration, namely collaboRATE, integRATE, Observer OPTION-5, and Observer OPTION-12, which are freely available for use. He is codeveloper of the OptionGrid patient decision aids, which are licensed to EBSCO Health. He has received reimbursement for travel, accommodations, and expenses from EBSCO Health, ACCESS Community Health Network, and Chicago (Federally Qualified Medical Centers). Dr. Durand reports personal fees from ACCESS Community Health Network, personal fees from EBSCO Health, outside the submitted work, and as codeveloper of the OptionGrid patient decision aids, which are licensed to EBSCO Health. She has received reimbursement for travel, accommodations, and expenses from EBSCO Health and ACCESS Community Health Network.

Financial conflicts of interest

Glyn Elwyn (GE) and Marie-Anne Durand (M-A D) have developed the Option Grid patient decision aids, which are licensed to EBSCO Health. They receive consulting income from EBSCO Health and may receive royalties in the future. M-A D is a consultant for ACCESS Community Health Network. No other competing interests declared.

Funding

The authors did not receive funding for this research.

Published protocol linked here: https://www.ncbi.nlm.nih.gov/pubmed/28825182

People often do not receive the kind of care they want at the end of their lives.^1,2 Although most people say they do not wish to have aggressive interventions if they are dying,^3-5 nearly one in five dies in the hospital and one in seven dies in the intensive care unit (ICU), where aggressive care is usually provided.⁶ Coming demographic shifts will put this phenomenon in relief. The US Census Bureau estimates the number of people over age 85 will balloon to 20 million by 2050.⁷

A proposed strategy for reducing this mismatch is to expand shared decision making for people facing life-sustaining treatment decisions.^8-10 Patient decision aids are tools that help people make informed healthcare decisions in light of their values and preferences, facilitating shared decision making.^8,11 Decision aids can take many forms: paper-based, audio/video-based, or online. They can be intended for the clinical encounter (used in partnership with a physician, nurse, or other clinician), independent patient use, or peer-to-peer use.⁸ In a 2017 review, Stacey and colleagues found that patient decision aids improve knowledge, clarify values, encourage more active decision making, and improve risk perception, across a variety of treatment and screening decisions.¹² They also concluded that decision aids might help people make decisions that are more aligned with their values, without affecting health outcomes negatively. ¹²

The number of available patient decision aids for people making life-sustaining treatment choices during serious illness near death is currently unknown. A 2014 review of all advanced care planning decision aids, including those for people who are healthy and people who are seriously ill, found 16 published studies in the peer-reviewed literature that tested patient decision aids for advanced care planning, but they did not systematically search the Internet and query key informants.¹³

Given the frequency of serious illness and death in hospital settings, awareness of potentially useful tools, their quality, and their use may be of interest to practicing hospitalists. This awareness may inform their decision making around whether or not to use decision aids in their own practice.

Study Aims and Design

With our systematic environmental scan, we aimed to identify all decision aids available to seriously ill people near death facing choices about life-sustaining treatments, developed by both academic researchers and private organizations. We set out to articulate their quality and the degree to which they are used.

Protocol

We developed four research questions to address our study objectives. Our questions were as follows: (1) What English-language patient decision aids are available? (2) What are the characteristics of these patient decision aids? (3) What is the quality of these patient decision aids, including readability? (4) What organizations use these patient decision aids in routine care (exploratory)? ^14-16 See protocol: doi: 10.1007/s40271-017-0268-2.¹⁷

Decision Aid Search Strategy

We searched for patient decision aids among published systematic reviews, Internet search results (Google.com), and app stores (Google Play and Apple App Store). To identify previously published systematic reviews, we searched MEDLINE via PubMed, with the date range from inception to 2017. We chose not to include other academic databases because the unit of observation for this environmental scan was the decision aids themselves, not the published articles. Additionally, we were aware of systematic reviews concerning this issue and felt that adding additional databases would not appreciably improve our likelihood of identifying eligible decision aids. We conducted searches using Google.com on November 30, 2016, and January 26, 2017, and included the first 100 search results. We also contacted shared decision-making and palliative care experts using a previously established list, via an online survey and one-on-one interviews between April 17, 2017, and August 30, 2017.

Published Reviews

Using a search strategy developed with a librarian, we identified reviews of decision aids that met our inclusion criteria using the MEDLINE database.¹⁷ The primary reviewer (CHS) examined the results of the search, identifying reviews appropriate for further investigation and the secondary reviewer (KP) extracted patient decision aids potentially eligible for our study. See Appendix Table 1 and our published protocol.¹⁷ Notably, given that the decision aids themselves, not published articles, were the unit of observation for our environmental scan, we did not perform dual coding on the MEDLINE extraction.

Google and App Stores

Two reviewers (CHS and MAD) performed the Google and application screening, including both the Apple App Store and Google Play.¹⁷ Using Google Advanced Search, we ran the queries detailed in Appendix Table 2. We disabled cookies and limited our search to English.

The primary reviewer ran each Google search and app store search, archiving the first 100 results of Google searches and first 50 results of app store searches.¹⁸ Then, the primary reviewer opened each page and scanned for patient decision aids or references to patient decision aids, marking those that met our inclusion criteria, those that might meet our inclusion criteria with further research, and those that were not appropriate. We documented specific reasons for exclusion. The secondary reviewer assessed a randomly-selected, 10% subsample. We calculated interrater reliability using a Cohen’s Kappa statistic.

Key Informants

To identify decision aids that did not appear in our online search, we surveyed 187 key informants who work in or study issues related to aging, death and dying and shared decision making.¹⁹ We developed a questionnaire for these informants and deployed it using the online survey software Qualtrics (see Appendix 1. Key Informant Survey). We used a snowball approach, asking participants for other individuals they thought we should speak with about other relevant decision aids. We corresponded with individuals who suggested decision aids that were not already in our decision aid database.

Decision Aid Selection Criteria

We included patient decision aids designed to help seriously ill people near death or their caregivers make decisions about life-sustaining treatments. See Appendix Table 1 for an explanation of terms. We saved decision aids that met our inclusion criteria in an online database, organizing them by target user or index decision(s). When identified decision aids were unavailable online, we e-mailed developers three times to ask for access to the decision aid. If after three queries, we did not receive access to the decision aid, we excluded the tool from our review. Similarly, if developers explicitly refused to participate in the study, we excluded them.

Once we banked and organized the decision aids, one reviewer (KP) systematically collected information about decision aid characteristics using a data collection form (see Appendix 2. Table 3). The data we collected for decision aids from all sources included (1) the index decision, (2) secondary decision(s), (3) the disease/condition, (4) availability (whether the decision aids are available publicly or proprietorially), and (5) use, ie, whether we learned anything about routine use in clinical environments.

Decision Aid Quality Grading Methods

At least two or three reviewers (C.H.S., K.P., M.A.D.), independently assessed the quality of each included patient decision aid, using the NQF standards. Before assessing the quality of each decision aid, we tested an NQF quality assessment form on five decision aids. We subsequently added specificity to the NQF quality criteria for this review. At least two of three reviewers (CHS, KP, MAD) assessed the quality of all included patient decision aids. We calculated interrater reliability using both Cohen’s Kappa statistic for individual quality categories and Spearman’s correlations for overall scores.

Notably, one of the NQF items concerns plain language. We assessed plain language using average readability scores, generated via Readable.io. If readability scores were below seventh-grade level, we considered them plain language. When we could not assess readability using an average score, ie, in the case of video decision aids, the researchers made a qualitative judgment about the plain language criteria.

Statistical Analysis

Our primary outcome was the number and variety of decision aids available for seriously ill individuals near death facing choices about life-sustaining treatments. Secondary outcomes included the quality, actual availability, and use of the available decision aids. We used Stata 13 to synthesize our results. We also reported overall quality and use. We conducted subgroup analyses, including quality, availability, and use of decision aids by category.

Decision Aid Selection Process

We identified 608 links with information about potential decision aids from our Google search. The two raters had substantial interrater reliability according to Cohen’s Kappa statistic (K = 0.64).²⁰ We did not detect any possible decision aids with our app store searches. We identified 31 studies from our MEDLINE search with information about potential decision aids eligible for inclusion. We received 60 responses to our expert survey from the 187 administered (a 32% response rate).

Altogether, we identified 105 potential decision aids from these sources. We excluded 22/105 potential decision aids from our analysis because they were not publicly accessible, and we could not successfully obtain them from the developers. It remains unknown whether these tools would have qualified for inclusion in our review. We excluded 55/105 tools for not meeting one of the following criteria: 1) not being decision aids according to the NQF criteria 2) not concerning life-sustaining treatments 3) not being targeted at people with serious illness near death. A majority of decision aids for life-sustaining treatment decisions are intended for people who do not yet have an advanced serious illness or are not near death. There were 27 decision aids in our final review (Figure 1).

Characteristics of Included Decision Aids

Of the 27 decision aids we included in our review, 14 (52%) were tailored to seriously ill individuals with specific conditions. Eleven decision aids (41%) concerned specific life-sustaining treatments. Two decision aids concerned general treatment approaches, such as life-sustaining care versus palliative care (Table 1).

The decision aids were of variable length and approach. Some were text only, while others were image heavy. The mean length of decision aids was 19 pages, while the median length was 10 pages. Included decision aids offered interventions meant to return patients to health, as well as palliative interventions and comfort care.

Notably, most of the decision aids we included in our review (25 decision aids; 93%) were freely available online. Three (11%) were not. Seventeen (63%) decision aids were developed in the U.S., eight (30%) in Canada, two (7%) in Australia, and one (4%) in the Netherlands (in Dutch, translated using Google Translate). Additionally, there were 22 potentially eligible decision aids that we could not access to review and therefore could not include.

Quality of Included Decision Aids

The overall correlation of scores between the two reviewers was high (0.85). Agreement was high for both reviewers for all categories (balanced 90%, K = 0.0; outcome probabilities 86%, K = 0.7; publication date 93%, K = 0.8; update policy 93%, K = 0.7; funding sources 96%, K = 0.8), except the category concerning the rigor of the decision aid development process (66%, K = 0.2) and the evidence sources used (79%, K = 0.6) categories.

The quality of the decision aids was high in some categories. Of 27 decision aids, most presented options in a balanced way (24, 89%) and identified funding sources (23, 85%). They also reported publication dates most of the time (19, 70%). Readability of the included decision aids was mixed. The average readability grade level was 7.5, with a low score of 4.1 and a high score of 10.7. Eleven decision aids (41%) had readability levels less than seventh grade (Table 2). Thirteen had plain language, including video decision aids that we agreed used plain language.

The decision aids also had consistently low scores in some categories. Of 27, only 11 listed their evidence sources (41%), 11 reported a rigorous evidence-synthesis method (41%), six stated their competing interests (22%), and three offered an update policy (11%). There were no notable differences in the quality of the decision aids in each of the three category types (condition-specific, treatment-specific, general).

Use of Included and Excluded Decision Aids (exploratory)

We received 60 of 187 responses to our key informant survey. We asked every respondent if they were aware of any relevant decision aids. Of the 60 respondents, 45 (75%) said they were aware of decision aids, but only 38 (63%) offered the names of potential tools. Twenty-six respondents (43%) said they were aware of institutions that used the decision aids in routine and sustained care. Twenty-four respondents (40%) offered names of organizations, but most of the suggestions concerned decision aids that did not qualify for inclusion in our review or care that was not routine or sustained. In this preliminary use estimation, we found evidence for the use of only three decision aids or similar tools in routine care, two of which we included in our review.

We found many decision aids of varying quality for people with serious illnesses facing decisions about life-sustaining treatments. Most available decision aids are customized for people with particular diseases or conditions, like cancer or heart failure, with few generalized tools. This may make it difficult for practicing clinicians to find tools that are appropriate for their patients. It could also contribute to the gap between their availability and use in routine care, which is an essential but exploratory finding of this systematic environmental scan. Even if seriously ill people or those who cared for them wanted to obtain and use a decision aid independently, a large proportion of them are not publicly accessible.

Concerning the quality of decision aids, they were usually balanced and listed their funding sources, but other quality areas we often missing concerning their development, content, and disclosures. These deficiencies may affect the trustworthiness of decision aids, which may make practicing clinicians less likely to use them in hospital settings. Reporting of outcome probabilities was particularly weak. Reporting outcome probabilities in ways that people who are ill and their relatives can understand, especially during times of heightened emotion, is critically important. Therefore, it is a cause for concern that the available decision aids often neglect to use evidence-based techniques for conveying outcome information.

Our work built on Butler and colleagues’ “state of the science” review in 2014.¹³ Focusing specifically on proximal life-sustaining treatment decisions, we found many more decision aids by expanding our search beyond the peer-reviewed literature to include the Internet and experts.¹³ We also identified an important gap worthy of further exploration between the decision aids available and their usage in real-world clinical environments.

Our review confirms that implementation of decision aids in routine care is a continued challenge, especially for seriously ill people facing life-sustaining treatment decisions.⁵³ Why tools that are efficacious in controlled trial environments have failed to gain acceptance in real-world settings remains unanswered for this population.⁵⁴ For decision aids in general, researchers have reported barriers concerning clinician awareness, perception, and comfort, as well as usability issues.^55,56 Additionally, systems-level barriers exist, like culture and priorities, difficulty incorporating decision aids into the workflow, resistance from parties who favor other interventions, and the costs associated with implementation.⁵⁶ There may also be particular barriers related to the topics of death and dying.A strength of this work is thatwe applied the rigor of the systematic review method to the environmental scan, a newer method that answers different questions, such as “How many?”, “How much?”, and “How often?” We hope our use of the word systematic will reinforce perception among the scientific community that the environmental scan method is thorough, valid and worthwhile. We believe this method unearthed more decision aids than a traditional systematic review limited to the academic literature would have revealed. Another strength of our review was the rigor of screening and assessment.

A limitation of our work is the challenge of defining serious illness. We worked with palliative care physicians to make these judgments as grounded in clinical practice as possible. The preliminary nature and selection of experts for our sustained—use survey are limitations as well. Despite our efforts to conduct a comprehensive review of a vast environment of tools, we may have missed some decision aids that met our inclusion criteria. An additional limitation of our work is that due to the exploratory nature of our sustained-use survey, we cannot determine with accuracy how often these tools are used, although we have provided the first preliminary assessment of use, to our knowledge.

The gap between prolific patient decision aid development and real-world usage is puzzling. It is possible that using a tool at all is inappropriate for the complex, emotionally-laden decision-making process associated with death and dying. Alternatively, the tools may be inappropriate for serious illness, due to their design, their content, or some other characteristics. Perhaps the existing tools are too tailored for specific conditions and interventions―less appropriate for generalized use. Indeed, only two decision aids included in our final review addressed general care pathways, like life-sustaining care, palliative care, and hospice care. The others were highly specific, concerning particular diseases like kidney disease and particular interventions, like CPR. We know that most people die with comultimorbidities, meaning such specificity may paradoxically make it more difficult for individuals and their families to identify with the content in the materials.^57,58 Without having data from real-world use, we cannot know whether any particular tool is suited or helpful for hospital practice.

It is essential for practicing hospitalists to know whether patient decision aids are appropriate for use in routine care. We hope that our review will help clinicians and health systems find appropriate tools to use with their patients. We also believe there should be mechanisms for providing feedback on whether decision aids are feasible and acceptable to hospitalized people and their caregivers and to practicing hospitalists and what leads to their sustained implementation.^55,56 This can be explored with on-the-ground observational research or through health system quality improvement efforts.

Acknowledgments

Pamela J. Bagley provided search strategy support. Meredith MacMartin provided clinical counsel. Amber Barnato provided comments and insight as an advisor and a new member of Catherine’s Ph.D. committee.

Author contributions

Catherine H. Saunders designed the study, with support from Marie-Anne Durand, Glyn Elwyn, and Kathryn Kirkland. Catherine H. Saunders conducted all screening, with support from Marie-Anne Durand. Khusbu Patel managed the inventory of decision aids. Catherine H. Saunders designed and distributed the key informant survey, with support from Marie-Anne Durand. Hyunkyung Kang and Catherine H. Saunders managed follow-up with key informants. Khusbu Patel and Catherine H. Saunders conducted the decision aid quality review. Catherine H. Saunders, Marie-Anne Durand, and Kathryn Kirkland screened decision aids to determine appropriateness for people with serious illness. Catherine H. Saunders drafted the manuscript, and all authors reviewed and approved it.

Ethical approval

The Dartmouth College Committee for the Protection of Human Subjects designated this project as exempt from further review. All survey participants confirmed their consent via an online form.

Disclosures

Ms. Saunders, Ms. Patel, Ms. Kang, and Dr. Kirkland have nothing to disclose. Dr. Elwyn reports personal fees from ACCESS Community Health Network, personal fees from EBSCO Health, personal fees from Chicago (Federally Qualified Medical Centers), outside the submitted work, and as Director of &think LLC, which owns the registered trademark for OptionGrids(TM) patient decision aids. He owns copyright in measures of shared decision making and care integration, namely collaboRATE, integRATE, Observer OPTION-5, and Observer OPTION-12, which are freely available for use. He is codeveloper of the OptionGrid patient decision aids, which are licensed to EBSCO Health. He has received reimbursement for travel, accommodations, and expenses from EBSCO Health, ACCESS Community Health Network, and Chicago (Federally Qualified Medical Centers). Dr. Durand reports personal fees from ACCESS Community Health Network, personal fees from EBSCO Health, outside the submitted work, and as codeveloper of the OptionGrid patient decision aids, which are licensed to EBSCO Health. She has received reimbursement for travel, accommodations, and expenses from EBSCO Health and ACCESS Community Health Network.

Financial conflicts of interest

Glyn Elwyn (GE) and Marie-Anne Durand (M-A D) have developed the Option Grid patient decision aids, which are licensed to EBSCO Health. They receive consulting income from EBSCO Health and may receive royalties in the future. M-A D is a consultant for ACCESS Community Health Network. No other competing interests declared.

Funding

The authors did not receive funding for this research.

Published protocol linked here: https://www.ncbi.nlm.nih.gov/pubmed/28825182

People often do not receive the kind of care they want at the end of their lives.^1,2 Although most people say they do not wish to have aggressive interventions if they are dying,^3-5 nearly one in five dies in the hospital and one in seven dies in the intensive care unit (ICU), where aggressive care is usually provided.⁶ Coming demographic shifts will put this phenomenon in relief. The US Census Bureau estimates the number of people over age 85 will balloon to 20 million by 2050.⁷

A proposed strategy for reducing this mismatch is to expand shared decision making for people facing life-sustaining treatment decisions.^8-10 Patient decision aids are tools that help people make informed healthcare decisions in light of their values and preferences, facilitating shared decision making.^8,11 Decision aids can take many forms: paper-based, audio/video-based, or online. They can be intended for the clinical encounter (used in partnership with a physician, nurse, or other clinician), independent patient use, or peer-to-peer use.⁸ In a 2017 review, Stacey and colleagues found that patient decision aids improve knowledge, clarify values, encourage more active decision making, and improve risk perception, across a variety of treatment and screening decisions.¹² They also concluded that decision aids might help people make decisions that are more aligned with their values, without affecting health outcomes negatively. ¹²

The number of available patient decision aids for people making life-sustaining treatment choices during serious illness near death is currently unknown. A 2014 review of all advanced care planning decision aids, including those for people who are healthy and people who are seriously ill, found 16 published studies in the peer-reviewed literature that tested patient decision aids for advanced care planning, but they did not systematically search the Internet and query key informants.¹³

Given the frequency of serious illness and death in hospital settings, awareness of potentially useful tools, their quality, and their use may be of interest to practicing hospitalists. This awareness may inform their decision making around whether or not to use decision aids in their own practice.

Study Aims and Design

With our systematic environmental scan, we aimed to identify all decision aids available to seriously ill people near death facing choices about life-sustaining treatments, developed by both academic researchers and private organizations. We set out to articulate their quality and the degree to which they are used.

Protocol

We developed four research questions to address our study objectives. Our questions were as follows: (1) What English-language patient decision aids are available? (2) What are the characteristics of these patient decision aids? (3) What is the quality of these patient decision aids, including readability? (4) What organizations use these patient decision aids in routine care (exploratory)? ^14-16 See protocol: doi: 10.1007/s40271-017-0268-2.¹⁷

Decision Aid Search Strategy

We searched for patient decision aids among published systematic reviews, Internet search results (Google.com), and app stores (Google Play and Apple App Store). To identify previously published systematic reviews, we searched MEDLINE via PubMed, with the date range from inception to 2017. We chose not to include other academic databases because the unit of observation for this environmental scan was the decision aids themselves, not the published articles. Additionally, we were aware of systematic reviews concerning this issue and felt that adding additional databases would not appreciably improve our likelihood of identifying eligible decision aids. We conducted searches using Google.com on November 30, 2016, and January 26, 2017, and included the first 100 search results. We also contacted shared decision-making and palliative care experts using a previously established list, via an online survey and one-on-one interviews between April 17, 2017, and August 30, 2017.

Published Reviews

Using a search strategy developed with a librarian, we identified reviews of decision aids that met our inclusion criteria using the MEDLINE database.¹⁷ The primary reviewer (CHS) examined the results of the search, identifying reviews appropriate for further investigation and the secondary reviewer (KP) extracted patient decision aids potentially eligible for our study. See Appendix Table 1 and our published protocol.¹⁷ Notably, given that the decision aids themselves, not published articles, were the unit of observation for our environmental scan, we did not perform dual coding on the MEDLINE extraction.

Google and App Stores

Two reviewers (CHS and MAD) performed the Google and application screening, including both the Apple App Store and Google Play.¹⁷ Using Google Advanced Search, we ran the queries detailed in Appendix Table 2. We disabled cookies and limited our search to English.

The primary reviewer ran each Google search and app store search, archiving the first 100 results of Google searches and first 50 results of app store searches.¹⁸ Then, the primary reviewer opened each page and scanned for patient decision aids or references to patient decision aids, marking those that met our inclusion criteria, those that might meet our inclusion criteria with further research, and those that were not appropriate. We documented specific reasons for exclusion. The secondary reviewer assessed a randomly-selected, 10% subsample. We calculated interrater reliability using a Cohen’s Kappa statistic.

Key Informants

To identify decision aids that did not appear in our online search, we surveyed 187 key informants who work in or study issues related to aging, death and dying and shared decision making.¹⁹ We developed a questionnaire for these informants and deployed it using the online survey software Qualtrics (see Appendix 1. Key Informant Survey). We used a snowball approach, asking participants for other individuals they thought we should speak with about other relevant decision aids. We corresponded with individuals who suggested decision aids that were not already in our decision aid database.

Decision Aid Selection Criteria

We included patient decision aids designed to help seriously ill people near death or their caregivers make decisions about life-sustaining treatments. See Appendix Table 1 for an explanation of terms. We saved decision aids that met our inclusion criteria in an online database, organizing them by target user or index decision(s). When identified decision aids were unavailable online, we e-mailed developers three times to ask for access to the decision aid. If after three queries, we did not receive access to the decision aid, we excluded the tool from our review. Similarly, if developers explicitly refused to participate in the study, we excluded them.

Once we banked and organized the decision aids, one reviewer (KP) systematically collected information about decision aid characteristics using a data collection form (see Appendix 2. Table 3). The data we collected for decision aids from all sources included (1) the index decision, (2) secondary decision(s), (3) the disease/condition, (4) availability (whether the decision aids are available publicly or proprietorially), and (5) use, ie, whether we learned anything about routine use in clinical environments.

Decision Aid Quality Grading Methods

At least two or three reviewers (C.H.S., K.P., M.A.D.), independently assessed the quality of each included patient decision aid, using the NQF standards. Before assessing the quality of each decision aid, we tested an NQF quality assessment form on five decision aids. We subsequently added specificity to the NQF quality criteria for this review. At least two of three reviewers (CHS, KP, MAD) assessed the quality of all included patient decision aids. We calculated interrater reliability using both Cohen’s Kappa statistic for individual quality categories and Spearman’s correlations for overall scores.

Notably, one of the NQF items concerns plain language. We assessed plain language using average readability scores, generated via Readable.io. If readability scores were below seventh-grade level, we considered them plain language. When we could not assess readability using an average score, ie, in the case of video decision aids, the researchers made a qualitative judgment about the plain language criteria.

Statistical Analysis

Our primary outcome was the number and variety of decision aids available for seriously ill individuals near death facing choices about life-sustaining treatments. Secondary outcomes included the quality, actual availability, and use of the available decision aids. We used Stata 13 to synthesize our results. We also reported overall quality and use. We conducted subgroup analyses, including quality, availability, and use of decision aids by category.

Decision Aid Selection Process

We identified 608 links with information about potential decision aids from our Google search. The two raters had substantial interrater reliability according to Cohen’s Kappa statistic (K = 0.64).²⁰ We did not detect any possible decision aids with our app store searches. We identified 31 studies from our MEDLINE search with information about potential decision aids eligible for inclusion. We received 60 responses to our expert survey from the 187 administered (a 32% response rate).

Altogether, we identified 105 potential decision aids from these sources. We excluded 22/105 potential decision aids from our analysis because they were not publicly accessible, and we could not successfully obtain them from the developers. It remains unknown whether these tools would have qualified for inclusion in our review. We excluded 55/105 tools for not meeting one of the following criteria: 1) not being decision aids according to the NQF criteria 2) not concerning life-sustaining treatments 3) not being targeted at people with serious illness near death. A majority of decision aids for life-sustaining treatment decisions are intended for people who do not yet have an advanced serious illness or are not near death. There were 27 decision aids in our final review (Figure 1).

Characteristics of Included Decision Aids

Of the 27 decision aids we included in our review, 14 (52%) were tailored to seriously ill individuals with specific conditions. Eleven decision aids (41%) concerned specific life-sustaining treatments. Two decision aids concerned general treatment approaches, such as life-sustaining care versus palliative care (Table 1).

The decision aids were of variable length and approach. Some were text only, while others were image heavy. The mean length of decision aids was 19 pages, while the median length was 10 pages. Included decision aids offered interventions meant to return patients to health, as well as palliative interventions and comfort care.

Notably, most of the decision aids we included in our review (25 decision aids; 93%) were freely available online. Three (11%) were not. Seventeen (63%) decision aids were developed in the U.S., eight (30%) in Canada, two (7%) in Australia, and one (4%) in the Netherlands (in Dutch, translated using Google Translate). Additionally, there were 22 potentially eligible decision aids that we could not access to review and therefore could not include.

Quality of Included Decision Aids

The overall correlation of scores between the two reviewers was high (0.85). Agreement was high for both reviewers for all categories (balanced 90%, K = 0.0; outcome probabilities 86%, K = 0.7; publication date 93%, K = 0.8; update policy 93%, K = 0.7; funding sources 96%, K = 0.8), except the category concerning the rigor of the decision aid development process (66%, K = 0.2) and the evidence sources used (79%, K = 0.6) categories.

The quality of the decision aids was high in some categories. Of 27 decision aids, most presented options in a balanced way (24, 89%) and identified funding sources (23, 85%). They also reported publication dates most of the time (19, 70%). Readability of the included decision aids was mixed. The average readability grade level was 7.5, with a low score of 4.1 and a high score of 10.7. Eleven decision aids (41%) had readability levels less than seventh grade (Table 2). Thirteen had plain language, including video decision aids that we agreed used plain language.

The decision aids also had consistently low scores in some categories. Of 27, only 11 listed their evidence sources (41%), 11 reported a rigorous evidence-synthesis method (41%), six stated their competing interests (22%), and three offered an update policy (11%). There were no notable differences in the quality of the decision aids in each of the three category types (condition-specific, treatment-specific, general).

Use of Included and Excluded Decision Aids (exploratory)

We received 60 of 187 responses to our key informant survey. We asked every respondent if they were aware of any relevant decision aids. Of the 60 respondents, 45 (75%) said they were aware of decision aids, but only 38 (63%) offered the names of potential tools. Twenty-six respondents (43%) said they were aware of institutions that used the decision aids in routine and sustained care. Twenty-four respondents (40%) offered names of organizations, but most of the suggestions concerned decision aids that did not qualify for inclusion in our review or care that was not routine or sustained. In this preliminary use estimation, we found evidence for the use of only three decision aids or similar tools in routine care, two of which we included in our review.

We found many decision aids of varying quality for people with serious illnesses facing decisions about life-sustaining treatments. Most available decision aids are customized for people with particular diseases or conditions, like cancer or heart failure, with few generalized tools. This may make it difficult for practicing clinicians to find tools that are appropriate for their patients. It could also contribute to the gap between their availability and use in routine care, which is an essential but exploratory finding of this systematic environmental scan. Even if seriously ill people or those who cared for them wanted to obtain and use a decision aid independently, a large proportion of them are not publicly accessible.

Concerning the quality of decision aids, they were usually balanced and listed their funding sources, but other quality areas we often missing concerning their development, content, and disclosures. These deficiencies may affect the trustworthiness of decision aids, which may make practicing clinicians less likely to use them in hospital settings. Reporting of outcome probabilities was particularly weak. Reporting outcome probabilities in ways that people who are ill and their relatives can understand, especially during times of heightened emotion, is critically important. Therefore, it is a cause for concern that the available decision aids often neglect to use evidence-based techniques for conveying outcome information.

Our work built on Butler and colleagues’ “state of the science” review in 2014.¹³ Focusing specifically on proximal life-sustaining treatment decisions, we found many more decision aids by expanding our search beyond the peer-reviewed literature to include the Internet and experts.¹³ We also identified an important gap worthy of further exploration between the decision aids available and their usage in real-world clinical environments.

Our review confirms that implementation of decision aids in routine care is a continued challenge, especially for seriously ill people facing life-sustaining treatment decisions.⁵³ Why tools that are efficacious in controlled trial environments have failed to gain acceptance in real-world settings remains unanswered for this population.⁵⁴ For decision aids in general, researchers have reported barriers concerning clinician awareness, perception, and comfort, as well as usability issues.^55,56 Additionally, systems-level barriers exist, like culture and priorities, difficulty incorporating decision aids into the workflow, resistance from parties who favor other interventions, and the costs associated with implementation.⁵⁶ There may also be particular barriers related to the topics of death and dying.A strength of this work is thatwe applied the rigor of the systematic review method to the environmental scan, a newer method that answers different questions, such as “How many?”, “How much?”, and “How often?” We hope our use of the word systematic will reinforce perception among the scientific community that the environmental scan method is thorough, valid and worthwhile. We believe this method unearthed more decision aids than a traditional systematic review limited to the academic literature would have revealed. Another strength of our review was the rigor of screening and assessment.

A limitation of our work is the challenge of defining serious illness. We worked with palliative care physicians to make these judgments as grounded in clinical practice as possible. The preliminary nature and selection of experts for our sustained—use survey are limitations as well. Despite our efforts to conduct a comprehensive review of a vast environment of tools, we may have missed some decision aids that met our inclusion criteria. An additional limitation of our work is that due to the exploratory nature of our sustained-use survey, we cannot determine with accuracy how often these tools are used, although we have provided the first preliminary assessment of use, to our knowledge.

The gap between prolific patient decision aid development and real-world usage is puzzling. It is possible that using a tool at all is inappropriate for the complex, emotionally-laden decision-making process associated with death and dying. Alternatively, the tools may be inappropriate for serious illness, due to their design, their content, or some other characteristics. Perhaps the existing tools are too tailored for specific conditions and interventions―less appropriate for generalized use. Indeed, only two decision aids included in our final review addressed general care pathways, like life-sustaining care, palliative care, and hospice care. The others were highly specific, concerning particular diseases like kidney disease and particular interventions, like CPR. We know that most people die with comultimorbidities, meaning such specificity may paradoxically make it more difficult for individuals and their families to identify with the content in the materials.^57,58 Without having data from real-world use, we cannot know whether any particular tool is suited or helpful for hospital practice.

It is essential for practicing hospitalists to know whether patient decision aids are appropriate for use in routine care. We hope that our review will help clinicians and health systems find appropriate tools to use with their patients. We also believe there should be mechanisms for providing feedback on whether decision aids are feasible and acceptable to hospitalized people and their caregivers and to practicing hospitalists and what leads to their sustained implementation.^55,56 This can be explored with on-the-ground observational research or through health system quality improvement efforts.

Acknowledgments

Pamela J. Bagley provided search strategy support. Meredith MacMartin provided clinical counsel. Amber Barnato provided comments and insight as an advisor and a new member of Catherine’s Ph.D. committee.

Author contributions

Catherine H. Saunders designed the study, with support from Marie-Anne Durand, Glyn Elwyn, and Kathryn Kirkland. Catherine H. Saunders conducted all screening, with support from Marie-Anne Durand. Khusbu Patel managed the inventory of decision aids. Catherine H. Saunders designed and distributed the key informant survey, with support from Marie-Anne Durand. Hyunkyung Kang and Catherine H. Saunders managed follow-up with key informants. Khusbu Patel and Catherine H. Saunders conducted the decision aid quality review. Catherine H. Saunders, Marie-Anne Durand, and Kathryn Kirkland screened decision aids to determine appropriateness for people with serious illness. Catherine H. Saunders drafted the manuscript, and all authors reviewed and approved it.

Ethical approval

The Dartmouth College Committee for the Protection of Human Subjects designated this project as exempt from further review. All survey participants confirmed their consent via an online form.

Disclosures

Ms. Saunders, Ms. Patel, Ms. Kang, and Dr. Kirkland have nothing to disclose. Dr. Elwyn reports personal fees from ACCESS Community Health Network, personal fees from EBSCO Health, personal fees from Chicago (Federally Qualified Medical Centers), outside the submitted work, and as Director of &think LLC, which owns the registered trademark for OptionGrids(TM) patient decision aids. He owns copyright in measures of shared decision making and care integration, namely collaboRATE, integRATE, Observer OPTION-5, and Observer OPTION-12, which are freely available for use. He is codeveloper of the OptionGrid patient decision aids, which are licensed to EBSCO Health. He has received reimbursement for travel, accommodations, and expenses from EBSCO Health, ACCESS Community Health Network, and Chicago (Federally Qualified Medical Centers). Dr. Durand reports personal fees from ACCESS Community Health Network, personal fees from EBSCO Health, outside the submitted work, and as codeveloper of the OptionGrid patient decision aids, which are licensed to EBSCO Health. She has received reimbursement for travel, accommodations, and expenses from EBSCO Health and ACCESS Community Health Network.

Financial conflicts of interest

Glyn Elwyn (GE) and Marie-Anne Durand (M-A D) have developed the Option Grid patient decision aids, which are licensed to EBSCO Health. They receive consulting income from EBSCO Health and may receive royalties in the future. M-A D is a consultant for ACCESS Community Health Network. No other competing interests declared.

Funding

The authors did not receive funding for this research.

Published protocol linked here: https://www.ncbi.nlm.nih.gov/pubmed/28825182

Increasingly, metrics such as length of stay (LOS) and readmissions are being utilized in the United States to assess quality of healthcare because these factors may represent opportunities to reduce cost and improve healthcare delivery.^1-8 However, the relatively low rate of pediatric readmissions,⁹ coupled with limited data regarding recommended LOS or best practices to prevent readmissions in children, challenges the ability of hospitals to safely reduce LOS and readmission rates for children.^10–12

In adults, weekend admission is associated with prolonged LOS, increased readmission rates, and increased risk of mortality.^13-21 This association is referred to as the “weekend effect.” While the weekend effect has been examined in children, the results of these studies have been variable, with some studies supporting this association and others refuting it.^22-31 In contrast to patient demographic and clinical characteristics that are known to affect LOS and readmissions,³² the weekend effect represents a potentially modifiable aspect of a hospitalization that could be targeted to improve healthcare delivery.

With increasing national attention toward improving quality of care and reducing LOS and healthcare costs, more definitive evidence of the weekend effect is necessary to prioritize resource use at both the local and national levels. Therefore, we sought to determine the association of weekend admission and weekend discharge on LOS and 30-day readmissions, respectively, among a national cohort of children. We hypothesized that children admitted on the weekend would have longer LOS, whereas those discharged on the weekend would have higher readmission rates.

Study Design and Data Source

We conducted a multicenter, retrospective, cross-sectional study. Data were obtained from the Pediatric Health Information System (PHIS), an administrative and billing database of 46 free-standing tertiary care pediatric hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Patient data are de-identified within PHIS; however, encrypted patient identifiers allow individual patients to be followed across visits. This study was not considered human subjects research by the policies of the Cincinnati Children’s Hospital Institutional Review Board.

Participants

We included hospitalizations to a PHIS-participating hospital for children aged 0-17 years between October 1, 2014 and September 30, 2015. We excluded children who were transferred from/to another institution, left against medical advice, or died in the hospital because these indications may result in incomplete LOS information and would not consistently contribute to readmission rates. We also excluded birth hospitalizations and children admitted for planned procedures. Birth hospitalizations were defined as hospitalizations that began on the day of birth. Planned procedures were identified using methodology previously described by Berry et al.⁹ With the use of this methodology, a planned procedure was identified if the coded primary procedure was one in which >80% of cases (eg, spinal fusion) are scheduled in advance. Finally, we excluded data from three hospitals due to incomplete data (eg, no admission or discharge time recorded).

Main Exposures

No standard definition of weekend admission or discharge was identified in the literature.³³ Thus, we defined a weekend admission as an admission between 3:00 pm Friday and 2:59 pm Sunday and a weekend discharge as a discharge between 3:00 pm Friday and 11:59 pm Sunday. These times were chosen by group consensus to account for the potential differences in hospital care during weekend hours (eg, decreased levels of provider staffing, access to ancillary services). To allow for a full 30-day readmission window, we defined an index admission as a hospitalization with no admission within the preceding 30 days. Individual children may contribute more than one index hospitalization to the dataset.

Main Outcomes

Our outcomes included LOS for weekend admission and 30-day readmissions for weekend discharge. LOS, measured in hours, was defined using the reported admission and discharge times. Readmissions were defined as a return to the same hospital within the subsequent 30 days following discharge.

Patient Demographics and Other Study Variables

Patient demographics included age, gender, race/ethnicity, payer, and median household income quartile based on the patient’s home ZIP code. Other study variables included presence of a complex chronic condition (CCC),³⁴ technology dependence,³⁴ number of chronic conditions of any complexity, admission through the emergency department, intensive care unit (ICU) admission, and case mix index. ICU admission and case mix index were chosen as markers for severity of illness. ICU admission was defined as any child who incurred ICU charges at any time following admission. Case mix index in PHIS is a relative weight assigned to each discharge based on the All-Patient Refined Diagnostic Group (APR-DRG; 3M) assignment and APR-DRG severity of illness, which ranges from 1 (minor) to 4 (extreme). The weights are derived by the Children’s Hospital Association from the HCUP KID 2012 database as the ratio of the average cost for discharges within a specific APR-DRG severity of illness combination to the average cost for all discharges in the database.

Statistical Analysis

Continuous variables were summarized with medians and interquartile ranges, while categorical variables were summarized with frequencies and percentages. Differences in admission and discharge characteristics between weekend and weekday were assessed using Wilcoxon rank sum tests for continuous variables and chi-square tests of association for categorical variables. We used generalized linear mixed modeling (GLMM) techniques to assess the impact of weekend admission on LOS and weekend discharge on readmission, adjusting for important patient demographic and clinical characteristics. Furthermore, we used GLMM point estimates to describe the variation across hospitals of the impact of weekday versus weekend care on LOS and readmissions. We assumed an underlying log-normal distribution for LOS and an underlying binomial distribution for 30-day readmission. All GLMMs included a random intercept for each hospital to account for patient clustering within a hospital. All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, North Carolina), and P values <.05 were considered statistically significant.

We identified 390,745 hospitalizations that met inclusion criteria (Supplementary Figure 1). The median LOS among our cohort was 41 hours (interquartile range [IQR] 24-71) and the median 30-day readmission rate was 8.2% (IQR 7.2-9.4).

Admission Demographics for Weekends and Weekdays

Among the included hospitalizations, 92,266 (23.6%) admissions occurred on a weekend (Supplementary Table 1). Overall, a higher percentage of children <5 years of age were admitted on a weekend compared with those admitted on a weekday (53.3% vs 49.1%, P < .001). We observed a small but statistically significant difference in the proportion of weekend versus weekday admissions according to gender, race/ethnicity, payer, and median household income quartile. Children with medical complexity and those with technology dependence were admitted less frequently on a weekend. A higher proportion of children were admitted through the emergency department on a weekend and a higher frequency of ICU utilization was observed for children admitted on a weekend compared with those admitted on a weekday.

Association Between Study Variables and Length of Stay

In comparing adjusted LOS for weekend versus weekday admissions across 43 hospitals, not only did LOS vary across hospitals (P < .001), but the association between LOS and weekend versus weekday care also varied across hospitals (P < .001) (Figure 1). Weekend admission was associated with a significantly longer LOS at eight (18.6%) hospitals and a significantly shorter LOS at four (9.3%) hospitals with nonstatistically significant differences at the remaining hospitals.

In adjusted analyses, we observed that infants ≤30 days of age, on average, had an adjusted LOS that was 24% longer than that of 15- to 17-year-olds, while children aged 1-14 years had an adjusted LOS that was 6%-18% shorter (Table 1). ICU utilization, admission through the emergency department, and number of chronic conditions had the greatest association with LOS. As the number of chronic conditions increased, the LOS increased. No association was found between weekend versus weekday admission and LOS (adjusted LOS [95% CI]: weekend 63.70 [61.01-66.52] hours versus weekday 63.40 [60.73-66.19] hours, P = .112).

Discharge Demographics for Weekends and Weekdays

Of the included hospitalizations, 127,421 (32.6%) discharges occurred on a weekend (Supplementary Table 2). Overall, a greater percentage of weekend discharges comprised children <5 years of age compared with the percentage of weekday discharges for children <5 years of age (51.5% vs 49.5%, P < .001). No statistically significant differences were found in gender, payer, or median household income quartile between those children discharged on a weekend versus those discharged on a weekday. We found small, statistically significant differences in the proportion of weekend versus weekday discharges according to race/ethnicity, with fewer non-Hispanic white children being discharged on the weekend versus weekday. Children with medical complexity, technology dependence, and patients with ICU utilization were less frequently discharged on a weekend compared with those discharged on a weekday.

Association Between Study Variables and Readmissions

In comparing the adjusted odds of readmissions for weekend versus weekday discharges across 43 PHIS hospitals, we observed significant variation (P < .001) in readmission rates from hospital to hospital (Figure 2). However, the direction of impact of weekend care on readmissions was similar (P = .314) across hospitals (ie, for 37 of 43 hospitals, the readmission rate was greater for weekend discharges compared with that for weekday discharges). For 17 (39.5%) of 43 hospitals, weekend discharge was associated with a significantly higher readmission rate, while the differences between weekday and weekend discharge were not statistically significant for the remaining hospitals.

In adjusted analyses, we observed that infants <1 year were more likely to be readmitted compared with 15- to 17-year-olds, while children 5-14 years of age were less likely to be readmitted (Table 2). Medical complexity and the number of chronic conditions had the greatest association with readmissions, with increased likelihood of readmission observed as the number of chronic conditions increased. Weekend discharge was associated with increased probability of readmission compared with weekday discharge (adjusted probability of readmission [95% CI]: weekend 0.13 [0.12-0.13] vs weekday 0.11 [0.11-0.12], P < .001).

In this multicenter retrospective study, we observed substantial variation across hospitals in the relationship between weekend admission and LOS and weekend discharge and readmission rates. Overall, we did not observe an association between weekend admission and LOS. However, significant associations were noted between weekend admission and LOS at some hospitals, although the magnitude and direction of the effect varied. We observed a modestly increased risk of readmission among those discharged on the weekend. At the hospital level, the association between weekend discharge and increased readmissions was statistically significant at 39.5% of hospitals. Not surprisingly, certain patient demographic and clinical characteristics, including medical complexity and number of chronic conditions, were also associated with LOS and readmission risk. Taken together, our findings demonstrate that among a large sample of children, the degree to which a weekend admission or discharge impacts LOS or readmission risk varies considerably according to specific patient characteristics and individual hospital.

While the reasons for the weekend effect are unclear, data supporting this difference have been observed across many diverse patient groups and health systems both nationally and internationally.^13-27,31 Weekend care is thought to differ from weekday care because of differences in physician and nurse staffing, availability of ancillary services, access to diagnostic testing and therapeutic interventions, ability to arrange outpatient follow-up, and individual patient clinical factors, including acuity of illness. Few studies have assessed the effect of weekend discharges on patient or system outcomes. Among children within a single health system, readmission risk was associated with weekend admission but not with weekend discharge.²² This observation suggests that if differential care exists, then it occurs during initial clinical management rather than during discharge planning. Consequently, understanding the interaction of weekend admission and LOS is important. In addition, the relative paucity of pediatric data examining a weekend discharge effect limits the ability to generalize these findings across other hospitals or health systems.

In contrast to prior work, we observed a modest increased risk for readmission among those discharged on the weekend in a large sample of children. Auger and Davis reported a lack of association between weekend discharge and readmissions at one tertiary care children’s hospital, citing reduced discharge volumes on the weekend, especially among children with medical complexity, as a possible driver for their observation.²² The inclusion of a much larger population across 43 hospitals in our study may explain our different findings compared with previous research. In addition, the inclusion/exclusion criteria differed between the two studies; we excluded index admissions for planned procedures in this study (which are more likely to occur during the week), which may have contributed to the differing conclusions. Although Auger and Davis suggest that differences in initial clinical management may be responsible for the weekend effect,²² our observations suggest that discharge planning practices may also contribute to readmission risk. For example, a family’s inability to access compounded medications at a local pharmacy or to access primary care following discharge could reasonably contribute to treatment failure and increased readmission risk. Attention to improving and standardizing discharge practices may alleviate differences in readmission risk among children discharged on a weekend.

Individual patient characteristics greatly influence LOS and readmission risk. Congruent with prior studies, medical complexity and technology dependence were among the factors in our study that had the strongest association with LOS and readmission risk.³² As with prior studies²², we observed that children with medical complexity and technology dependence were less frequently admitted and discharged on a weekend than on a weekday, which suggests that physicians may avoid complicated discharges on the weekend. Children with medical complexity present a unique challenge to physicians when assessing discharge readiness, given that these children frequently require careful coordination of durable medical equipment, obtainment of special medication preparations, and possibly the resumption or establishment of home health services. Notably, we cannot discern from our data what proportion of discharges may be delayed over the weekend secondary to challenges involved in coordinating care for children with medical complexity. Future investigations aimed at assessing physician decision making and discharge readiness in relation to discharge timing among children with medical complexity may establish this relationship more clearly.

We observed substantial variation in LOS and readmission risk across 43 tertiary care children’s hospitals. Since the 1970s, numerous studies have reported worse outcomes among patients admitted on the weekend. While the majority of studies support the weekend effect, several recent studies suggest that patients admitted on the weekend are at no greater risk of adverse outcomes than those admitted during the week.^35-37 Our work builds on the existing literature, demonstrating a complex and variable relationship between weekend admission/discharge, LOS, and readmission risk across hospitals. Notably, while many hospitals in our study experienced a significant weekend effect in LOS or readmission risk, only four hospitals experienced a statistically significant weekend effect for both LOS and readmission risk (three hospitals experienced increased risk for both, while one hospital experienced increased readmission risk but decreased LOS). Future investigations of the weekend effect should focus on exploring the differences in admission/discharge practices and staffing patterns of hospitals that did or did not experience a weekend effect.

This study has several limitations. We may have underestimated the total number of readmissions because we are unable to capture readmissions to other institutions by using the PHIS database. Our definition of a weekend admission or discharge did not account for three-day weekends or other holidays where staffing issues would be expected to be similar to that on weekends; consequently, our approach would be expected to bias the results toward null. Thus, a possible (but unlikely) result is that our approach masked a weekend effect that might have been more prominent had holidays been included. Although prior studies suggest that low physician/nurse staffing volumes and high patient workload are associated with worse patient outcomes,^38,39 we are unable to discern the role of differential staffing patterns, patient workload, or service availability in our observations using the PHIS database. Moreover, the PHIS database does not allow for any assessment of the preventability of a readmission or the impact of patient/family preference on the decision to admit or discharge, factors that could reasonably contribute to some of the observed variation. Finally, the PHIS database contains administrative data only, thus limiting our ability to fully adjust for patient severity of illness and sociodemographic factors that may have affected clinical decision making, including discharge decision making.

In a study of 43 children’s hospitals, children discharged on the weekend had a slightly increased readmission risk compared with children discharged on a weekday. Wide variation in the weekend effect on LOS and readmission risk was evident across hospitals. Individual patient characteristics had a greater impact on LOS and readmission risk than the weekend effect. Future investigations aimed at understanding which factors contribute most strongly to a weekend effect within individual hospitals (eg, differences in institutional admission/discharge practices) may help alleviate the weekend effect and improve healthcare quality.

Acknowledgments

This manuscript resulted from “Paper in a Day,” a Pediatric Research in Inpatient Settings (PRIS) Network-sponsored workshop presented at the Pediatric Hospital Medicine 2017 annual meeting. Workshop participants learned how to ask and answer a health services research question and efficiently prepare a manuscript for publication. The following are the members of the PRIS Network who contributed to this work: Jessica L. Bettenhausen, MD; Rebecca M. Cantu, MD, MPH; Jillian M Cotter, MD; Megan Deisz, MD; Teresa Frazer, MD; Pratichi Goenka, MD; Ashley Jenkins, MD; Kathryn E. Kyler, MD; Janet T. Lau, MD; Brian E. Lee, MD; Christiane Lenzen, MD; Trisha Marshall, MD; John M. Morrison MD, PhD; Lauren Nassetta, MD; Raymond Parlar-Chun, MD; Sonya Tang Girdwood MD, PhD; Tony R Tarchichi, MD; Irina G. Trifonova, MD; Jacqueline M. Walker, MD, MHPE; and Susan C. Walley, MD. See appendix for contact information for members of the PRIS Network.

Funding

The authors have no financial relationships relevant to this article to disclose.

Disclosures

The authors have no conflicts of interest to disclose.

Increasingly, metrics such as length of stay (LOS) and readmissions are being utilized in the United States to assess quality of healthcare because these factors may represent opportunities to reduce cost and improve healthcare delivery.^1-8 However, the relatively low rate of pediatric readmissions,⁹ coupled with limited data regarding recommended LOS or best practices to prevent readmissions in children, challenges the ability of hospitals to safely reduce LOS and readmission rates for children.^10–12

In adults, weekend admission is associated with prolonged LOS, increased readmission rates, and increased risk of mortality.^13-21 This association is referred to as the “weekend effect.” While the weekend effect has been examined in children, the results of these studies have been variable, with some studies supporting this association and others refuting it.^22-31 In contrast to patient demographic and clinical characteristics that are known to affect LOS and readmissions,³² the weekend effect represents a potentially modifiable aspect of a hospitalization that could be targeted to improve healthcare delivery.

With increasing national attention toward improving quality of care and reducing LOS and healthcare costs, more definitive evidence of the weekend effect is necessary to prioritize resource use at both the local and national levels. Therefore, we sought to determine the association of weekend admission and weekend discharge on LOS and 30-day readmissions, respectively, among a national cohort of children. We hypothesized that children admitted on the weekend would have longer LOS, whereas those discharged on the weekend would have higher readmission rates.

Study Design and Data Source

We conducted a multicenter, retrospective, cross-sectional study. Data were obtained from the Pediatric Health Information System (PHIS), an administrative and billing database of 46 free-standing tertiary care pediatric hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Patient data are de-identified within PHIS; however, encrypted patient identifiers allow individual patients to be followed across visits. This study was not considered human subjects research by the policies of the Cincinnati Children’s Hospital Institutional Review Board.

Participants

We included hospitalizations to a PHIS-participating hospital for children aged 0-17 years between October 1, 2014 and September 30, 2015. We excluded children who were transferred from/to another institution, left against medical advice, or died in the hospital because these indications may result in incomplete LOS information and would not consistently contribute to readmission rates. We also excluded birth hospitalizations and children admitted for planned procedures. Birth hospitalizations were defined as hospitalizations that began on the day of birth. Planned procedures were identified using methodology previously described by Berry et al.⁹ With the use of this methodology, a planned procedure was identified if the coded primary procedure was one in which >80% of cases (eg, spinal fusion) are scheduled in advance. Finally, we excluded data from three hospitals due to incomplete data (eg, no admission or discharge time recorded).

Main Exposures

No standard definition of weekend admission or discharge was identified in the literature.³³ Thus, we defined a weekend admission as an admission between 3:00 pm Friday and 2:59 pm Sunday and a weekend discharge as a discharge between 3:00 pm Friday and 11:59 pm Sunday. These times were chosen by group consensus to account for the potential differences in hospital care during weekend hours (eg, decreased levels of provider staffing, access to ancillary services). To allow for a full 30-day readmission window, we defined an index admission as a hospitalization with no admission within the preceding 30 days. Individual children may contribute more than one index hospitalization to the dataset.

Main Outcomes

Our outcomes included LOS for weekend admission and 30-day readmissions for weekend discharge. LOS, measured in hours, was defined using the reported admission and discharge times. Readmissions were defined as a return to the same hospital within the subsequent 30 days following discharge.

Patient Demographics and Other Study Variables

Patient demographics included age, gender, race/ethnicity, payer, and median household income quartile based on the patient’s home ZIP code. Other study variables included presence of a complex chronic condition (CCC),³⁴ technology dependence,³⁴ number of chronic conditions of any complexity, admission through the emergency department, intensive care unit (ICU) admission, and case mix index. ICU admission and case mix index were chosen as markers for severity of illness. ICU admission was defined as any child who incurred ICU charges at any time following admission. Case mix index in PHIS is a relative weight assigned to each discharge based on the All-Patient Refined Diagnostic Group (APR-DRG; 3M) assignment and APR-DRG severity of illness, which ranges from 1 (minor) to 4 (extreme). The weights are derived by the Children’s Hospital Association from the HCUP KID 2012 database as the ratio of the average cost for discharges within a specific APR-DRG severity of illness combination to the average cost for all discharges in the database.

Statistical Analysis

Continuous variables were summarized with medians and interquartile ranges, while categorical variables were summarized with frequencies and percentages. Differences in admission and discharge characteristics between weekend and weekday were assessed using Wilcoxon rank sum tests for continuous variables and chi-square tests of association for categorical variables. We used generalized linear mixed modeling (GLMM) techniques to assess the impact of weekend admission on LOS and weekend discharge on readmission, adjusting for important patient demographic and clinical characteristics. Furthermore, we used GLMM point estimates to describe the variation across hospitals of the impact of weekday versus weekend care on LOS and readmissions. We assumed an underlying log-normal distribution for LOS and an underlying binomial distribution for 30-day readmission. All GLMMs included a random intercept for each hospital to account for patient clustering within a hospital. All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, North Carolina), and P values <.05 were considered statistically significant.

We identified 390,745 hospitalizations that met inclusion criteria (Supplementary Figure 1). The median LOS among our cohort was 41 hours (interquartile range [IQR] 24-71) and the median 30-day readmission rate was 8.2% (IQR 7.2-9.4).

Admission Demographics for Weekends and Weekdays

Among the included hospitalizations, 92,266 (23.6%) admissions occurred on a weekend (Supplementary Table 1). Overall, a higher percentage of children <5 years of age were admitted on a weekend compared with those admitted on a weekday (53.3% vs 49.1%, P < .001). We observed a small but statistically significant difference in the proportion of weekend versus weekday admissions according to gender, race/ethnicity, payer, and median household income quartile. Children with medical complexity and those with technology dependence were admitted less frequently on a weekend. A higher proportion of children were admitted through the emergency department on a weekend and a higher frequency of ICU utilization was observed for children admitted on a weekend compared with those admitted on a weekday.

Association Between Study Variables and Length of Stay

In comparing adjusted LOS for weekend versus weekday admissions across 43 hospitals, not only did LOS vary across hospitals (P < .001), but the association between LOS and weekend versus weekday care also varied across hospitals (P < .001) (Figure 1). Weekend admission was associated with a significantly longer LOS at eight (18.6%) hospitals and a significantly shorter LOS at four (9.3%) hospitals with nonstatistically significant differences at the remaining hospitals.

In adjusted analyses, we observed that infants ≤30 days of age, on average, had an adjusted LOS that was 24% longer than that of 15- to 17-year-olds, while children aged 1-14 years had an adjusted LOS that was 6%-18% shorter (Table 1). ICU utilization, admission through the emergency department, and number of chronic conditions had the greatest association with LOS. As the number of chronic conditions increased, the LOS increased. No association was found between weekend versus weekday admission and LOS (adjusted LOS [95% CI]: weekend 63.70 [61.01-66.52] hours versus weekday 63.40 [60.73-66.19] hours, P = .112).

Discharge Demographics for Weekends and Weekdays

Of the included hospitalizations, 127,421 (32.6%) discharges occurred on a weekend (Supplementary Table 2). Overall, a greater percentage of weekend discharges comprised children <5 years of age compared with the percentage of weekday discharges for children <5 years of age (51.5% vs 49.5%, P < .001). No statistically significant differences were found in gender, payer, or median household income quartile between those children discharged on a weekend versus those discharged on a weekday. We found small, statistically significant differences in the proportion of weekend versus weekday discharges according to race/ethnicity, with fewer non-Hispanic white children being discharged on the weekend versus weekday. Children with medical complexity, technology dependence, and patients with ICU utilization were less frequently discharged on a weekend compared with those discharged on a weekday.

Association Between Study Variables and Readmissions

In comparing the adjusted odds of readmissions for weekend versus weekday discharges across 43 PHIS hospitals, we observed significant variation (P < .001) in readmission rates from hospital to hospital (Figure 2). However, the direction of impact of weekend care on readmissions was similar (P = .314) across hospitals (ie, for 37 of 43 hospitals, the readmission rate was greater for weekend discharges compared with that for weekday discharges). For 17 (39.5%) of 43 hospitals, weekend discharge was associated with a significantly higher readmission rate, while the differences between weekday and weekend discharge were not statistically significant for the remaining hospitals.

In adjusted analyses, we observed that infants <1 year were more likely to be readmitted compared with 15- to 17-year-olds, while children 5-14 years of age were less likely to be readmitted (Table 2). Medical complexity and the number of chronic conditions had the greatest association with readmissions, with increased likelihood of readmission observed as the number of chronic conditions increased. Weekend discharge was associated with increased probability of readmission compared with weekday discharge (adjusted probability of readmission [95% CI]: weekend 0.13 [0.12-0.13] vs weekday 0.11 [0.11-0.12], P < .001).

In this multicenter retrospective study, we observed substantial variation across hospitals in the relationship between weekend admission and LOS and weekend discharge and readmission rates. Overall, we did not observe an association between weekend admission and LOS. However, significant associations were noted between weekend admission and LOS at some hospitals, although the magnitude and direction of the effect varied. We observed a modestly increased risk of readmission among those discharged on the weekend. At the hospital level, the association between weekend discharge and increased readmissions was statistically significant at 39.5% of hospitals. Not surprisingly, certain patient demographic and clinical characteristics, including medical complexity and number of chronic conditions, were also associated with LOS and readmission risk. Taken together, our findings demonstrate that among a large sample of children, the degree to which a weekend admission or discharge impacts LOS or readmission risk varies considerably according to specific patient characteristics and individual hospital.

While the reasons for the weekend effect are unclear, data supporting this difference have been observed across many diverse patient groups and health systems both nationally and internationally.^13-27,31 Weekend care is thought to differ from weekday care because of differences in physician and nurse staffing, availability of ancillary services, access to diagnostic testing and therapeutic interventions, ability to arrange outpatient follow-up, and individual patient clinical factors, including acuity of illness. Few studies have assessed the effect of weekend discharges on patient or system outcomes. Among children within a single health system, readmission risk was associated with weekend admission but not with weekend discharge.²² This observation suggests that if differential care exists, then it occurs during initial clinical management rather than during discharge planning. Consequently, understanding the interaction of weekend admission and LOS is important. In addition, the relative paucity of pediatric data examining a weekend discharge effect limits the ability to generalize these findings across other hospitals or health systems.

In contrast to prior work, we observed a modest increased risk for readmission among those discharged on the weekend in a large sample of children. Auger and Davis reported a lack of association between weekend discharge and readmissions at one tertiary care children’s hospital, citing reduced discharge volumes on the weekend, especially among children with medical complexity, as a possible driver for their observation.²² The inclusion of a much larger population across 43 hospitals in our study may explain our different findings compared with previous research. In addition, the inclusion/exclusion criteria differed between the two studies; we excluded index admissions for planned procedures in this study (which are more likely to occur during the week), which may have contributed to the differing conclusions. Although Auger and Davis suggest that differences in initial clinical management may be responsible for the weekend effect,²² our observations suggest that discharge planning practices may also contribute to readmission risk. For example, a family’s inability to access compounded medications at a local pharmacy or to access primary care following discharge could reasonably contribute to treatment failure and increased readmission risk. Attention to improving and standardizing discharge practices may alleviate differences in readmission risk among children discharged on a weekend.

Individual patient characteristics greatly influence LOS and readmission risk. Congruent with prior studies, medical complexity and technology dependence were among the factors in our study that had the strongest association with LOS and readmission risk.³² As with prior studies²², we observed that children with medical complexity and technology dependence were less frequently admitted and discharged on a weekend than on a weekday, which suggests that physicians may avoid complicated discharges on the weekend. Children with medical complexity present a unique challenge to physicians when assessing discharge readiness, given that these children frequently require careful coordination of durable medical equipment, obtainment of special medication preparations, and possibly the resumption or establishment of home health services. Notably, we cannot discern from our data what proportion of discharges may be delayed over the weekend secondary to challenges involved in coordinating care for children with medical complexity. Future investigations aimed at assessing physician decision making and discharge readiness in relation to discharge timing among children with medical complexity may establish this relationship more clearly.

We observed substantial variation in LOS and readmission risk across 43 tertiary care children’s hospitals. Since the 1970s, numerous studies have reported worse outcomes among patients admitted on the weekend. While the majority of studies support the weekend effect, several recent studies suggest that patients admitted on the weekend are at no greater risk of adverse outcomes than those admitted during the week.^35-37 Our work builds on the existing literature, demonstrating a complex and variable relationship between weekend admission/discharge, LOS, and readmission risk across hospitals. Notably, while many hospitals in our study experienced a significant weekend effect in LOS or readmission risk, only four hospitals experienced a statistically significant weekend effect for both LOS and readmission risk (three hospitals experienced increased risk for both, while one hospital experienced increased readmission risk but decreased LOS). Future investigations of the weekend effect should focus on exploring the differences in admission/discharge practices and staffing patterns of hospitals that did or did not experience a weekend effect.

This study has several limitations. We may have underestimated the total number of readmissions because we are unable to capture readmissions to other institutions by using the PHIS database. Our definition of a weekend admission or discharge did not account for three-day weekends or other holidays where staffing issues would be expected to be similar to that on weekends; consequently, our approach would be expected to bias the results toward null. Thus, a possible (but unlikely) result is that our approach masked a weekend effect that might have been more prominent had holidays been included. Although prior studies suggest that low physician/nurse staffing volumes and high patient workload are associated with worse patient outcomes,^38,39 we are unable to discern the role of differential staffing patterns, patient workload, or service availability in our observations using the PHIS database. Moreover, the PHIS database does not allow for any assessment of the preventability of a readmission or the impact of patient/family preference on the decision to admit or discharge, factors that could reasonably contribute to some of the observed variation. Finally, the PHIS database contains administrative data only, thus limiting our ability to fully adjust for patient severity of illness and sociodemographic factors that may have affected clinical decision making, including discharge decision making.

In a study of 43 children’s hospitals, children discharged on the weekend had a slightly increased readmission risk compared with children discharged on a weekday. Wide variation in the weekend effect on LOS and readmission risk was evident across hospitals. Individual patient characteristics had a greater impact on LOS and readmission risk than the weekend effect. Future investigations aimed at understanding which factors contribute most strongly to a weekend effect within individual hospitals (eg, differences in institutional admission/discharge practices) may help alleviate the weekend effect and improve healthcare quality.

Acknowledgments

This manuscript resulted from “Paper in a Day,” a Pediatric Research in Inpatient Settings (PRIS) Network-sponsored workshop presented at the Pediatric Hospital Medicine 2017 annual meeting. Workshop participants learned how to ask and answer a health services research question and efficiently prepare a manuscript for publication. The following are the members of the PRIS Network who contributed to this work: Jessica L. Bettenhausen, MD; Rebecca M. Cantu, MD, MPH; Jillian M Cotter, MD; Megan Deisz, MD; Teresa Frazer, MD; Pratichi Goenka, MD; Ashley Jenkins, MD; Kathryn E. Kyler, MD; Janet T. Lau, MD; Brian E. Lee, MD; Christiane Lenzen, MD; Trisha Marshall, MD; John M. Morrison MD, PhD; Lauren Nassetta, MD; Raymond Parlar-Chun, MD; Sonya Tang Girdwood MD, PhD; Tony R Tarchichi, MD; Irina G. Trifonova, MD; Jacqueline M. Walker, MD, MHPE; and Susan C. Walley, MD. See appendix for contact information for members of the PRIS Network.

Funding

The authors have no financial relationships relevant to this article to disclose.

Disclosures

The authors have no conflicts of interest to disclose.

Increasingly, metrics such as length of stay (LOS) and readmissions are being utilized in the United States to assess quality of healthcare because these factors may represent opportunities to reduce cost and improve healthcare delivery.^1-8 However, the relatively low rate of pediatric readmissions,⁹ coupled with limited data regarding recommended LOS or best practices to prevent readmissions in children, challenges the ability of hospitals to safely reduce LOS and readmission rates for children.^10–12

In adults, weekend admission is associated with prolonged LOS, increased readmission rates, and increased risk of mortality.^13-21 This association is referred to as the “weekend effect.” While the weekend effect has been examined in children, the results of these studies have been variable, with some studies supporting this association and others refuting it.^22-31 In contrast to patient demographic and clinical characteristics that are known to affect LOS and readmissions,³² the weekend effect represents a potentially modifiable aspect of a hospitalization that could be targeted to improve healthcare delivery.

With increasing national attention toward improving quality of care and reducing LOS and healthcare costs, more definitive evidence of the weekend effect is necessary to prioritize resource use at both the local and national levels. Therefore, we sought to determine the association of weekend admission and weekend discharge on LOS and 30-day readmissions, respectively, among a national cohort of children. We hypothesized that children admitted on the weekend would have longer LOS, whereas those discharged on the weekend would have higher readmission rates.

Study Design and Data Source

We conducted a multicenter, retrospective, cross-sectional study. Data were obtained from the Pediatric Health Information System (PHIS), an administrative and billing database of 46 free-standing tertiary care pediatric hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Patient data are de-identified within PHIS; however, encrypted patient identifiers allow individual patients to be followed across visits. This study was not considered human subjects research by the policies of the Cincinnati Children’s Hospital Institutional Review Board.

Participants

We included hospitalizations to a PHIS-participating hospital for children aged 0-17 years between October 1, 2014 and September 30, 2015. We excluded children who were transferred from/to another institution, left against medical advice, or died in the hospital because these indications may result in incomplete LOS information and would not consistently contribute to readmission rates. We also excluded birth hospitalizations and children admitted for planned procedures. Birth hospitalizations were defined as hospitalizations that began on the day of birth. Planned procedures were identified using methodology previously described by Berry et al.⁹ With the use of this methodology, a planned procedure was identified if the coded primary procedure was one in which >80% of cases (eg, spinal fusion) are scheduled in advance. Finally, we excluded data from three hospitals due to incomplete data (eg, no admission or discharge time recorded).

Main Exposures

No standard definition of weekend admission or discharge was identified in the literature.³³ Thus, we defined a weekend admission as an admission between 3:00 pm Friday and 2:59 pm Sunday and a weekend discharge as a discharge between 3:00 pm Friday and 11:59 pm Sunday. These times were chosen by group consensus to account for the potential differences in hospital care during weekend hours (eg, decreased levels of provider staffing, access to ancillary services). To allow for a full 30-day readmission window, we defined an index admission as a hospitalization with no admission within the preceding 30 days. Individual children may contribute more than one index hospitalization to the dataset.

Main Outcomes

Our outcomes included LOS for weekend admission and 30-day readmissions for weekend discharge. LOS, measured in hours, was defined using the reported admission and discharge times. Readmissions were defined as a return to the same hospital within the subsequent 30 days following discharge.

Patient Demographics and Other Study Variables

Patient demographics included age, gender, race/ethnicity, payer, and median household income quartile based on the patient’s home ZIP code. Other study variables included presence of a complex chronic condition (CCC),³⁴ technology dependence,³⁴ number of chronic conditions of any complexity, admission through the emergency department, intensive care unit (ICU) admission, and case mix index. ICU admission and case mix index were chosen as markers for severity of illness. ICU admission was defined as any child who incurred ICU charges at any time following admission. Case mix index in PHIS is a relative weight assigned to each discharge based on the All-Patient Refined Diagnostic Group (APR-DRG; 3M) assignment and APR-DRG severity of illness, which ranges from 1 (minor) to 4 (extreme). The weights are derived by the Children’s Hospital Association from the HCUP KID 2012 database as the ratio of the average cost for discharges within a specific APR-DRG severity of illness combination to the average cost for all discharges in the database.

Statistical Analysis

Continuous variables were summarized with medians and interquartile ranges, while categorical variables were summarized with frequencies and percentages. Differences in admission and discharge characteristics between weekend and weekday were assessed using Wilcoxon rank sum tests for continuous variables and chi-square tests of association for categorical variables. We used generalized linear mixed modeling (GLMM) techniques to assess the impact of weekend admission on LOS and weekend discharge on readmission, adjusting for important patient demographic and clinical characteristics. Furthermore, we used GLMM point estimates to describe the variation across hospitals of the impact of weekday versus weekend care on LOS and readmissions. We assumed an underlying log-normal distribution for LOS and an underlying binomial distribution for 30-day readmission. All GLMMs included a random intercept for each hospital to account for patient clustering within a hospital. All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, North Carolina), and P values <.05 were considered statistically significant.

We identified 390,745 hospitalizations that met inclusion criteria (Supplementary Figure 1). The median LOS among our cohort was 41 hours (interquartile range [IQR] 24-71) and the median 30-day readmission rate was 8.2% (IQR 7.2-9.4).

Admission Demographics for Weekends and Weekdays

Among the included hospitalizations, 92,266 (23.6%) admissions occurred on a weekend (Supplementary Table 1). Overall, a higher percentage of children <5 years of age were admitted on a weekend compared with those admitted on a weekday (53.3% vs 49.1%, P < .001). We observed a small but statistically significant difference in the proportion of weekend versus weekday admissions according to gender, race/ethnicity, payer, and median household income quartile. Children with medical complexity and those with technology dependence were admitted less frequently on a weekend. A higher proportion of children were admitted through the emergency department on a weekend and a higher frequency of ICU utilization was observed for children admitted on a weekend compared with those admitted on a weekday.

Association Between Study Variables and Length of Stay

In comparing adjusted LOS for weekend versus weekday admissions across 43 hospitals, not only did LOS vary across hospitals (P < .001), but the association between LOS and weekend versus weekday care also varied across hospitals (P < .001) (Figure 1). Weekend admission was associated with a significantly longer LOS at eight (18.6%) hospitals and a significantly shorter LOS at four (9.3%) hospitals with nonstatistically significant differences at the remaining hospitals.

In adjusted analyses, we observed that infants ≤30 days of age, on average, had an adjusted LOS that was 24% longer than that of 15- to 17-year-olds, while children aged 1-14 years had an adjusted LOS that was 6%-18% shorter (Table 1). ICU utilization, admission through the emergency department, and number of chronic conditions had the greatest association with LOS. As the number of chronic conditions increased, the LOS increased. No association was found between weekend versus weekday admission and LOS (adjusted LOS [95% CI]: weekend 63.70 [61.01-66.52] hours versus weekday 63.40 [60.73-66.19] hours, P = .112).

Discharge Demographics for Weekends and Weekdays

Of the included hospitalizations, 127,421 (32.6%) discharges occurred on a weekend (Supplementary Table 2). Overall, a greater percentage of weekend discharges comprised children <5 years of age compared with the percentage of weekday discharges for children <5 years of age (51.5% vs 49.5%, P < .001). No statistically significant differences were found in gender, payer, or median household income quartile between those children discharged on a weekend versus those discharged on a weekday. We found small, statistically significant differences in the proportion of weekend versus weekday discharges according to race/ethnicity, with fewer non-Hispanic white children being discharged on the weekend versus weekday. Children with medical complexity, technology dependence, and patients with ICU utilization were less frequently discharged on a weekend compared with those discharged on a weekday.

Association Between Study Variables and Readmissions

In comparing the adjusted odds of readmissions for weekend versus weekday discharges across 43 PHIS hospitals, we observed significant variation (P < .001) in readmission rates from hospital to hospital (Figure 2). However, the direction of impact of weekend care on readmissions was similar (P = .314) across hospitals (ie, for 37 of 43 hospitals, the readmission rate was greater for weekend discharges compared with that for weekday discharges). For 17 (39.5%) of 43 hospitals, weekend discharge was associated with a significantly higher readmission rate, while the differences between weekday and weekend discharge were not statistically significant for the remaining hospitals.

In adjusted analyses, we observed that infants <1 year were more likely to be readmitted compared with 15- to 17-year-olds, while children 5-14 years of age were less likely to be readmitted (Table 2). Medical complexity and the number of chronic conditions had the greatest association with readmissions, with increased likelihood of readmission observed as the number of chronic conditions increased. Weekend discharge was associated with increased probability of readmission compared with weekday discharge (adjusted probability of readmission [95% CI]: weekend 0.13 [0.12-0.13] vs weekday 0.11 [0.11-0.12], P < .001).

In this multicenter retrospective study, we observed substantial variation across hospitals in the relationship between weekend admission and LOS and weekend discharge and readmission rates. Overall, we did not observe an association between weekend admission and LOS. However, significant associations were noted between weekend admission and LOS at some hospitals, although the magnitude and direction of the effect varied. We observed a modestly increased risk of readmission among those discharged on the weekend. At the hospital level, the association between weekend discharge and increased readmissions was statistically significant at 39.5% of hospitals. Not surprisingly, certain patient demographic and clinical characteristics, including medical complexity and number of chronic conditions, were also associated with LOS and readmission risk. Taken together, our findings demonstrate that among a large sample of children, the degree to which a weekend admission or discharge impacts LOS or readmission risk varies considerably according to specific patient characteristics and individual hospital.

While the reasons for the weekend effect are unclear, data supporting this difference have been observed across many diverse patient groups and health systems both nationally and internationally.^13-27,31 Weekend care is thought to differ from weekday care because of differences in physician and nurse staffing, availability of ancillary services, access to diagnostic testing and therapeutic interventions, ability to arrange outpatient follow-up, and individual patient clinical factors, including acuity of illness. Few studies have assessed the effect of weekend discharges on patient or system outcomes. Among children within a single health system, readmission risk was associated with weekend admission but not with weekend discharge.²² This observation suggests that if differential care exists, then it occurs during initial clinical management rather than during discharge planning. Consequently, understanding the interaction of weekend admission and LOS is important. In addition, the relative paucity of pediatric data examining a weekend discharge effect limits the ability to generalize these findings across other hospitals or health systems.

In contrast to prior work, we observed a modest increased risk for readmission among those discharged on the weekend in a large sample of children. Auger and Davis reported a lack of association between weekend discharge and readmissions at one tertiary care children’s hospital, citing reduced discharge volumes on the weekend, especially among children with medical complexity, as a possible driver for their observation.²² The inclusion of a much larger population across 43 hospitals in our study may explain our different findings compared with previous research. In addition, the inclusion/exclusion criteria differed between the two studies; we excluded index admissions for planned procedures in this study (which are more likely to occur during the week), which may have contributed to the differing conclusions. Although Auger and Davis suggest that differences in initial clinical management may be responsible for the weekend effect,²² our observations suggest that discharge planning practices may also contribute to readmission risk. For example, a family’s inability to access compounded medications at a local pharmacy or to access primary care following discharge could reasonably contribute to treatment failure and increased readmission risk. Attention to improving and standardizing discharge practices may alleviate differences in readmission risk among children discharged on a weekend.

Individual patient characteristics greatly influence LOS and readmission risk. Congruent with prior studies, medical complexity and technology dependence were among the factors in our study that had the strongest association with LOS and readmission risk.³² As with prior studies²², we observed that children with medical complexity and technology dependence were less frequently admitted and discharged on a weekend than on a weekday, which suggests that physicians may avoid complicated discharges on the weekend. Children with medical complexity present a unique challenge to physicians when assessing discharge readiness, given that these children frequently require careful coordination of durable medical equipment, obtainment of special medication preparations, and possibly the resumption or establishment of home health services. Notably, we cannot discern from our data what proportion of discharges may be delayed over the weekend secondary to challenges involved in coordinating care for children with medical complexity. Future investigations aimed at assessing physician decision making and discharge readiness in relation to discharge timing among children with medical complexity may establish this relationship more clearly.

We observed substantial variation in LOS and readmission risk across 43 tertiary care children’s hospitals. Since the 1970s, numerous studies have reported worse outcomes among patients admitted on the weekend. While the majority of studies support the weekend effect, several recent studies suggest that patients admitted on the weekend are at no greater risk of adverse outcomes than those admitted during the week.^35-37 Our work builds on the existing literature, demonstrating a complex and variable relationship between weekend admission/discharge, LOS, and readmission risk across hospitals. Notably, while many hospitals in our study experienced a significant weekend effect in LOS or readmission risk, only four hospitals experienced a statistically significant weekend effect for both LOS and readmission risk (three hospitals experienced increased risk for both, while one hospital experienced increased readmission risk but decreased LOS). Future investigations of the weekend effect should focus on exploring the differences in admission/discharge practices and staffing patterns of hospitals that did or did not experience a weekend effect.

This study has several limitations. We may have underestimated the total number of readmissions because we are unable to capture readmissions to other institutions by using the PHIS database. Our definition of a weekend admission or discharge did not account for three-day weekends or other holidays where staffing issues would be expected to be similar to that on weekends; consequently, our approach would be expected to bias the results toward null. Thus, a possible (but unlikely) result is that our approach masked a weekend effect that might have been more prominent had holidays been included. Although prior studies suggest that low physician/nurse staffing volumes and high patient workload are associated with worse patient outcomes,^38,39 we are unable to discern the role of differential staffing patterns, patient workload, or service availability in our observations using the PHIS database. Moreover, the PHIS database does not allow for any assessment of the preventability of a readmission or the impact of patient/family preference on the decision to admit or discharge, factors that could reasonably contribute to some of the observed variation. Finally, the PHIS database contains administrative data only, thus limiting our ability to fully adjust for patient severity of illness and sociodemographic factors that may have affected clinical decision making, including discharge decision making.

In a study of 43 children’s hospitals, children discharged on the weekend had a slightly increased readmission risk compared with children discharged on a weekday. Wide variation in the weekend effect on LOS and readmission risk was evident across hospitals. Individual patient characteristics had a greater impact on LOS and readmission risk than the weekend effect. Future investigations aimed at understanding which factors contribute most strongly to a weekend effect within individual hospitals (eg, differences in institutional admission/discharge practices) may help alleviate the weekend effect and improve healthcare quality.

Acknowledgments

This manuscript resulted from “Paper in a Day,” a Pediatric Research in Inpatient Settings (PRIS) Network-sponsored workshop presented at the Pediatric Hospital Medicine 2017 annual meeting. Workshop participants learned how to ask and answer a health services research question and efficiently prepare a manuscript for publication. The following are the members of the PRIS Network who contributed to this work: Jessica L. Bettenhausen, MD; Rebecca M. Cantu, MD, MPH; Jillian M Cotter, MD; Megan Deisz, MD; Teresa Frazer, MD; Pratichi Goenka, MD; Ashley Jenkins, MD; Kathryn E. Kyler, MD; Janet T. Lau, MD; Brian E. Lee, MD; Christiane Lenzen, MD; Trisha Marshall, MD; John M. Morrison MD, PhD; Lauren Nassetta, MD; Raymond Parlar-Chun, MD; Sonya Tang Girdwood MD, PhD; Tony R Tarchichi, MD; Irina G. Trifonova, MD; Jacqueline M. Walker, MD, MHPE; and Susan C. Walley, MD. See appendix for contact information for members of the PRIS Network.

Funding

The authors have no financial relationships relevant to this article to disclose.

Disclosures

The authors have no conflicts of interest to disclose.

BACKGROUND: Identifying hospitals that are both early and consistent adopters of high-value care can help shed light on the culture and practices at those institutions that are necessary to promote high-value care nationwide. The use of troponin testing to diagnose acute myocardial infarction (AMI), and not testing for myoglobin or creatine kinase-MB (CK-MB), is a high-value recommendation of the Choosing Wisely® campaign.

OBJECTIVE: To examine the variation in cardiac biomarker testing and the effect of the Choosing Wisely® troponin-only testing recommendation for the diagnosis of AMI.

DESIGN: A retrospective, observational study using administrative ordering data from Vizient’s Clinical Database/Resource Manager.

PATIENTS: Hospitalized patients with a principal discharge diagnosis of AMI.

INTERVENTION: The Choosing Wisely® recommendation to order troponin-only testing to diagnose AMI was released during the first quarter of 2015.

RESULTS: In 19 hospitals, troponin-only testing was consistently ordered to diagnose AMI before the Choosing Wisely® recommendation and throughout the study period. In 34 hospitals, both troponin testing and myoglobin/CK-MB testing were ordered to diagnose AMI even after the Choosing Wisely® recommendation. In 26 hospitals with low rates of troponin-only testing before the Choosing Wisely® recommendation, the release of the recommendation was associated with a statistically significant increase in the rate of troponin-only testing to diagnose AMI.

CONCLUSION: In institutions with low rates of troponin-only testing prior to the Choosing Wisely® recommendation, the recommendation was associated with a significant increase in the rate of troponin-only testing.

Read the entire article in the Dec. 2017 issue of the Journal of Hospital Medicine.

Also in JHM this month

Hospital perceptions of Medicare’s Sepsis Quality Reporting Initiative

AUTHORS: Ian J. Barbash, MD, MS; Kimberly J. Rak, PhD; Courtney C. Kuza, MPH; and Jeremy M. Kahn, MD, MS

Health literacy and hospital length of stay: An inpatient cohort study

AUTHORS: Ethan G. Jaffee, MD; Vineet M. Arora, MD, MAPP; Madeleine I. Matthiesen, MD; David O. Meltzer, MD, PhD, MHM; and Valerie G. Press, MD, FAAP, FACP, MPH

How exemplary teaching physicians interact with hospitalized patients

AUTHORS: Sanjay Saint, MD, MPH, FHM; Molly Harrod, PhD; Karen E. Fowler, MPH; and Nathan Houchens, MD, FACP, FHM

A randomized cohort controlled trial to compare intern sign-out training interventions

AUTHORS: Soo-Hoon Lee, PhD; Christopher Terndrup, MD; Phillip H. Phan, PhD; Sandra E. Zaeh, MD; Kwame Atsina, MD; Nicole Minkove, MD; Alexander Billioux, MD; DPhil, Souvik Chatterjee, MD; Idoreyin Montague, MD; Bennett Clark, MD; Andrew Hughes, MD; and Sanjay V. Desai, MD

BACKGROUND: Identifying hospitals that are both early and consistent adopters of high-value care can help shed light on the culture and practices at those institutions that are necessary to promote high-value care nationwide. The use of troponin testing to diagnose acute myocardial infarction (AMI), and not testing for myoglobin or creatine kinase-MB (CK-MB), is a high-value recommendation of the Choosing Wisely® campaign.

OBJECTIVE: To examine the variation in cardiac biomarker testing and the effect of the Choosing Wisely® troponin-only testing recommendation for the diagnosis of AMI.

DESIGN: A retrospective, observational study using administrative ordering data from Vizient’s Clinical Database/Resource Manager.

PATIENTS: Hospitalized patients with a principal discharge diagnosis of AMI.

INTERVENTION: The Choosing Wisely® recommendation to order troponin-only testing to diagnose AMI was released during the first quarter of 2015.

RESULTS: In 19 hospitals, troponin-only testing was consistently ordered to diagnose AMI before the Choosing Wisely® recommendation and throughout the study period. In 34 hospitals, both troponin testing and myoglobin/CK-MB testing were ordered to diagnose AMI even after the Choosing Wisely® recommendation. In 26 hospitals with low rates of troponin-only testing before the Choosing Wisely® recommendation, the release of the recommendation was associated with a statistically significant increase in the rate of troponin-only testing to diagnose AMI.

CONCLUSION: In institutions with low rates of troponin-only testing prior to the Choosing Wisely® recommendation, the recommendation was associated with a significant increase in the rate of troponin-only testing.

Read the entire article in the Dec. 2017 issue of the Journal of Hospital Medicine.

Also in JHM this month

Hospital perceptions of Medicare’s Sepsis Quality Reporting Initiative

AUTHORS: Ian J. Barbash, MD, MS; Kimberly J. Rak, PhD; Courtney C. Kuza, MPH; and Jeremy M. Kahn, MD, MS

Health literacy and hospital length of stay: An inpatient cohort study

AUTHORS: Ethan G. Jaffee, MD; Vineet M. Arora, MD, MAPP; Madeleine I. Matthiesen, MD; David O. Meltzer, MD, PhD, MHM; and Valerie G. Press, MD, FAAP, FACP, MPH

How exemplary teaching physicians interact with hospitalized patients

AUTHORS: Sanjay Saint, MD, MPH, FHM; Molly Harrod, PhD; Karen E. Fowler, MPH; and Nathan Houchens, MD, FACP, FHM

A randomized cohort controlled trial to compare intern sign-out training interventions

AUTHORS: Soo-Hoon Lee, PhD; Christopher Terndrup, MD; Phillip H. Phan, PhD; Sandra E. Zaeh, MD; Kwame Atsina, MD; Nicole Minkove, MD; Alexander Billioux, MD; DPhil, Souvik Chatterjee, MD; Idoreyin Montague, MD; Bennett Clark, MD; Andrew Hughes, MD; and Sanjay V. Desai, MD

BACKGROUND: Identifying hospitals that are both early and consistent adopters of high-value care can help shed light on the culture and practices at those institutions that are necessary to promote high-value care nationwide. The use of troponin testing to diagnose acute myocardial infarction (AMI), and not testing for myoglobin or creatine kinase-MB (CK-MB), is a high-value recommendation of the Choosing Wisely® campaign.

OBJECTIVE: To examine the variation in cardiac biomarker testing and the effect of the Choosing Wisely® troponin-only testing recommendation for the diagnosis of AMI.

DESIGN: A retrospective, observational study using administrative ordering data from Vizient’s Clinical Database/Resource Manager.

PATIENTS: Hospitalized patients with a principal discharge diagnosis of AMI.

INTERVENTION: The Choosing Wisely® recommendation to order troponin-only testing to diagnose AMI was released during the first quarter of 2015.

RESULTS: In 19 hospitals, troponin-only testing was consistently ordered to diagnose AMI before the Choosing Wisely® recommendation and throughout the study period. In 34 hospitals, both troponin testing and myoglobin/CK-MB testing were ordered to diagnose AMI even after the Choosing Wisely® recommendation. In 26 hospitals with low rates of troponin-only testing before the Choosing Wisely® recommendation, the release of the recommendation was associated with a statistically significant increase in the rate of troponin-only testing to diagnose AMI.

CONCLUSION: In institutions with low rates of troponin-only testing prior to the Choosing Wisely® recommendation, the recommendation was associated with a significant increase in the rate of troponin-only testing.

Read the entire article in the Dec. 2017 issue of the Journal of Hospital Medicine.

Also in JHM this month

Hospital perceptions of Medicare’s Sepsis Quality Reporting Initiative

AUTHORS: Ian J. Barbash, MD, MS; Kimberly J. Rak, PhD; Courtney C. Kuza, MPH; and Jeremy M. Kahn, MD, MS

Health literacy and hospital length of stay: An inpatient cohort study

AUTHORS: Ethan G. Jaffee, MD; Vineet M. Arora, MD, MAPP; Madeleine I. Matthiesen, MD; David O. Meltzer, MD, PhD, MHM; and Valerie G. Press, MD, FAAP, FACP, MPH

How exemplary teaching physicians interact with hospitalized patients

AUTHORS: Sanjay Saint, MD, MPH, FHM; Molly Harrod, PhD; Karen E. Fowler, MPH; and Nathan Houchens, MD, FACP, FHM

A randomized cohort controlled trial to compare intern sign-out training interventions

AUTHORS: Soo-Hoon Lee, PhD; Christopher Terndrup, MD; Phillip H. Phan, PhD; Sandra E. Zaeh, MD; Kwame Atsina, MD; Nicole Minkove, MD; Alexander Billioux, MD; DPhil, Souvik Chatterjee, MD; Idoreyin Montague, MD; Bennett Clark, MD; Andrew Hughes, MD; and Sanjay V. Desai, MD

Case Scenario

A 62-year-old man who regularly presented to the ED for exacerbations of chronic obstructive pulmonary disease (COPD) after running out of his medications presented again for evaluation and treatment. His outpatient care had been poorly coordinated, and he relied on the ED to provide him with the support he needed. This presentation represented his fifth visit to the ED over the past 3 months.

The patient’s medical history was positive for asthma since childhood, tobacco use, hypertension, and a recent diagnosis of congestive heart failure (CHF). Over the past year, he had four hospital admissions, and was currently unable to walk from his bedroom to another room without becoming short of breath. He also had recently experienced a 20-lb weight loss.

At this visit, the patient complained of chest pain and lightheadedness, which he described as suffocating. Prior to these recent symptoms, he enjoyed walking in his neighborhood and talking with friends. He was an avid reader and sports fan, but admitted that he now had trouble focusing on reading and following games on television. He lived alone, and his family lived across the country. The patient further admitted that although he had attempted to quit cigarette smoking, he was unable to give up his 50-pack per year habit. He had no completed advance health care directive and had significant challenges tending to his basic needs.

The Trajectory of COPD

Chronic obstructive pulmonary disease is a common chronic illness that causes significant morbidity and mortality. A 2016 National Health Services report cited respiratory illness, primarily from COPD, as the third leading cause of death in the United States in 2014.¹The trajectory of this disease is marked by frequent exacerbations with partial recovery to baseline function. The burden of those living with COPD is significant and marked by a poor overall health-related quality of life (QOL). The ED has become a staging area for patients seeking care for exacerbations of COPD.²

The World Health Organization (WHO) and the Global Initiative for Chronic Obstructive Lung Disease (GOLD) have defined COPD as a spectrum of diseases including emphysema, chronic bronchitis, and chronic obstructive asthma characterized by persistent airflow limitation that is usually progressive and associated with an enhanced chronic inflammatory response to noxious particles or gases in the airways and lungs.³ Exacerbations and comorbidities contribute to the overall severity of COPD in individual patients.⁴

The case presented in this article illustrates the common scenario of a patient whose COPD has become severe and highly symptomatic with declining function to the point where he requires home support. His physical decline had been rapid and resulted in many unmet needs. When a patient such as this presents for emergent care, he must first be stabilized; then a care plan will need to be developed prior to discharge.

Management Goals

The overall goals of treating COPD are based on preserving function and are not curative in nature. Chronic obstructive pulmonary disease is a progressive illness that will intensify over time.⁵ As such, palliative care services are warranted. However, many patients with COPD do not receive palliative care services compared to patients with such other serious and life-limiting disease as cancer and heart disease.

Acute Exacerbations of COPD

Incidence

The frequency of acute exacerbations of COPD (AECOPD) increases with age, productive cough, long-standing COPD, previous hospitalizations related to COPD, eosinophilia, and comorbidities (eg, CHF). Patients with moderate to severe COPD and a history of prior exacerbations were found to have a higher likelihood of future exacerbations. From a quality and cost perspective, it may be useful to identify high-risk patients and strengthen their outpatient program to lessen the need for ED care and more intensive support.^6,7

In our case scenario, the patient could have been stabilized at home with a well-controlled plan and home support, which would have resulted in an improved QOL and more time free from his high symptom burden.

Causes

Bacterial and viral respiratory infections are the most likely cause of AECOPD. Environmental pollution and pulmonary embolism are also triggers. Typically, patients with AECOPD present to the ED up to several times a year² and represent the third most common cause of 30-day readmissions to the hospital.⁸ Prior exacerbations, dyspnea, and other medical comorbidities are also risk factors for more frequent hospital visits.

Presenting Signs and Symptoms

Each occurrence of AECOPD represents a worsening of a patient’s respiratory symptoms beyond normal variations. This might include increases in cough, sputum production, and dyspnea. The goal in caring for a person with an AECOPD is to stabilize the acute event and provide a treatment plan. The range of acuity for moderate to severe disease makes devising an appropriate treatment plan challenging, and after implementing the best plans, the patient’s course may be characterized by a prolonged cycle of admissions and readmissions without substantial return to baseline.

Management

In practice, ED management of AECOPD in older adults typically differs significantly from published guideline recommendations,⁹ which may result in pooroutcomes related to shortcomings in quality of care. Better adherence to guideline recommendations when caring for elderly patients with COPD may lead to improved clinical outcomes and better resource usage.^6,9

Risk Stratification

Complicating ED management is the challenge of determining the severity of illness and degree of the exacerbation. Airflow obstruction alone is not sufficient to predict outcomes, as any particular measure of obstruction is associated with a spectrum of forced expiratory volume in the first second (FEV₁) and varying performance. Moreover, peak-flow measurements are not useful in the setting of AECOPD, as opposed to their use in acute asthma exacerbations, and are not predictive of changes in clinical status.

GOLD and NICE Criteria

Guidelines have been developed and widely promoted to assist ED and hospital and community clinicians in providing evidence-based management for COPD patients. The GOLD Criteria and the National Institute for Clinical Excellence (NICE) are both clinical guidelines on management of COPD.¹⁰

Although well recognized and commonly used, the original GOLD criteria did not take into account the frequency and importance of the extrapulmonary manifestations of COPD in predicting outcome. Typically, those with severe or very severe COPD have an average of 12 co-occurring symptoms, an even greater number of signs and symptoms than those occurring in patients with cancer or heart or renal disease.¹¹

BODE Criteria

The body mass index, airflow obstruction, dyspnea and exercise capacity (BODE) criteria assess and predict the health-related QOL and mortality risk for patients with COPD. Risk is adjusted based on four factors—weight, airway obstruction, dyspnea, and exercise capacity (ie, 6-minute walk distance).¹³

Initial Evaluation and Work-Up

As previously noted, when an AECOPD patient arrives to the ED, the first priority is to stabilize the patient and initiate treatment. In this respect, initial identification of the patient’s pulse oxygen saturation (SpO₂) is important.

Laboratory Evaluation

In cases of respiratory failure, obtaining arterial blood gas (ABG) values are critical. The ABG test will assist in determining acute exacerbations of chronic hypercapnia and the need for ventilatory support. When considering CHF, a plasma B-type natriuretic peptide is useful to assess for CHF.

Imaging Studies

A chest radiograph may be useful in the initial evaluation to identify abnormalities, including barotrauma (ie, pneumothorax) and infiltrates. Additionally, in patients with comorbidities, it is important to assess cardiac status, and a chest X-ray may assist in identification of pulmonary edema, pleural effusions, and cardiomegaly. If the radiograph does show a pulmonary infiltrate (ie, pneumonia), it will help identify the probable triggers, but even in these instances, a sputum gram stain will not assist in the diagnosis.

Treatment

Relieving airflow obstruction is achieved with inhaled short-acting bronchodilators and systemic glucocorticoids, by treating infection, and by providing supplemental oxygen and ventilatory support.

Bronchodilators

The short-acting beta-adrenergic agonists (eg, albuterol) act rapidly and are effective in producing bronchodilation. Nebulized therapy may be most comfortable for the acutely ill patient. Typical dosing is 2.5 mg albuterol diluted to 3 cc by nebulizer every hour. Higher doses are not more effective, and there is no evidence of a higher response rate from constant nebulized therapy, which can cause anxiety and tachycardia in patients.¹⁴ Anticholinergic agents (eg, ipratropium) are often added despite unclear data regarding clinical advantage. In one study evaluating the effectiveness of adding ipratropium to albuterol, patients receiving a combination had the same improvement in FEV₁ at 90 minutes.¹⁵ Patients receiving ipratropium alone had the lowest rate of reported side effects.¹⁵

Systemic Glucocorticoids

Short-course systemic glucocorticoids are an important addition to treatment and have been found to improve spirometry and decrease relapse rate. The oral and intravenous (IV) routes provide the same benefit. For the acutely ill patient with challenges swallowing, the IV route is preferred. The optimal dose is not clear, but hydrocortisone doses of 100 mg to 125 mg every 6 hours for 3 days are effective, as is oral prednisone 30 mg per day for 14 days, or 60 mg per day for 3 days with a taper.

Antibiotic Therapy

Antibiotics are indicated for patients with moderate to severe AECOPD who are ill enough to be admitted to the hospital. Empiric broad spectrum treatment is recommended. The initial antibiotic regimen should target likely bacterial pathogens (Haemophilus influenzae, Moraxella catarrhalis, and Streptococcus pneumoniae in most patients) and take into account local patterns of antibiotic resistance. Flouroquinolones or third-generation cephalosporins generally provide sufficient coverage. For patients experiencing only a mild exacerbation, antibiotics are not warranted.

Magnesium Sulfate

Other supplemental medications that have been evaluated include magnesium sulfate for bronchial smooth muscle relaxation. Studies have found that while magnesium is helpful in asthma, results are mixed with COPD.¹⁶

Supplemental Oxygen

Oxygen therapy is important during an AECOPD episode. Often, concerns arise about decreasing respiratory drive, which is typically driven by hypoxia in patients who have chronic hypercapnia. Arterial blood gas determinations are important in managing a patient’s respiratory status and will assist in determining actual oxygenation and any coexistent metabolic disturbances.

Noninvasive Ventilation. Oxygen can be administered efficiently by a venturi mask, which delivers precise fractions of oxygen, or by nasal cannula. A facemask is less comfortable, but is available for higher oxygen requirements, providing up to 55% oxygen, while a nonrebreather mask delivers up to 90% oxygen.

When necessary, noninvasive positive pressure ventilation (NPPV) improves outcomes for those with severe dyspnea and signs of respiratory fatigue manifested as increased work of breathing. Noninvasive positive pressure ventilation can improve clinical outcomes and is the ventilator mode of choice for those patients with COPD. Indications include severe dyspnea with signs of increased work of breathing and respiratory acidosis (arterial pH <7.35) and partial pressure of arterial carbon dioxide (PaCO₂) >45 mm Hg.

Whenever possible, NPPV should be initiated with a triggered mode to allow spontaneous breaths. Inspiratory pressure of 8 cm to 12 cm H₂O and expiratory pressure of 3 cm to 5 cm of H₂ are recommended.

Mechanical Ventilation. Mechanical ventilation is often undesirable because it may be extraordinarily difficult to wean a patient off the device and permit safe extubation. However, if a patient cannot be stabilized with NPPV, intubation and mechanical ventilation must be considered. Typically, this occurs when there is severe respiratory distress, tachypnea >30 breaths/min, accessory muscle use, and altered mentation.

Goals of intubation/mechanical respiration include correcting oxygenation and severe respiratory acidosis as well as reducing the work of breathing. Barotrauma is a significant risk when patients with COPD require mechanical ventilation. Volume-limited modes of ventilation are commonly used, while pressure support or pressure-limited modes are less suitable for patients with airflow limitation. Again, invasive ventilation should only be administered if a patient cannot tolerate NPPV.

Palliative Care in the ED

Palliative care is an approach that improves the QOL of patients and their families facing the issues associated with life-threatening illness, through the prevention and relief of suffering by means of early identification and accurate assessment and treatment of pain and physical, psychosocial, and spiritual problems.³ This approach to care is warranted for COPD patients given the myriad of burdensome symptoms and functional decline that occurs.¹⁷

Palliative care expands traditional treatment goals to include enhancing QOL; helping with medical decision making; and identifying the goals of care. Palliative care is provided by board-certified physicians for the most complex of cases. However, the primary practice of palliative care must be delivered at the bedside by the treating provider. Managing pain, dyspnea, nausea, vomiting, and changes in bowel habits, as well as discussing goals of care, are among the basic palliative care skills all providers need to have and apply when indicated.

Palliative Care for Dyspnea

Opioids. Primary palliative care in the ED includes the appropriate use of low-dose oral and parenteral opioids to treat dyspnea in AECOPD. The use of a low-dose opioid, such as morphine 2 mg IV, titrated up to a desired response, is a safe and effective practice.¹⁸ Note the 2-mg starting dose is considered low-dose.¹⁹

With respect to managing dyspnea in AECOPD patients, nebulized opioids have not been found to be better than nebulized saline. More specific data regarding the use of oral opioids for managing refractory dyspnea in patients with predominantly COPD have been recently published: Long-acting morphine 20 mg once daily provides symptomatic relief in refractory dyspnea in the community setting. For the opioid-naïve patient, a lower dose is recommended.²⁰

Oxygenation. There is no hard evidence of the effectiveness of oxygen in the palliation of breathlessness. Humidified air is effective initially, as is providing a fan at the bedside. Short-burst oxygen therapy should only be considered for episodes of severe breathlessness in patients whose COPD is not relieved by other treatments. Oxygen should continue to be prescribed only if an improvement in breathlessness following therapy has been documented. The American Thoracic Society recommends continuous oxygen therapy in patients with COPD who have severe resting hypoxemia (PaCO₂ ≤55 mm Hg or SpO₂ ≤88%).²¹

POLST Form

The Physicians Order for Life-Sustaining Treatment (POLST) form is a set of medical orders, similar to the “do not resuscitate” (allow natural death) order. A POLST form is not an advance directive and does not serve as a substitute for a patient’s assignation of a health care agent or durable power of attorney for health care.²²

The POLST form enables physicians to order treatments patients would want, identify those treatments that patients would not want, and not provide those the patient considers “extraordinary” and excessively burdensome. A POLST form does not allow for active euthanasia or physician-assisted suicide.

Identifying treatment preferences is an important part of the initial evaluation of all patients. When dealing with an airway issue in a COPD patient, management can become complex. Ideally, the POLST form should arrive with the patient in the ED and list preferences regarding possible intensive interventions such as intubation and chest compressions. Discussing these issues with a patient in extreme distress is difficult or impossible, and in these cases, access to pertinent medical records, discussing preferences with family caregivers, and availability of a POLST form are much better ways to determine therapy.

Palliative Home Care

Patient Safety Considerations

Weight loss and associated muscle wasting are common features in patients with severe COPD, creating a high-risk situation for falls and a need for assistance with activities of daily living. The patient who is frail when discharged home from the ED requires a home-care plan before leaving the ED, and strict follow-up with the patient’s primary care provider will typically be needed within 2 to 4 weeks.

Psychological Considerations

Being mindful of the anxiety and depression that accompany the physical limitations of those with COPD is important. Mood disturbances serve as risk factors for re-hospitalization and mortality.¹³Multiple palliative care interventions provide patients assistance with these issues, including the use of antidepressants that may aid sleep, stabilize mood, and stimulate appetite.

Early referral to the palliative care team will provide improved care for the patient and family. Palliative care referral will provide continued management of the physical symptoms and evaluation and treatment of the psychosocial issues that accompany COPD. Additionally, the palliative care team can assist with safe discharge planning and follow-up, including the provision of the patient’s home needs as well as the family’s ability to cope with the home setting.

Prognosis

Predicting prognosis is difficult for the COPD patient due to the highly variable illness trajectory. Some patients have a low FEV₁ and yet are very functional. However, assessment of severity of lung function impairment, frequency of exacerbations, and need for long-term oxygen therapy helps identify those patients who are entering the final 12 months of life. Evaluating symptom burden and impact on activities of daily living for patients with COPD is comparable to those of cancer patients, and in both cases, palliative care approaches are necessary.

Predicting Morbidity and Mortality

A profile developed from observational studies can help predict 6- to 12-month morbidity and mortality in patients with advanced COPD. This profile includes the following criteria:

• Significant dyspnea;
• FEV₁ <30%;
• Number of exacerbations;
• Left heart failure or other comorbidities;
• Weight loss or cachexia;
• Decreasing performance status;
• Age older than 70 years; and
• Depression.

Although additional research is required to refine and verify this profile, reviewing these data points can prompt providers to initiate discussions with patients about treatment preferences and end-of-life care.^23,24

Palliative Performance Scale

The Palliative Performance Scale (PPS) is another scale used to predict prognosis and eligibility for hospice care.²⁵ This score provides a patient’s estimated survival.²⁵ For a patient with a PPS score of 50%, hospice education may be appropriate.

Case Scenario Continued

Both the BODE and GOLD criteria scores assisted in determining prognosis and risk profiles of the patient in our case scenario. By applying the BODE criteria, our patient had a 4-year survival benefit of under 18%. The GOLD criteria results for this patient also were consistent with the BODE criteria and reflected end-stage COPD. Since this patient also had a PPS score of 50%, hospice education and care were discussed and initiated.

Conclusion

Patients with AECOPD commonly present to the ED. Such patients suffer with a high burden of illness and a need for immediate symptom management. However, after these measures have been instituted, strong evidence suggests that these patients typically do not receive palliative care with the same frequency compared to cancer or heart disease patients.

Management of AECOPD in the ED must include rapid treatment of dyspnea and pain, but also a determination of treatment preferences and an understanding of the prognosis. Several criteria are available to guide prognostic awareness and may help further the goals of care and disposition. Primary palliative care should be started by the ED provider for appropriate patients, with early referral to the palliative care team.

Case Scenario

A 62-year-old man who regularly presented to the ED for exacerbations of chronic obstructive pulmonary disease (COPD) after running out of his medications presented again for evaluation and treatment. His outpatient care had been poorly coordinated, and he relied on the ED to provide him with the support he needed. This presentation represented his fifth visit to the ED over the past 3 months.

The patient’s medical history was positive for asthma since childhood, tobacco use, hypertension, and a recent diagnosis of congestive heart failure (CHF). Over the past year, he had four hospital admissions, and was currently unable to walk from his bedroom to another room without becoming short of breath. He also had recently experienced a 20-lb weight loss.

At this visit, the patient complained of chest pain and lightheadedness, which he described as suffocating. Prior to these recent symptoms, he enjoyed walking in his neighborhood and talking with friends. He was an avid reader and sports fan, but admitted that he now had trouble focusing on reading and following games on television. He lived alone, and his family lived across the country. The patient further admitted that although he had attempted to quit cigarette smoking, he was unable to give up his 50-pack per year habit. He had no completed advance health care directive and had significant challenges tending to his basic needs.

The Trajectory of COPD

Chronic obstructive pulmonary disease is a common chronic illness that causes significant morbidity and mortality. A 2016 National Health Services report cited respiratory illness, primarily from COPD, as the third leading cause of death in the United States in 2014.¹The trajectory of this disease is marked by frequent exacerbations with partial recovery to baseline function. The burden of those living with COPD is significant and marked by a poor overall health-related quality of life (QOL). The ED has become a staging area for patients seeking care for exacerbations of COPD.²

The World Health Organization (WHO) and the Global Initiative for Chronic Obstructive Lung Disease (GOLD) have defined COPD as a spectrum of diseases including emphysema, chronic bronchitis, and chronic obstructive asthma characterized by persistent airflow limitation that is usually progressive and associated with an enhanced chronic inflammatory response to noxious particles or gases in the airways and lungs.³ Exacerbations and comorbidities contribute to the overall severity of COPD in individual patients.⁴

The case presented in this article illustrates the common scenario of a patient whose COPD has become severe and highly symptomatic with declining function to the point where he requires home support. His physical decline had been rapid and resulted in many unmet needs. When a patient such as this presents for emergent care, he must first be stabilized; then a care plan will need to be developed prior to discharge.

Management Goals

The overall goals of treating COPD are based on preserving function and are not curative in nature. Chronic obstructive pulmonary disease is a progressive illness that will intensify over time.⁵ As such, palliative care services are warranted. However, many patients with COPD do not receive palliative care services compared to patients with such other serious and life-limiting disease as cancer and heart disease.

Acute Exacerbations of COPD

Incidence

The frequency of acute exacerbations of COPD (AECOPD) increases with age, productive cough, long-standing COPD, previous hospitalizations related to COPD, eosinophilia, and comorbidities (eg, CHF). Patients with moderate to severe COPD and a history of prior exacerbations were found to have a higher likelihood of future exacerbations. From a quality and cost perspective, it may be useful to identify high-risk patients and strengthen their outpatient program to lessen the need for ED care and more intensive support.^6,7

In our case scenario, the patient could have been stabilized at home with a well-controlled plan and home support, which would have resulted in an improved QOL and more time free from his high symptom burden.

Causes

Bacterial and viral respiratory infections are the most likely cause of AECOPD. Environmental pollution and pulmonary embolism are also triggers. Typically, patients with AECOPD present to the ED up to several times a year² and represent the third most common cause of 30-day readmissions to the hospital.⁸ Prior exacerbations, dyspnea, and other medical comorbidities are also risk factors for more frequent hospital visits.

Presenting Signs and Symptoms

Each occurrence of AECOPD represents a worsening of a patient’s respiratory symptoms beyond normal variations. This might include increases in cough, sputum production, and dyspnea. The goal in caring for a person with an AECOPD is to stabilize the acute event and provide a treatment plan. The range of acuity for moderate to severe disease makes devising an appropriate treatment plan challenging, and after implementing the best plans, the patient’s course may be characterized by a prolonged cycle of admissions and readmissions without substantial return to baseline.

Management

In practice, ED management of AECOPD in older adults typically differs significantly from published guideline recommendations,⁹ which may result in pooroutcomes related to shortcomings in quality of care. Better adherence to guideline recommendations when caring for elderly patients with COPD may lead to improved clinical outcomes and better resource usage.^6,9

Risk Stratification

Complicating ED management is the challenge of determining the severity of illness and degree of the exacerbation. Airflow obstruction alone is not sufficient to predict outcomes, as any particular measure of obstruction is associated with a spectrum of forced expiratory volume in the first second (FEV₁) and varying performance. Moreover, peak-flow measurements are not useful in the setting of AECOPD, as opposed to their use in acute asthma exacerbations, and are not predictive of changes in clinical status.

GOLD and NICE Criteria

Guidelines have been developed and widely promoted to assist ED and hospital and community clinicians in providing evidence-based management for COPD patients. The GOLD Criteria and the National Institute for Clinical Excellence (NICE) are both clinical guidelines on management of COPD.¹⁰

Although well recognized and commonly used, the original GOLD criteria did not take into account the frequency and importance of the extrapulmonary manifestations of COPD in predicting outcome. Typically, those with severe or very severe COPD have an average of 12 co-occurring symptoms, an even greater number of signs and symptoms than those occurring in patients with cancer or heart or renal disease.¹¹

BODE Criteria

The body mass index, airflow obstruction, dyspnea and exercise capacity (BODE) criteria assess and predict the health-related QOL and mortality risk for patients with COPD. Risk is adjusted based on four factors—weight, airway obstruction, dyspnea, and exercise capacity (ie, 6-minute walk distance).¹³

Initial Evaluation and Work-Up

As previously noted, when an AECOPD patient arrives to the ED, the first priority is to stabilize the patient and initiate treatment. In this respect, initial identification of the patient’s pulse oxygen saturation (SpO₂) is important.

Laboratory Evaluation

In cases of respiratory failure, obtaining arterial blood gas (ABG) values are critical. The ABG test will assist in determining acute exacerbations of chronic hypercapnia and the need for ventilatory support. When considering CHF, a plasma B-type natriuretic peptide is useful to assess for CHF.

Imaging Studies

A chest radiograph may be useful in the initial evaluation to identify abnormalities, including barotrauma (ie, pneumothorax) and infiltrates. Additionally, in patients with comorbidities, it is important to assess cardiac status, and a chest X-ray may assist in identification of pulmonary edema, pleural effusions, and cardiomegaly. If the radiograph does show a pulmonary infiltrate (ie, pneumonia), it will help identify the probable triggers, but even in these instances, a sputum gram stain will not assist in the diagnosis.

Treatment

Relieving airflow obstruction is achieved with inhaled short-acting bronchodilators and systemic glucocorticoids, by treating infection, and by providing supplemental oxygen and ventilatory support.

Bronchodilators

The short-acting beta-adrenergic agonists (eg, albuterol) act rapidly and are effective in producing bronchodilation. Nebulized therapy may be most comfortable for the acutely ill patient. Typical dosing is 2.5 mg albuterol diluted to 3 cc by nebulizer every hour. Higher doses are not more effective, and there is no evidence of a higher response rate from constant nebulized therapy, which can cause anxiety and tachycardia in patients.¹⁴ Anticholinergic agents (eg, ipratropium) are often added despite unclear data regarding clinical advantage. In one study evaluating the effectiveness of adding ipratropium to albuterol, patients receiving a combination had the same improvement in FEV₁ at 90 minutes.¹⁵ Patients receiving ipratropium alone had the lowest rate of reported side effects.¹⁵

Systemic Glucocorticoids

Short-course systemic glucocorticoids are an important addition to treatment and have been found to improve spirometry and decrease relapse rate. The oral and intravenous (IV) routes provide the same benefit. For the acutely ill patient with challenges swallowing, the IV route is preferred. The optimal dose is not clear, but hydrocortisone doses of 100 mg to 125 mg every 6 hours for 3 days are effective, as is oral prednisone 30 mg per day for 14 days, or 60 mg per day for 3 days with a taper.

Antibiotic Therapy

Antibiotics are indicated for patients with moderate to severe AECOPD who are ill enough to be admitted to the hospital. Empiric broad spectrum treatment is recommended. The initial antibiotic regimen should target likely bacterial pathogens (Haemophilus influenzae, Moraxella catarrhalis, and Streptococcus pneumoniae in most patients) and take into account local patterns of antibiotic resistance. Flouroquinolones or third-generation cephalosporins generally provide sufficient coverage. For patients experiencing only a mild exacerbation, antibiotics are not warranted.

Magnesium Sulfate

Other supplemental medications that have been evaluated include magnesium sulfate for bronchial smooth muscle relaxation. Studies have found that while magnesium is helpful in asthma, results are mixed with COPD.¹⁶

Supplemental Oxygen

Oxygen therapy is important during an AECOPD episode. Often, concerns arise about decreasing respiratory drive, which is typically driven by hypoxia in patients who have chronic hypercapnia. Arterial blood gas determinations are important in managing a patient’s respiratory status and will assist in determining actual oxygenation and any coexistent metabolic disturbances.

Noninvasive Ventilation. Oxygen can be administered efficiently by a venturi mask, which delivers precise fractions of oxygen, or by nasal cannula. A facemask is less comfortable, but is available for higher oxygen requirements, providing up to 55% oxygen, while a nonrebreather mask delivers up to 90% oxygen.

When necessary, noninvasive positive pressure ventilation (NPPV) improves outcomes for those with severe dyspnea and signs of respiratory fatigue manifested as increased work of breathing. Noninvasive positive pressure ventilation can improve clinical outcomes and is the ventilator mode of choice for those patients with COPD. Indications include severe dyspnea with signs of increased work of breathing and respiratory acidosis (arterial pH <7.35) and partial pressure of arterial carbon dioxide (PaCO₂) >45 mm Hg.

Whenever possible, NPPV should be initiated with a triggered mode to allow spontaneous breaths. Inspiratory pressure of 8 cm to 12 cm H₂O and expiratory pressure of 3 cm to 5 cm of H₂ are recommended.

Mechanical Ventilation. Mechanical ventilation is often undesirable because it may be extraordinarily difficult to wean a patient off the device and permit safe extubation. However, if a patient cannot be stabilized with NPPV, intubation and mechanical ventilation must be considered. Typically, this occurs when there is severe respiratory distress, tachypnea >30 breaths/min, accessory muscle use, and altered mentation.

Goals of intubation/mechanical respiration include correcting oxygenation and severe respiratory acidosis as well as reducing the work of breathing. Barotrauma is a significant risk when patients with COPD require mechanical ventilation. Volume-limited modes of ventilation are commonly used, while pressure support or pressure-limited modes are less suitable for patients with airflow limitation. Again, invasive ventilation should only be administered if a patient cannot tolerate NPPV.

Palliative Care in the ED

Palliative care is an approach that improves the QOL of patients and their families facing the issues associated with life-threatening illness, through the prevention and relief of suffering by means of early identification and accurate assessment and treatment of pain and physical, psychosocial, and spiritual problems.³ This approach to care is warranted for COPD patients given the myriad of burdensome symptoms and functional decline that occurs.¹⁷

Palliative care expands traditional treatment goals to include enhancing QOL; helping with medical decision making; and identifying the goals of care. Palliative care is provided by board-certified physicians for the most complex of cases. However, the primary practice of palliative care must be delivered at the bedside by the treating provider. Managing pain, dyspnea, nausea, vomiting, and changes in bowel habits, as well as discussing goals of care, are among the basic palliative care skills all providers need to have and apply when indicated.

Palliative Care for Dyspnea

Opioids. Primary palliative care in the ED includes the appropriate use of low-dose oral and parenteral opioids to treat dyspnea in AECOPD. The use of a low-dose opioid, such as morphine 2 mg IV, titrated up to a desired response, is a safe and effective practice.¹⁸ Note the 2-mg starting dose is considered low-dose.¹⁹

With respect to managing dyspnea in AECOPD patients, nebulized opioids have not been found to be better than nebulized saline. More specific data regarding the use of oral opioids for managing refractory dyspnea in patients with predominantly COPD have been recently published: Long-acting morphine 20 mg once daily provides symptomatic relief in refractory dyspnea in the community setting. For the opioid-naïve patient, a lower dose is recommended.²⁰

Oxygenation. There is no hard evidence of the effectiveness of oxygen in the palliation of breathlessness. Humidified air is effective initially, as is providing a fan at the bedside. Short-burst oxygen therapy should only be considered for episodes of severe breathlessness in patients whose COPD is not relieved by other treatments. Oxygen should continue to be prescribed only if an improvement in breathlessness following therapy has been documented. The American Thoracic Society recommends continuous oxygen therapy in patients with COPD who have severe resting hypoxemia (PaCO₂ ≤55 mm Hg or SpO₂ ≤88%).²¹

POLST Form

The Physicians Order for Life-Sustaining Treatment (POLST) form is a set of medical orders, similar to the “do not resuscitate” (allow natural death) order. A POLST form is not an advance directive and does not serve as a substitute for a patient’s assignation of a health care agent or durable power of attorney for health care.²²

The POLST form enables physicians to order treatments patients would want, identify those treatments that patients would not want, and not provide those the patient considers “extraordinary” and excessively burdensome. A POLST form does not allow for active euthanasia or physician-assisted suicide.

Identifying treatment preferences is an important part of the initial evaluation of all patients. When dealing with an airway issue in a COPD patient, management can become complex. Ideally, the POLST form should arrive with the patient in the ED and list preferences regarding possible intensive interventions such as intubation and chest compressions. Discussing these issues with a patient in extreme distress is difficult or impossible, and in these cases, access to pertinent medical records, discussing preferences with family caregivers, and availability of a POLST form are much better ways to determine therapy.

Palliative Home Care

Patient Safety Considerations

Weight loss and associated muscle wasting are common features in patients with severe COPD, creating a high-risk situation for falls and a need for assistance with activities of daily living. The patient who is frail when discharged home from the ED requires a home-care plan before leaving the ED, and strict follow-up with the patient’s primary care provider will typically be needed within 2 to 4 weeks.

Psychological Considerations

Being mindful of the anxiety and depression that accompany the physical limitations of those with COPD is important. Mood disturbances serve as risk factors for re-hospitalization and mortality.¹³Multiple palliative care interventions provide patients assistance with these issues, including the use of antidepressants that may aid sleep, stabilize mood, and stimulate appetite.

Early referral to the palliative care team will provide improved care for the patient and family. Palliative care referral will provide continued management of the physical symptoms and evaluation and treatment of the psychosocial issues that accompany COPD. Additionally, the palliative care team can assist with safe discharge planning and follow-up, including the provision of the patient’s home needs as well as the family’s ability to cope with the home setting.

Prognosis

Predicting prognosis is difficult for the COPD patient due to the highly variable illness trajectory. Some patients have a low FEV₁ and yet are very functional. However, assessment of severity of lung function impairment, frequency of exacerbations, and need for long-term oxygen therapy helps identify those patients who are entering the final 12 months of life. Evaluating symptom burden and impact on activities of daily living for patients with COPD is comparable to those of cancer patients, and in both cases, palliative care approaches are necessary.

Predicting Morbidity and Mortality

A profile developed from observational studies can help predict 6- to 12-month morbidity and mortality in patients with advanced COPD. This profile includes the following criteria:

• Significant dyspnea;
• FEV₁ <30%;
• Number of exacerbations;
• Left heart failure or other comorbidities;
• Weight loss or cachexia;
• Decreasing performance status;
• Age older than 70 years; and
• Depression.

Although additional research is required to refine and verify this profile, reviewing these data points can prompt providers to initiate discussions with patients about treatment preferences and end-of-life care.^23,24

Palliative Performance Scale

The Palliative Performance Scale (PPS) is another scale used to predict prognosis and eligibility for hospice care.²⁵ This score provides a patient’s estimated survival.²⁵ For a patient with a PPS score of 50%, hospice education may be appropriate.

Case Scenario Continued

Both the BODE and GOLD criteria scores assisted in determining prognosis and risk profiles of the patient in our case scenario. By applying the BODE criteria, our patient had a 4-year survival benefit of under 18%. The GOLD criteria results for this patient also were consistent with the BODE criteria and reflected end-stage COPD. Since this patient also had a PPS score of 50%, hospice education and care were discussed and initiated.

Conclusion

Patients with AECOPD commonly present to the ED. Such patients suffer with a high burden of illness and a need for immediate symptom management. However, after these measures have been instituted, strong evidence suggests that these patients typically do not receive palliative care with the same frequency compared to cancer or heart disease patients.

Management of AECOPD in the ED must include rapid treatment of dyspnea and pain, but also a determination of treatment preferences and an understanding of the prognosis. Several criteria are available to guide prognostic awareness and may help further the goals of care and disposition. Primary palliative care should be started by the ED provider for appropriate patients, with early referral to the palliative care team.

Case Scenario

A 62-year-old man who regularly presented to the ED for exacerbations of chronic obstructive pulmonary disease (COPD) after running out of his medications presented again for evaluation and treatment. His outpatient care had been poorly coordinated, and he relied on the ED to provide him with the support he needed. This presentation represented his fifth visit to the ED over the past 3 months.

The patient’s medical history was positive for asthma since childhood, tobacco use, hypertension, and a recent diagnosis of congestive heart failure (CHF). Over the past year, he had four hospital admissions, and was currently unable to walk from his bedroom to another room without becoming short of breath. He also had recently experienced a 20-lb weight loss.

At this visit, the patient complained of chest pain and lightheadedness, which he described as suffocating. Prior to these recent symptoms, he enjoyed walking in his neighborhood and talking with friends. He was an avid reader and sports fan, but admitted that he now had trouble focusing on reading and following games on television. He lived alone, and his family lived across the country. The patient further admitted that although he had attempted to quit cigarette smoking, he was unable to give up his 50-pack per year habit. He had no completed advance health care directive and had significant challenges tending to his basic needs.

The Trajectory of COPD

Chronic obstructive pulmonary disease is a common chronic illness that causes significant morbidity and mortality. A 2016 National Health Services report cited respiratory illness, primarily from COPD, as the third leading cause of death in the United States in 2014.¹The trajectory of this disease is marked by frequent exacerbations with partial recovery to baseline function. The burden of those living with COPD is significant and marked by a poor overall health-related quality of life (QOL). The ED has become a staging area for patients seeking care for exacerbations of COPD.²

The World Health Organization (WHO) and the Global Initiative for Chronic Obstructive Lung Disease (GOLD) have defined COPD as a spectrum of diseases including emphysema, chronic bronchitis, and chronic obstructive asthma characterized by persistent airflow limitation that is usually progressive and associated with an enhanced chronic inflammatory response to noxious particles or gases in the airways and lungs.³ Exacerbations and comorbidities contribute to the overall severity of COPD in individual patients.⁴

The case presented in this article illustrates the common scenario of a patient whose COPD has become severe and highly symptomatic with declining function to the point where he requires home support. His physical decline had been rapid and resulted in many unmet needs. When a patient such as this presents for emergent care, he must first be stabilized; then a care plan will need to be developed prior to discharge.

Management Goals

The overall goals of treating COPD are based on preserving function and are not curative in nature. Chronic obstructive pulmonary disease is a progressive illness that will intensify over time.⁵ As such, palliative care services are warranted. However, many patients with COPD do not receive palliative care services compared to patients with such other serious and life-limiting disease as cancer and heart disease.

Acute Exacerbations of COPD

Incidence

The frequency of acute exacerbations of COPD (AECOPD) increases with age, productive cough, long-standing COPD, previous hospitalizations related to COPD, eosinophilia, and comorbidities (eg, CHF). Patients with moderate to severe COPD and a history of prior exacerbations were found to have a higher likelihood of future exacerbations. From a quality and cost perspective, it may be useful to identify high-risk patients and strengthen their outpatient program to lessen the need for ED care and more intensive support.^6,7

In our case scenario, the patient could have been stabilized at home with a well-controlled plan and home support, which would have resulted in an improved QOL and more time free from his high symptom burden.

Causes

Bacterial and viral respiratory infections are the most likely cause of AECOPD. Environmental pollution and pulmonary embolism are also triggers. Typically, patients with AECOPD present to the ED up to several times a year² and represent the third most common cause of 30-day readmissions to the hospital.⁸ Prior exacerbations, dyspnea, and other medical comorbidities are also risk factors for more frequent hospital visits.

Presenting Signs and Symptoms

Each occurrence of AECOPD represents a worsening of a patient’s respiratory symptoms beyond normal variations. This might include increases in cough, sputum production, and dyspnea. The goal in caring for a person with an AECOPD is to stabilize the acute event and provide a treatment plan. The range of acuity for moderate to severe disease makes devising an appropriate treatment plan challenging, and after implementing the best plans, the patient’s course may be characterized by a prolonged cycle of admissions and readmissions without substantial return to baseline.

Management

In practice, ED management of AECOPD in older adults typically differs significantly from published guideline recommendations,⁹ which may result in pooroutcomes related to shortcomings in quality of care. Better adherence to guideline recommendations when caring for elderly patients with COPD may lead to improved clinical outcomes and better resource usage.^6,9

Risk Stratification

Complicating ED management is the challenge of determining the severity of illness and degree of the exacerbation. Airflow obstruction alone is not sufficient to predict outcomes, as any particular measure of obstruction is associated with a spectrum of forced expiratory volume in the first second (FEV₁) and varying performance. Moreover, peak-flow measurements are not useful in the setting of AECOPD, as opposed to their use in acute asthma exacerbations, and are not predictive of changes in clinical status.

GOLD and NICE Criteria

Guidelines have been developed and widely promoted to assist ED and hospital and community clinicians in providing evidence-based management for COPD patients. The GOLD Criteria and the National Institute for Clinical Excellence (NICE) are both clinical guidelines on management of COPD.¹⁰

Although well recognized and commonly used, the original GOLD criteria did not take into account the frequency and importance of the extrapulmonary manifestations of COPD in predicting outcome. Typically, those with severe or very severe COPD have an average of 12 co-occurring symptoms, an even greater number of signs and symptoms than those occurring in patients with cancer or heart or renal disease.¹¹

BODE Criteria

The body mass index, airflow obstruction, dyspnea and exercise capacity (BODE) criteria assess and predict the health-related QOL and mortality risk for patients with COPD. Risk is adjusted based on four factors—weight, airway obstruction, dyspnea, and exercise capacity (ie, 6-minute walk distance).¹³

Initial Evaluation and Work-Up

As previously noted, when an AECOPD patient arrives to the ED, the first priority is to stabilize the patient and initiate treatment. In this respect, initial identification of the patient’s pulse oxygen saturation (SpO₂) is important.

Laboratory Evaluation

In cases of respiratory failure, obtaining arterial blood gas (ABG) values are critical. The ABG test will assist in determining acute exacerbations of chronic hypercapnia and the need for ventilatory support. When considering CHF, a plasma B-type natriuretic peptide is useful to assess for CHF.

Imaging Studies

A chest radiograph may be useful in the initial evaluation to identify abnormalities, including barotrauma (ie, pneumothorax) and infiltrates. Additionally, in patients with comorbidities, it is important to assess cardiac status, and a chest X-ray may assist in identification of pulmonary edema, pleural effusions, and cardiomegaly. If the radiograph does show a pulmonary infiltrate (ie, pneumonia), it will help identify the probable triggers, but even in these instances, a sputum gram stain will not assist in the diagnosis.

Treatment

Relieving airflow obstruction is achieved with inhaled short-acting bronchodilators and systemic glucocorticoids, by treating infection, and by providing supplemental oxygen and ventilatory support.

Bronchodilators

The short-acting beta-adrenergic agonists (eg, albuterol) act rapidly and are effective in producing bronchodilation. Nebulized therapy may be most comfortable for the acutely ill patient. Typical dosing is 2.5 mg albuterol diluted to 3 cc by nebulizer every hour. Higher doses are not more effective, and there is no evidence of a higher response rate from constant nebulized therapy, which can cause anxiety and tachycardia in patients.¹⁴ Anticholinergic agents (eg, ipratropium) are often added despite unclear data regarding clinical advantage. In one study evaluating the effectiveness of adding ipratropium to albuterol, patients receiving a combination had the same improvement in FEV₁ at 90 minutes.¹⁵ Patients receiving ipratropium alone had the lowest rate of reported side effects.¹⁵

Systemic Glucocorticoids

Short-course systemic glucocorticoids are an important addition to treatment and have been found to improve spirometry and decrease relapse rate. The oral and intravenous (IV) routes provide the same benefit. For the acutely ill patient with challenges swallowing, the IV route is preferred. The optimal dose is not clear, but hydrocortisone doses of 100 mg to 125 mg every 6 hours for 3 days are effective, as is oral prednisone 30 mg per day for 14 days, or 60 mg per day for 3 days with a taper.

Antibiotic Therapy

Antibiotics are indicated for patients with moderate to severe AECOPD who are ill enough to be admitted to the hospital. Empiric broad spectrum treatment is recommended. The initial antibiotic regimen should target likely bacterial pathogens (Haemophilus influenzae, Moraxella catarrhalis, and Streptococcus pneumoniae in most patients) and take into account local patterns of antibiotic resistance. Flouroquinolones or third-generation cephalosporins generally provide sufficient coverage. For patients experiencing only a mild exacerbation, antibiotics are not warranted.

Magnesium Sulfate

Other supplemental medications that have been evaluated include magnesium sulfate for bronchial smooth muscle relaxation. Studies have found that while magnesium is helpful in asthma, results are mixed with COPD.¹⁶

Supplemental Oxygen

Oxygen therapy is important during an AECOPD episode. Often, concerns arise about decreasing respiratory drive, which is typically driven by hypoxia in patients who have chronic hypercapnia. Arterial blood gas determinations are important in managing a patient’s respiratory status and will assist in determining actual oxygenation and any coexistent metabolic disturbances.

Noninvasive Ventilation. Oxygen can be administered efficiently by a venturi mask, which delivers precise fractions of oxygen, or by nasal cannula. A facemask is less comfortable, but is available for higher oxygen requirements, providing up to 55% oxygen, while a nonrebreather mask delivers up to 90% oxygen.

When necessary, noninvasive positive pressure ventilation (NPPV) improves outcomes for those with severe dyspnea and signs of respiratory fatigue manifested as increased work of breathing. Noninvasive positive pressure ventilation can improve clinical outcomes and is the ventilator mode of choice for those patients with COPD. Indications include severe dyspnea with signs of increased work of breathing and respiratory acidosis (arterial pH <7.35) and partial pressure of arterial carbon dioxide (PaCO₂) >45 mm Hg.

Whenever possible, NPPV should be initiated with a triggered mode to allow spontaneous breaths. Inspiratory pressure of 8 cm to 12 cm H₂O and expiratory pressure of 3 cm to 5 cm of H₂ are recommended.

Mechanical Ventilation. Mechanical ventilation is often undesirable because it may be extraordinarily difficult to wean a patient off the device and permit safe extubation. However, if a patient cannot be stabilized with NPPV, intubation and mechanical ventilation must be considered. Typically, this occurs when there is severe respiratory distress, tachypnea >30 breaths/min, accessory muscle use, and altered mentation.

Goals of intubation/mechanical respiration include correcting oxygenation and severe respiratory acidosis as well as reducing the work of breathing. Barotrauma is a significant risk when patients with COPD require mechanical ventilation. Volume-limited modes of ventilation are commonly used, while pressure support or pressure-limited modes are less suitable for patients with airflow limitation. Again, invasive ventilation should only be administered if a patient cannot tolerate NPPV.

Palliative Care in the ED

Palliative care is an approach that improves the QOL of patients and their families facing the issues associated with life-threatening illness, through the prevention and relief of suffering by means of early identification and accurate assessment and treatment of pain and physical, psychosocial, and spiritual problems.³ This approach to care is warranted for COPD patients given the myriad of burdensome symptoms and functional decline that occurs.¹⁷

Palliative care expands traditional treatment goals to include enhancing QOL; helping with medical decision making; and identifying the goals of care. Palliative care is provided by board-certified physicians for the most complex of cases. However, the primary practice of palliative care must be delivered at the bedside by the treating provider. Managing pain, dyspnea, nausea, vomiting, and changes in bowel habits, as well as discussing goals of care, are among the basic palliative care skills all providers need to have and apply when indicated.

Palliative Care for Dyspnea

Opioids. Primary palliative care in the ED includes the appropriate use of low-dose oral and parenteral opioids to treat dyspnea in AECOPD. The use of a low-dose opioid, such as morphine 2 mg IV, titrated up to a desired response, is a safe and effective practice.¹⁸ Note the 2-mg starting dose is considered low-dose.¹⁹

With respect to managing dyspnea in AECOPD patients, nebulized opioids have not been found to be better than nebulized saline. More specific data regarding the use of oral opioids for managing refractory dyspnea in patients with predominantly COPD have been recently published: Long-acting morphine 20 mg once daily provides symptomatic relief in refractory dyspnea in the community setting. For the opioid-naïve patient, a lower dose is recommended.²⁰

Oxygenation. There is no hard evidence of the effectiveness of oxygen in the palliation of breathlessness. Humidified air is effective initially, as is providing a fan at the bedside. Short-burst oxygen therapy should only be considered for episodes of severe breathlessness in patients whose COPD is not relieved by other treatments. Oxygen should continue to be prescribed only if an improvement in breathlessness following therapy has been documented. The American Thoracic Society recommends continuous oxygen therapy in patients with COPD who have severe resting hypoxemia (PaCO₂ ≤55 mm Hg or SpO₂ ≤88%).²¹

POLST Form

The Physicians Order for Life-Sustaining Treatment (POLST) form is a set of medical orders, similar to the “do not resuscitate” (allow natural death) order. A POLST form is not an advance directive and does not serve as a substitute for a patient’s assignation of a health care agent or durable power of attorney for health care.²²

The POLST form enables physicians to order treatments patients would want, identify those treatments that patients would not want, and not provide those the patient considers “extraordinary” and excessively burdensome. A POLST form does not allow for active euthanasia or physician-assisted suicide.

Identifying treatment preferences is an important part of the initial evaluation of all patients. When dealing with an airway issue in a COPD patient, management can become complex. Ideally, the POLST form should arrive with the patient in the ED and list preferences regarding possible intensive interventions such as intubation and chest compressions. Discussing these issues with a patient in extreme distress is difficult or impossible, and in these cases, access to pertinent medical records, discussing preferences with family caregivers, and availability of a POLST form are much better ways to determine therapy.

Palliative Home Care

Patient Safety Considerations

Weight loss and associated muscle wasting are common features in patients with severe COPD, creating a high-risk situation for falls and a need for assistance with activities of daily living. The patient who is frail when discharged home from the ED requires a home-care plan before leaving the ED, and strict follow-up with the patient’s primary care provider will typically be needed within 2 to 4 weeks.

Psychological Considerations

Being mindful of the anxiety and depression that accompany the physical limitations of those with COPD is important. Mood disturbances serve as risk factors for re-hospitalization and mortality.¹³Multiple palliative care interventions provide patients assistance with these issues, including the use of antidepressants that may aid sleep, stabilize mood, and stimulate appetite.

Early referral to the palliative care team will provide improved care for the patient and family. Palliative care referral will provide continued management of the physical symptoms and evaluation and treatment of the psychosocial issues that accompany COPD. Additionally, the palliative care team can assist with safe discharge planning and follow-up, including the provision of the patient’s home needs as well as the family’s ability to cope with the home setting.

Prognosis

Predicting prognosis is difficult for the COPD patient due to the highly variable illness trajectory. Some patients have a low FEV₁ and yet are very functional. However, assessment of severity of lung function impairment, frequency of exacerbations, and need for long-term oxygen therapy helps identify those patients who are entering the final 12 months of life. Evaluating symptom burden and impact on activities of daily living for patients with COPD is comparable to those of cancer patients, and in both cases, palliative care approaches are necessary.

Predicting Morbidity and Mortality

A profile developed from observational studies can help predict 6- to 12-month morbidity and mortality in patients with advanced COPD. This profile includes the following criteria:

• Significant dyspnea;
• FEV₁ <30%;
• Number of exacerbations;
• Left heart failure or other comorbidities;
• Weight loss or cachexia;
• Decreasing performance status;
• Age older than 70 years; and
• Depression.

Although additional research is required to refine and verify this profile, reviewing these data points can prompt providers to initiate discussions with patients about treatment preferences and end-of-life care.^23,24

Palliative Performance Scale

The Palliative Performance Scale (PPS) is another scale used to predict prognosis and eligibility for hospice care.²⁵ This score provides a patient’s estimated survival.²⁵ For a patient with a PPS score of 50%, hospice education may be appropriate.

Case Scenario Continued

Both the BODE and GOLD criteria scores assisted in determining prognosis and risk profiles of the patient in our case scenario. By applying the BODE criteria, our patient had a 4-year survival benefit of under 18%. The GOLD criteria results for this patient also were consistent with the BODE criteria and reflected end-stage COPD. Since this patient also had a PPS score of 50%, hospice education and care were discussed and initiated.

Conclusion

Patients with AECOPD commonly present to the ED. Such patients suffer with a high burden of illness and a need for immediate symptom management. However, after these measures have been instituted, strong evidence suggests that these patients typically do not receive palliative care with the same frequency compared to cancer or heart disease patients.

Management of AECOPD in the ED must include rapid treatment of dyspnea and pain, but also a determination of treatment preferences and an understanding of the prognosis. Several criteria are available to guide prognostic awareness and may help further the goals of care and disposition. Primary palliative care should be started by the ED provider for appropriate patients, with early referral to the palliative care team.