Original Research

Hospital admission represents a time period during which patients are at risk for poor clinical outcomes. Although some risk is directly generated by illness pathophysiology, some additive risk is generated by the emergency department (ED)inpatient service handover inherent in the admission process.[1] Increased risk of suboptimal outcomes can result from ED overcrowding, which has been associated with increased mortality, difficulty in patient disposition, and delays in provision of care.[2] Inpatient bed occupancy, as well as availability and organization of accepting inpatient service healthcare staff, can affect ED overcrowding as well.[3, 4]

The overwhelming majority of hospitalist groups accept a significant portion of their admissions via the ED.[5] Hospitalist services must balance their daily group workload between ongoing care and discharge of inpatients and the activity of admitting new patients to their service. Two major models of admission processing exist for hospitalist groups to accomplish these competing tasks. One model, called the general model, employs the use of individual hospitalists to simultaneously perform admission activity as well as ongoing ward‐based care for inpatients during their workday. In the general model, a hospitalist who admits patients on their first hospital day will generally continue to see them on their second hospital day. The other model, called the admitter‐rounder model, divides the hospitalist daily group workflow between hospitalists who are assigned to perform only admission activity (admitters), and hospitalists who are assigned to perform only ongoing care for patients who are already admitted (rounders). In the admitter‐rounder model, the admitter on a patient's first hospital day will generally not serve as the patient's rounder on subsequent hospital days.

Limited evidence exists to guide hospitalist groups on which model their service design should adopt. Conflicting evidence exists as to whether the fragmentation of care generated by an admitter‐rounder admission model is beneficial or harmful.[6, 7, 8, 9] Increased availability of attending inpatient physicians during the EDinpatient admission process has been associated with improved hospital mortality and decreased readmissions in hospital settings outside the United States, where attending availability may otherwise be limited.[10, 11, 12] Separation of admission and rounding activity within a hospitalist workforce may allow each group of hospitalists to provide more timely and effective care related to their respective tasks. Our division implemented a change from a general model to an admitter‐rounder model of care on January 2, 2012. We hypothesized that changing from a general admission model to an admitter‐rounder model of care would be associated with a decreased rate of transfer to the intensive care unit (ICU) 24 hours after floor arrival and shortened ED length of stay (LOS), due to improved availability of hospitalists during the admission process. Due to the introduction of discontinuity, we hypothesized that adoption of the admitter‐rounder model would be associated with a prolongation of hospital LOS and no overall effect on 30 day postdischarge readmission rate. We sought to examine the relationship between our division's service design change and our hypothesized variables of interest.

METHODS

RESULTS

DISCUSSION

Our observations were revealing for a statistically nonsignificant trend toward increased ICU transfers 24 hours after floor arrival after adoption of the admitter‐rounder model by the hospital medicine service. Despite prior publication of early transfer to the ICU being associated with adverse outcomes, including increased inpatient mortality, we observed no difference in mortality in our study groups.[13] We suspect that earlier transfer to the ICU in our study cohort may instead represent a protective action taken more frequently by admitting hospitalists in the admitter‐rounder model in response to provider discontinuity risks embedded in the admission process. Requests for transfer to the ICU at our institution require approval by the ICU team, and requests from attending hospitalists may be responded to differently from requests enacted by teaching team members, which as a factor also may account for some of the adjusted differences in transfer incidence. Taken together, increased availability of hospitalists during the admission process may result in earlier implementation of an overall lower threshold for implementation of ICU transfer. Our conclusion is limited by our study cohort's overall inpatient mortality rate, which is sufficiently low to preclude further assessment of the relationship of adverse outcomes with ICU transfer rate in our study groups. Therefore, clinical significance of our primary outcome findings, as well as the workload factors that impact ICU transfers initiated by hospitalist and teaching services, require further examination.

Despite a hypothesized increase in hospital LOS caused by additional discontinuity of hospitalist care in the admitter‐rounder model, adoption of the admitter‐rounder model was not associated with an increased hospital LOS. We suspect this finding may represent the presence of action(s) proximal to the admission process, on the part of either admission and/or rounding hospitalists, which decrease hospital LOS to a degree offsetting the expected LOS increase generated by provider discontinuity. Examples of such actions include more efficient testing or consultation, or improved detection of diagnostic errors.

Adoption of the admitter‐rounder model by the hospital medicine service was also associated with decreased hospital readmission rates compared to the time‐matched teaching service. We suspect that assignment of daily discharge and admission service activity to separate hospitalists in the admitter‐rounder model may allow more opportunity for rounder hospitalists to engage in activity protective against readmissions, such as greater direct engagement with postdischarge resources, or improved hospitalist availability for multidisciplinary inpatient efforts focused on discharge planning.

Adoption of the admitter‐rounder model was found to be associated with a median 29‐minute increase in ED LOS compared to the time‐matched teaching service. As a floor team member's physical presence in the ED was not required for ED‐floor transfer during the study period, increased physical availability of admitting hospitalists in the admitter‐rounder model may allow for increased opportunity for a hospitalist to disrupt ED‐specific workflows related to patient transfer (eg, disruption of transportation service activity by an earlier bedside visit from the admitting hospitalist). Hospitalists in the general model were allowed to leave after performing their daily duties, whereas admitting hospitalists in the admitter‐rounder model were assigned to stay for a timed shift, regardless of the completion of admissions; the difference in duty assignment may be associated with different hospitalist behaviors during the admission process. Improved ease for ED staff to contact hospitalist staff in the admitter‐rounder model may have led ED staff to prioritize other tasks more demanding of their continuous engagement at the expense of initiating admissions, thereby paradoxically delaying admissions to hospital medicine.

Other studies exist that attempt to describe changes in admission service structure, particularly with regard to housestaff admission activity in relation to changes in resident work hours. Many of these studies vary with regard to implementation of separate physician teams for day and night coverage, or are focused on a specific medical condition, thereby limiting their applicability to a hospital medicine service free of work‐hour restrictions and engaged in care of a wide variety of medical conditions.[18, 19, 20] In contrast, our study is an attempt to examine, in isolation, outcomes associated with adoption of an admitter‐rounder model of care as a specific discontinuity risk during the admission process, within the context of a stable system of night coverage in place for all medical teams engaged in admission activity of undifferentiated medical patients.

Limitations of our study include the inability to ascertain causality of observed outcomes, due to our observational study design. Our study was of a single hospital, which may limit applicability of our results to other hospital environments. However, the admission models examined in our study are common among hospital medicine groups. Clinically relevant outcome metrics, such as mortality and unexpected transfer to the operating room, were measured but of too low incidence to allow for further meaningful analysis. The clinical consequences and workflow practices that correlate with our study's findings likely require case review and time‐motion analyses, respectively, to further delineate the relevance of our findings; these analyses were outside of the scope of our study, and further investigation is required. In summary, our observations suggest that adoption by hospitalist services of an admitter‐rounder model of care for admissions is associated with a decreased rate of hospital readmission 30 days after discharge, with no effect on median hospital LOS, a statistically nonsignificant trend toward more ICU transfers in the first 24 hours of a patient's hospital stay, and a slight increase in median ED LOS.

Hospital admission represents a time period during which patients are at risk for poor clinical outcomes. Although some risk is directly generated by illness pathophysiology, some additive risk is generated by the emergency department (ED)inpatient service handover inherent in the admission process.[1] Increased risk of suboptimal outcomes can result from ED overcrowding, which has been associated with increased mortality, difficulty in patient disposition, and delays in provision of care.[2] Inpatient bed occupancy, as well as availability and organization of accepting inpatient service healthcare staff, can affect ED overcrowding as well.[3, 4]

The overwhelming majority of hospitalist groups accept a significant portion of their admissions via the ED.[5] Hospitalist services must balance their daily group workload between ongoing care and discharge of inpatients and the activity of admitting new patients to their service. Two major models of admission processing exist for hospitalist groups to accomplish these competing tasks. One model, called the general model, employs the use of individual hospitalists to simultaneously perform admission activity as well as ongoing ward‐based care for inpatients during their workday. In the general model, a hospitalist who admits patients on their first hospital day will generally continue to see them on their second hospital day. The other model, called the admitter‐rounder model, divides the hospitalist daily group workflow between hospitalists who are assigned to perform only admission activity (admitters), and hospitalists who are assigned to perform only ongoing care for patients who are already admitted (rounders). In the admitter‐rounder model, the admitter on a patient's first hospital day will generally not serve as the patient's rounder on subsequent hospital days.

Limited evidence exists to guide hospitalist groups on which model their service design should adopt. Conflicting evidence exists as to whether the fragmentation of care generated by an admitter‐rounder admission model is beneficial or harmful.[6, 7, 8, 9] Increased availability of attending inpatient physicians during the EDinpatient admission process has been associated with improved hospital mortality and decreased readmissions in hospital settings outside the United States, where attending availability may otherwise be limited.[10, 11, 12] Separation of admission and rounding activity within a hospitalist workforce may allow each group of hospitalists to provide more timely and effective care related to their respective tasks. Our division implemented a change from a general model to an admitter‐rounder model of care on January 2, 2012. We hypothesized that changing from a general admission model to an admitter‐rounder model of care would be associated with a decreased rate of transfer to the intensive care unit (ICU) 24 hours after floor arrival and shortened ED length of stay (LOS), due to improved availability of hospitalists during the admission process. Due to the introduction of discontinuity, we hypothesized that adoption of the admitter‐rounder model would be associated with a prolongation of hospital LOS and no overall effect on 30 day postdischarge readmission rate. We sought to examine the relationship between our division's service design change and our hypothesized variables of interest.

METHODS

RESULTS

DISCUSSION

Our observations were revealing for a statistically nonsignificant trend toward increased ICU transfers 24 hours after floor arrival after adoption of the admitter‐rounder model by the hospital medicine service. Despite prior publication of early transfer to the ICU being associated with adverse outcomes, including increased inpatient mortality, we observed no difference in mortality in our study groups.[13] We suspect that earlier transfer to the ICU in our study cohort may instead represent a protective action taken more frequently by admitting hospitalists in the admitter‐rounder model in response to provider discontinuity risks embedded in the admission process. Requests for transfer to the ICU at our institution require approval by the ICU team, and requests from attending hospitalists may be responded to differently from requests enacted by teaching team members, which as a factor also may account for some of the adjusted differences in transfer incidence. Taken together, increased availability of hospitalists during the admission process may result in earlier implementation of an overall lower threshold for implementation of ICU transfer. Our conclusion is limited by our study cohort's overall inpatient mortality rate, which is sufficiently low to preclude further assessment of the relationship of adverse outcomes with ICU transfer rate in our study groups. Therefore, clinical significance of our primary outcome findings, as well as the workload factors that impact ICU transfers initiated by hospitalist and teaching services, require further examination.

Despite a hypothesized increase in hospital LOS caused by additional discontinuity of hospitalist care in the admitter‐rounder model, adoption of the admitter‐rounder model was not associated with an increased hospital LOS. We suspect this finding may represent the presence of action(s) proximal to the admission process, on the part of either admission and/or rounding hospitalists, which decrease hospital LOS to a degree offsetting the expected LOS increase generated by provider discontinuity. Examples of such actions include more efficient testing or consultation, or improved detection of diagnostic errors.

Adoption of the admitter‐rounder model by the hospital medicine service was also associated with decreased hospital readmission rates compared to the time‐matched teaching service. We suspect that assignment of daily discharge and admission service activity to separate hospitalists in the admitter‐rounder model may allow more opportunity for rounder hospitalists to engage in activity protective against readmissions, such as greater direct engagement with postdischarge resources, or improved hospitalist availability for multidisciplinary inpatient efforts focused on discharge planning.

Adoption of the admitter‐rounder model was found to be associated with a median 29‐minute increase in ED LOS compared to the time‐matched teaching service. As a floor team member's physical presence in the ED was not required for ED‐floor transfer during the study period, increased physical availability of admitting hospitalists in the admitter‐rounder model may allow for increased opportunity for a hospitalist to disrupt ED‐specific workflows related to patient transfer (eg, disruption of transportation service activity by an earlier bedside visit from the admitting hospitalist). Hospitalists in the general model were allowed to leave after performing their daily duties, whereas admitting hospitalists in the admitter‐rounder model were assigned to stay for a timed shift, regardless of the completion of admissions; the difference in duty assignment may be associated with different hospitalist behaviors during the admission process. Improved ease for ED staff to contact hospitalist staff in the admitter‐rounder model may have led ED staff to prioritize other tasks more demanding of their continuous engagement at the expense of initiating admissions, thereby paradoxically delaying admissions to hospital medicine.

Other studies exist that attempt to describe changes in admission service structure, particularly with regard to housestaff admission activity in relation to changes in resident work hours. Many of these studies vary with regard to implementation of separate physician teams for day and night coverage, or are focused on a specific medical condition, thereby limiting their applicability to a hospital medicine service free of work‐hour restrictions and engaged in care of a wide variety of medical conditions.[18, 19, 20] In contrast, our study is an attempt to examine, in isolation, outcomes associated with adoption of an admitter‐rounder model of care as a specific discontinuity risk during the admission process, within the context of a stable system of night coverage in place for all medical teams engaged in admission activity of undifferentiated medical patients.

Limitations of our study include the inability to ascertain causality of observed outcomes, due to our observational study design. Our study was of a single hospital, which may limit applicability of our results to other hospital environments. However, the admission models examined in our study are common among hospital medicine groups. Clinically relevant outcome metrics, such as mortality and unexpected transfer to the operating room, were measured but of too low incidence to allow for further meaningful analysis. The clinical consequences and workflow practices that correlate with our study's findings likely require case review and time‐motion analyses, respectively, to further delineate the relevance of our findings; these analyses were outside of the scope of our study, and further investigation is required. In summary, our observations suggest that adoption by hospitalist services of an admitter‐rounder model of care for admissions is associated with a decreased rate of hospital readmission 30 days after discharge, with no effect on median hospital LOS, a statistically nonsignificant trend toward more ICU transfers in the first 24 hours of a patient's hospital stay, and a slight increase in median ED LOS.

Hospital admission represents a time period during which patients are at risk for poor clinical outcomes. Although some risk is directly generated by illness pathophysiology, some additive risk is generated by the emergency department (ED)inpatient service handover inherent in the admission process.[1] Increased risk of suboptimal outcomes can result from ED overcrowding, which has been associated with increased mortality, difficulty in patient disposition, and delays in provision of care.[2] Inpatient bed occupancy, as well as availability and organization of accepting inpatient service healthcare staff, can affect ED overcrowding as well.[3, 4]

The overwhelming majority of hospitalist groups accept a significant portion of their admissions via the ED.[5] Hospitalist services must balance their daily group workload between ongoing care and discharge of inpatients and the activity of admitting new patients to their service. Two major models of admission processing exist for hospitalist groups to accomplish these competing tasks. One model, called the general model, employs the use of individual hospitalists to simultaneously perform admission activity as well as ongoing ward‐based care for inpatients during their workday. In the general model, a hospitalist who admits patients on their first hospital day will generally continue to see them on their second hospital day. The other model, called the admitter‐rounder model, divides the hospitalist daily group workflow between hospitalists who are assigned to perform only admission activity (admitters), and hospitalists who are assigned to perform only ongoing care for patients who are already admitted (rounders). In the admitter‐rounder model, the admitter on a patient's first hospital day will generally not serve as the patient's rounder on subsequent hospital days.

Limited evidence exists to guide hospitalist groups on which model their service design should adopt. Conflicting evidence exists as to whether the fragmentation of care generated by an admitter‐rounder admission model is beneficial or harmful.[6, 7, 8, 9] Increased availability of attending inpatient physicians during the EDinpatient admission process has been associated with improved hospital mortality and decreased readmissions in hospital settings outside the United States, where attending availability may otherwise be limited.[10, 11, 12] Separation of admission and rounding activity within a hospitalist workforce may allow each group of hospitalists to provide more timely and effective care related to their respective tasks. Our division implemented a change from a general model to an admitter‐rounder model of care on January 2, 2012. We hypothesized that changing from a general admission model to an admitter‐rounder model of care would be associated with a decreased rate of transfer to the intensive care unit (ICU) 24 hours after floor arrival and shortened ED length of stay (LOS), due to improved availability of hospitalists during the admission process. Due to the introduction of discontinuity, we hypothesized that adoption of the admitter‐rounder model would be associated with a prolongation of hospital LOS and no overall effect on 30 day postdischarge readmission rate. We sought to examine the relationship between our division's service design change and our hypothesized variables of interest.

METHODS

RESULTS

DISCUSSION

Our observations were revealing for a statistically nonsignificant trend toward increased ICU transfers 24 hours after floor arrival after adoption of the admitter‐rounder model by the hospital medicine service. Despite prior publication of early transfer to the ICU being associated with adverse outcomes, including increased inpatient mortality, we observed no difference in mortality in our study groups.[13] We suspect that earlier transfer to the ICU in our study cohort may instead represent a protective action taken more frequently by admitting hospitalists in the admitter‐rounder model in response to provider discontinuity risks embedded in the admission process. Requests for transfer to the ICU at our institution require approval by the ICU team, and requests from attending hospitalists may be responded to differently from requests enacted by teaching team members, which as a factor also may account for some of the adjusted differences in transfer incidence. Taken together, increased availability of hospitalists during the admission process may result in earlier implementation of an overall lower threshold for implementation of ICU transfer. Our conclusion is limited by our study cohort's overall inpatient mortality rate, which is sufficiently low to preclude further assessment of the relationship of adverse outcomes with ICU transfer rate in our study groups. Therefore, clinical significance of our primary outcome findings, as well as the workload factors that impact ICU transfers initiated by hospitalist and teaching services, require further examination.

Despite a hypothesized increase in hospital LOS caused by additional discontinuity of hospitalist care in the admitter‐rounder model, adoption of the admitter‐rounder model was not associated with an increased hospital LOS. We suspect this finding may represent the presence of action(s) proximal to the admission process, on the part of either admission and/or rounding hospitalists, which decrease hospital LOS to a degree offsetting the expected LOS increase generated by provider discontinuity. Examples of such actions include more efficient testing or consultation, or improved detection of diagnostic errors.

Adoption of the admitter‐rounder model by the hospital medicine service was also associated with decreased hospital readmission rates compared to the time‐matched teaching service. We suspect that assignment of daily discharge and admission service activity to separate hospitalists in the admitter‐rounder model may allow more opportunity for rounder hospitalists to engage in activity protective against readmissions, such as greater direct engagement with postdischarge resources, or improved hospitalist availability for multidisciplinary inpatient efforts focused on discharge planning.

Adoption of the admitter‐rounder model was found to be associated with a median 29‐minute increase in ED LOS compared to the time‐matched teaching service. As a floor team member's physical presence in the ED was not required for ED‐floor transfer during the study period, increased physical availability of admitting hospitalists in the admitter‐rounder model may allow for increased opportunity for a hospitalist to disrupt ED‐specific workflows related to patient transfer (eg, disruption of transportation service activity by an earlier bedside visit from the admitting hospitalist). Hospitalists in the general model were allowed to leave after performing their daily duties, whereas admitting hospitalists in the admitter‐rounder model were assigned to stay for a timed shift, regardless of the completion of admissions; the difference in duty assignment may be associated with different hospitalist behaviors during the admission process. Improved ease for ED staff to contact hospitalist staff in the admitter‐rounder model may have led ED staff to prioritize other tasks more demanding of their continuous engagement at the expense of initiating admissions, thereby paradoxically delaying admissions to hospital medicine.

Other studies exist that attempt to describe changes in admission service structure, particularly with regard to housestaff admission activity in relation to changes in resident work hours. Many of these studies vary with regard to implementation of separate physician teams for day and night coverage, or are focused on a specific medical condition, thereby limiting their applicability to a hospital medicine service free of work‐hour restrictions and engaged in care of a wide variety of medical conditions.[18, 19, 20] In contrast, our study is an attempt to examine, in isolation, outcomes associated with adoption of an admitter‐rounder model of care as a specific discontinuity risk during the admission process, within the context of a stable system of night coverage in place for all medical teams engaged in admission activity of undifferentiated medical patients.

Limitations of our study include the inability to ascertain causality of observed outcomes, due to our observational study design. Our study was of a single hospital, which may limit applicability of our results to other hospital environments. However, the admission models examined in our study are common among hospital medicine groups. Clinically relevant outcome metrics, such as mortality and unexpected transfer to the operating room, were measured but of too low incidence to allow for further meaningful analysis. The clinical consequences and workflow practices that correlate with our study's findings likely require case review and time‐motion analyses, respectively, to further delineate the relevance of our findings; these analyses were outside of the scope of our study, and further investigation is required. In summary, our observations suggest that adoption by hospitalist services of an admitter‐rounder model of care for admissions is associated with a decreased rate of hospital readmission 30 days after discharge, with no effect on median hospital LOS, a statistically nonsignificant trend toward more ICU transfers in the first 24 hours of a patient's hospital stay, and a slight increase in median ED LOS.

Thirty‐six million adults were discharged from US hospitals in 2012, with approximately 45% from medicine service lines.[1, 2] Discharge planning, a key aspect of care for hospitalized patients,[3] should involve the development of a plan to enable the patient to be discharged at the appropriate time and with provision of sufficient postdischarge support and services.[4]

Central to the discharge planning process is an assessment of a patient's readiness for discharge. Readiness is often a provider‐driven process, based on specific clinical and health system benchmarks.[5] However, providers' perception of readiness for discharge does not always correlate with patients' self‐assessments or objective measures of understanding.[6] For example, nurses overestimate patients' readiness for discharge compared to patients' own self‐report.[7] As a result, the need to include the patient perspective is increasingly recognized as an important contributing factor in the discharge planning process.[8, 9]

Current approaches to assessing discharge readiness are typically single assessments. However, these assessments do not take into account the complexity of discharge planning or patients' understanding, or their ability to carry out postacute care tasks.[8] In addition, few models have included assessments of physical stability and functional ability along with measures such as ability to manage self‐care activities at home, coping and social support, or access to health system and community resources.[10, 11]

To address these gaps in the existing literature, we carried out a prospective observational study of daily, patient‐reported, assessments of discharge readiness to better understand patients' perspectives on issues that could impede the transition to home. Using these data, we then sought to determine the prevalence of patient‐reported discharge barriers and the frequency with which they were resolved prior to the day of discharge. We also explored whether problems identified at discharge were associated with 30‐day readmission.

METHODS

RESULTS

DISCUSSION

Assessing discharge readiness highlights an opportunity to engage patients directly in their discharge planning process. However, our prospective study of 163 hospitalized adults revealed that unresolved discharge barriers were common; 90% of patients were discharged with at least 1 issue that might inhibit an effective transition home. The majority of these patients were also discharged home without any support services. In addition, many of the major barriers patients reportedpain, lack of understanding around plans, and ability to provide self‐carewere consistent from admission to discharge, suggesting a missed opportunity to address problems present early in a patient's stay.

Some of the issues our patients described, such as pain; lack of understanding of a recovery plan; and functional, social, and environmental vulnerabilities that impede recovery, have been described in studies using data collected in the postacute time period.[13, 14, 15] Focus on postacute barriers is likely to be of limited clinical utility to assist in any real‐time discharge planning, particularly planning that assesses individual patients' needs and tailors programs and education appropriately. Having said this, consistency between our results and data collected from postdischarge patients again supports broad areas of improvement for health systems.

Persistent gaps in care at discharge may be a result of limited standardization of discharge processes and a lack of engagement in obtaining patient‐reported concerns. Lack of a framework for preparing individual patients for discharge has been recognized as a significant obstacle to effective discharge planning. For example, Hesselink et al.'s qualitative study with almost 200 patients and providers across multiple institutions described how lack of a standard approach to providing discharge planning resulted in gaps in information provision.[16] Similarly, Horwitz et al. described wide variation in discharge practices at a US academic medical center, suggesting lack of a standard approach to identifying patient needs.[14]

Although many transitions of care programs have supported implementation of specific care interventions at a hospital or health system level, there have been surprisingly few studies describing efforts to standardize the assessment of discharge barriers and prospectively engage individual patients.[17] One emblematic study used stakeholder interviews and process mapping to develop a readiness report within their electronic medical record (EMR).[17] Aggregate data from the EMR including orders and discharge plans were coded, extracted, and summarized into a report. The overall goal of the report was to identify progress toward completion of discharge tasks; however, a limitation was that it did not explicitly include patient self‐assessments. Another study by Grimmer et al. describes the development of a patient‐centered discharge checklist that incorporated patients and care concerns.[18] The themes incorporated into this checklist cover many transitional issues; however, outside of the checklist's development, few publications or Web resources describe it in actual use.

Our approach may represent an advance in approaches to engaging patients in discharge planning and preparing patients for leaving the hospital. Although our data do not support efficacy of our daily surveys in terms of improving discharge planning, this initial evaluation provides the framework upon which providers can develop discharge plans that are both standardized in terms of using a structured multidomain communication tool to elicit barriers, as well as patient‐centered and patient‐directed, by using the information collected in the survey tool to initiate tailored discharge planning earlier in the hospital stay. However, our program points out an important limitation of an entirely patient‐initiated program, which is difficulty obtaining truly daily assessments. During this study, we had a single research assistant visit patients as frequently as possible during hospitalization, but even daily visits did not yield complete information on all patients. Although this limitation may in part be due to the fact that our study was a focused pilot of an approach we hope to expand, it also represents the complexity of patient experience in the hospital, where patients are often out of their room for tests, are unable to complete a survey because of problematic symptoms, or simply are unwilling or unable to participate in regular surveys.

Our study has a number of limitations. First, the number of patients in our study overall, and the number who completed at least 2 surveys, was relatively small, limiting the generalizability of the study and our ability to determine the true prevalence of unresolved barriers at discharge. In addition, our selection criteria and response rates have limited our sample in that our final group may not be representative of all patients admitted to our medicine service. The broad exclusion of patients who had physical or psychosocial barriers, and those who were acutely unwell and symptomatic, has the potential to introduce selection bias given the excluded populations are those most at risk of readmission. We also acknowledge that some of the issues that patients' are reporting may be chronic ones. However, given the fact that patients feel these issues, even if chronic, are unaddressed or that they want to talk with their doctor about them, is still a very large potential gap in care and patient engagement.

However, despite these limitations, which seem most likely to produce a cohort that is more likely to be able to participate in our survey, and in turn more likely to participate in their care more broadly, we still observed disappointing resolution of discharge barriers. In addition, our adapted survey instrument, though based on well‐supported conceptual frameworks,[19] has not been extensively tested outside of our hospital setting. Finally, as a single‐center study, our results cannot be generalized to other settings.

Assessing discharge readiness highlights an opportunity to obtain patient self‐reported barriers to discharge. This can facilitate discharge planning that targets individual patient needs. This information also emphasizes potentially fruitful opportunities for improved communication and education activities, potentially if these data are fed back to providers in real time, potentially as part of team‐based dashboards or the context of interdisciplinary team models.

Thirty‐six million adults were discharged from US hospitals in 2012, with approximately 45% from medicine service lines.[1, 2] Discharge planning, a key aspect of care for hospitalized patients,[3] should involve the development of a plan to enable the patient to be discharged at the appropriate time and with provision of sufficient postdischarge support and services.[4]

Central to the discharge planning process is an assessment of a patient's readiness for discharge. Readiness is often a provider‐driven process, based on specific clinical and health system benchmarks.[5] However, providers' perception of readiness for discharge does not always correlate with patients' self‐assessments or objective measures of understanding.[6] For example, nurses overestimate patients' readiness for discharge compared to patients' own self‐report.[7] As a result, the need to include the patient perspective is increasingly recognized as an important contributing factor in the discharge planning process.[8, 9]

Current approaches to assessing discharge readiness are typically single assessments. However, these assessments do not take into account the complexity of discharge planning or patients' understanding, or their ability to carry out postacute care tasks.[8] In addition, few models have included assessments of physical stability and functional ability along with measures such as ability to manage self‐care activities at home, coping and social support, or access to health system and community resources.[10, 11]

To address these gaps in the existing literature, we carried out a prospective observational study of daily, patient‐reported, assessments of discharge readiness to better understand patients' perspectives on issues that could impede the transition to home. Using these data, we then sought to determine the prevalence of patient‐reported discharge barriers and the frequency with which they were resolved prior to the day of discharge. We also explored whether problems identified at discharge were associated with 30‐day readmission.

METHODS

RESULTS

DISCUSSION

Assessing discharge readiness highlights an opportunity to engage patients directly in their discharge planning process. However, our prospective study of 163 hospitalized adults revealed that unresolved discharge barriers were common; 90% of patients were discharged with at least 1 issue that might inhibit an effective transition home. The majority of these patients were also discharged home without any support services. In addition, many of the major barriers patients reportedpain, lack of understanding around plans, and ability to provide self‐carewere consistent from admission to discharge, suggesting a missed opportunity to address problems present early in a patient's stay.

Some of the issues our patients described, such as pain; lack of understanding of a recovery plan; and functional, social, and environmental vulnerabilities that impede recovery, have been described in studies using data collected in the postacute time period.[13, 14, 15] Focus on postacute barriers is likely to be of limited clinical utility to assist in any real‐time discharge planning, particularly planning that assesses individual patients' needs and tailors programs and education appropriately. Having said this, consistency between our results and data collected from postdischarge patients again supports broad areas of improvement for health systems.

Persistent gaps in care at discharge may be a result of limited standardization of discharge processes and a lack of engagement in obtaining patient‐reported concerns. Lack of a framework for preparing individual patients for discharge has been recognized as a significant obstacle to effective discharge planning. For example, Hesselink et al.'s qualitative study with almost 200 patients and providers across multiple institutions described how lack of a standard approach to providing discharge planning resulted in gaps in information provision.[16] Similarly, Horwitz et al. described wide variation in discharge practices at a US academic medical center, suggesting lack of a standard approach to identifying patient needs.[14]

Although many transitions of care programs have supported implementation of specific care interventions at a hospital or health system level, there have been surprisingly few studies describing efforts to standardize the assessment of discharge barriers and prospectively engage individual patients.[17] One emblematic study used stakeholder interviews and process mapping to develop a readiness report within their electronic medical record (EMR).[17] Aggregate data from the EMR including orders and discharge plans were coded, extracted, and summarized into a report. The overall goal of the report was to identify progress toward completion of discharge tasks; however, a limitation was that it did not explicitly include patient self‐assessments. Another study by Grimmer et al. describes the development of a patient‐centered discharge checklist that incorporated patients and care concerns.[18] The themes incorporated into this checklist cover many transitional issues; however, outside of the checklist's development, few publications or Web resources describe it in actual use.

Our approach may represent an advance in approaches to engaging patients in discharge planning and preparing patients for leaving the hospital. Although our data do not support efficacy of our daily surveys in terms of improving discharge planning, this initial evaluation provides the framework upon which providers can develop discharge plans that are both standardized in terms of using a structured multidomain communication tool to elicit barriers, as well as patient‐centered and patient‐directed, by using the information collected in the survey tool to initiate tailored discharge planning earlier in the hospital stay. However, our program points out an important limitation of an entirely patient‐initiated program, which is difficulty obtaining truly daily assessments. During this study, we had a single research assistant visit patients as frequently as possible during hospitalization, but even daily visits did not yield complete information on all patients. Although this limitation may in part be due to the fact that our study was a focused pilot of an approach we hope to expand, it also represents the complexity of patient experience in the hospital, where patients are often out of their room for tests, are unable to complete a survey because of problematic symptoms, or simply are unwilling or unable to participate in regular surveys.

Our study has a number of limitations. First, the number of patients in our study overall, and the number who completed at least 2 surveys, was relatively small, limiting the generalizability of the study and our ability to determine the true prevalence of unresolved barriers at discharge. In addition, our selection criteria and response rates have limited our sample in that our final group may not be representative of all patients admitted to our medicine service. The broad exclusion of patients who had physical or psychosocial barriers, and those who were acutely unwell and symptomatic, has the potential to introduce selection bias given the excluded populations are those most at risk of readmission. We also acknowledge that some of the issues that patients' are reporting may be chronic ones. However, given the fact that patients feel these issues, even if chronic, are unaddressed or that they want to talk with their doctor about them, is still a very large potential gap in care and patient engagement.

However, despite these limitations, which seem most likely to produce a cohort that is more likely to be able to participate in our survey, and in turn more likely to participate in their care more broadly, we still observed disappointing resolution of discharge barriers. In addition, our adapted survey instrument, though based on well‐supported conceptual frameworks,[19] has not been extensively tested outside of our hospital setting. Finally, as a single‐center study, our results cannot be generalized to other settings.

Assessing discharge readiness highlights an opportunity to obtain patient self‐reported barriers to discharge. This can facilitate discharge planning that targets individual patient needs. This information also emphasizes potentially fruitful opportunities for improved communication and education activities, potentially if these data are fed back to providers in real time, potentially as part of team‐based dashboards or the context of interdisciplinary team models.

Thirty‐six million adults were discharged from US hospitals in 2012, with approximately 45% from medicine service lines.[1, 2] Discharge planning, a key aspect of care for hospitalized patients,[3] should involve the development of a plan to enable the patient to be discharged at the appropriate time and with provision of sufficient postdischarge support and services.[4]

Central to the discharge planning process is an assessment of a patient's readiness for discharge. Readiness is often a provider‐driven process, based on specific clinical and health system benchmarks.[5] However, providers' perception of readiness for discharge does not always correlate with patients' self‐assessments or objective measures of understanding.[6] For example, nurses overestimate patients' readiness for discharge compared to patients' own self‐report.[7] As a result, the need to include the patient perspective is increasingly recognized as an important contributing factor in the discharge planning process.[8, 9]

Current approaches to assessing discharge readiness are typically single assessments. However, these assessments do not take into account the complexity of discharge planning or patients' understanding, or their ability to carry out postacute care tasks.[8] In addition, few models have included assessments of physical stability and functional ability along with measures such as ability to manage self‐care activities at home, coping and social support, or access to health system and community resources.[10, 11]

To address these gaps in the existing literature, we carried out a prospective observational study of daily, patient‐reported, assessments of discharge readiness to better understand patients' perspectives on issues that could impede the transition to home. Using these data, we then sought to determine the prevalence of patient‐reported discharge barriers and the frequency with which they were resolved prior to the day of discharge. We also explored whether problems identified at discharge were associated with 30‐day readmission.

METHODS

RESULTS

DISCUSSION

Assessing discharge readiness highlights an opportunity to engage patients directly in their discharge planning process. However, our prospective study of 163 hospitalized adults revealed that unresolved discharge barriers were common; 90% of patients were discharged with at least 1 issue that might inhibit an effective transition home. The majority of these patients were also discharged home without any support services. In addition, many of the major barriers patients reportedpain, lack of understanding around plans, and ability to provide self‐carewere consistent from admission to discharge, suggesting a missed opportunity to address problems present early in a patient's stay.

Some of the issues our patients described, such as pain; lack of understanding of a recovery plan; and functional, social, and environmental vulnerabilities that impede recovery, have been described in studies using data collected in the postacute time period.[13, 14, 15] Focus on postacute barriers is likely to be of limited clinical utility to assist in any real‐time discharge planning, particularly planning that assesses individual patients' needs and tailors programs and education appropriately. Having said this, consistency between our results and data collected from postdischarge patients again supports broad areas of improvement for health systems.

Persistent gaps in care at discharge may be a result of limited standardization of discharge processes and a lack of engagement in obtaining patient‐reported concerns. Lack of a framework for preparing individual patients for discharge has been recognized as a significant obstacle to effective discharge planning. For example, Hesselink et al.'s qualitative study with almost 200 patients and providers across multiple institutions described how lack of a standard approach to providing discharge planning resulted in gaps in information provision.[16] Similarly, Horwitz et al. described wide variation in discharge practices at a US academic medical center, suggesting lack of a standard approach to identifying patient needs.[14]

Although many transitions of care programs have supported implementation of specific care interventions at a hospital or health system level, there have been surprisingly few studies describing efforts to standardize the assessment of discharge barriers and prospectively engage individual patients.[17] One emblematic study used stakeholder interviews and process mapping to develop a readiness report within their electronic medical record (EMR).[17] Aggregate data from the EMR including orders and discharge plans were coded, extracted, and summarized into a report. The overall goal of the report was to identify progress toward completion of discharge tasks; however, a limitation was that it did not explicitly include patient self‐assessments. Another study by Grimmer et al. describes the development of a patient‐centered discharge checklist that incorporated patients and care concerns.[18] The themes incorporated into this checklist cover many transitional issues; however, outside of the checklist's development, few publications or Web resources describe it in actual use.

Our approach may represent an advance in approaches to engaging patients in discharge planning and preparing patients for leaving the hospital. Although our data do not support efficacy of our daily surveys in terms of improving discharge planning, this initial evaluation provides the framework upon which providers can develop discharge plans that are both standardized in terms of using a structured multidomain communication tool to elicit barriers, as well as patient‐centered and patient‐directed, by using the information collected in the survey tool to initiate tailored discharge planning earlier in the hospital stay. However, our program points out an important limitation of an entirely patient‐initiated program, which is difficulty obtaining truly daily assessments. During this study, we had a single research assistant visit patients as frequently as possible during hospitalization, but even daily visits did not yield complete information on all patients. Although this limitation may in part be due to the fact that our study was a focused pilot of an approach we hope to expand, it also represents the complexity of patient experience in the hospital, where patients are often out of their room for tests, are unable to complete a survey because of problematic symptoms, or simply are unwilling or unable to participate in regular surveys.

Our study has a number of limitations. First, the number of patients in our study overall, and the number who completed at least 2 surveys, was relatively small, limiting the generalizability of the study and our ability to determine the true prevalence of unresolved barriers at discharge. In addition, our selection criteria and response rates have limited our sample in that our final group may not be representative of all patients admitted to our medicine service. The broad exclusion of patients who had physical or psychosocial barriers, and those who were acutely unwell and symptomatic, has the potential to introduce selection bias given the excluded populations are those most at risk of readmission. We also acknowledge that some of the issues that patients' are reporting may be chronic ones. However, given the fact that patients feel these issues, even if chronic, are unaddressed or that they want to talk with their doctor about them, is still a very large potential gap in care and patient engagement.

However, despite these limitations, which seem most likely to produce a cohort that is more likely to be able to participate in our survey, and in turn more likely to participate in their care more broadly, we still observed disappointing resolution of discharge barriers. In addition, our adapted survey instrument, though based on well‐supported conceptual frameworks,[19] has not been extensively tested outside of our hospital setting. Finally, as a single‐center study, our results cannot be generalized to other settings.

Assessing discharge readiness highlights an opportunity to obtain patient self‐reported barriers to discharge. This can facilitate discharge planning that targets individual patient needs. This information also emphasizes potentially fruitful opportunities for improved communication and education activities, potentially if these data are fed back to providers in real time, potentially as part of team‐based dashboards or the context of interdisciplinary team models.

Delirium, a clinical syndrome characterized by inattention and acute cognitive dysfunction, is very common in older hospitalized patients, with a reported incidence of 18% to 35% at time of admission and overall occurrence rates of 29% to 64%.[1] Previous studies have reported that a diagnosis of delirium is not benign and is associated with other adverse outcomes including prolonged hospitalization, institutionalization, increased cost, and mortality. These outcomes occurred independent of age, prior cognitive functioning, and comorbidities.[2] Guidelines recommend that management of inpatient delirium should be focused on addressing the underlying etiology and managed with nonpharmacological interventions whenever possible.[3, 4, 5] However, implementing these recommendations can prove to be very challenging in hospital settings. Providers frequently have to resort to medical therapies, including antipsychotics (APs). Although these medications are commonly used to treat delirium in elderly patients, there is limited evidence to support their efficacy, and there are currently no proven pharmacological alternatives to these medications.[6] Furthermore, previous studies have demonstrated an increased risk of stroke, infection, cognitive impairment, and mortality in elders with dementia who receive long‐term AP therapy.[7, 8, 9] Yet as many as 48% of hospitalized elders who were newly started on APs had these drugs continued at time of discharge.[10]

There have been few studies describing the long‐term outcomes of elderly patient who are started on APs in the hospital. Most information on outcomes comes from patients with dementia. Therefore, we studied the 1‐year outcomes of a cohort of patients with and without dementia who were started on APs in the hospital and then discharged on these medications. In this cohort, we aimed to describe the number of readmissions, reasons for readmissions, duration of AP therapy, use of other sedating medications such as anxiolytics, hypnotics, and antihistamines as well as the incidence of readmission and death 1 year after the index hospital discharge.

METHODS

We previously described a retrospective cohort of 300 elders (65 years old) admitted to a tertiary care hospital between October 1, 2012 and September 31, 2013 who were newly prescribed APs while hospitalized.[10] Of patients alive at the time of discharge (260), 56% (146 patients) were discharged on APs. Two investigators extracted these 148 patient charts independently to identify and quantify the number of readmissions to the index hospital. We then limited the sample to only the first readmission per patient following the index admission and extracted this readmission for each patient. We first determined if APs were present on the admission medication reconciliation. If APs were not present on admission, we examined whether they were resumed during the hospitalization using the electronic medication administration summary. If they were present on admission, we looked to see if they were discontinued during the readmission and if additional new APs were started during the hospitalizations. We documented the circumstances around APs use and identified patients who died during their hospitalizations. We identified delirium using the same terms that were described in our prior study on the same cohort of patients.[10] We determined if patients were delirious using a predetermined algorithm (Figure 1). Briefly, we first determined delirium was documented. We then examined whether there was a Confusion Assessment Method (CAM) instrument included in the record. If a CAM instrument was not documented, we then looked for documentation using specific terms (eg, disorientation, confusions). We identified patients with dementia by determining whether dementia was documented along with other admission medical comorbidities. If it was not, we determined whether dementia was newly diagnosed during the hospital stay using progress notes or consultation notes. We did not objectively define criteria for diagnosis of dementia. We used the National Death Index (NDI) to determine mortality for all patients 1 year after discharge from the index hospitalization. The NDI is a national database of death records maintained by the National Center for Health Statistics. It has shown consistently high sensitivity and specificity for detection of death.[11]

Figure 1

We used descriptive statistics (means, standard deviations, range, and percents as appropriate to the scale of measurement) to describe the patient sample. We then used multiple logistic regression to identify significant predictors of death within 1 year of discharge.[12] Univariate analysis was used to select candidates for the logistic model (t tests for continuous factors and ² for discrete factors). All factors with a significance level 0.2 on univariate analysis were included in the logistic regression, in addition to age and sex (regardless of significance). A maximum likelihood procedure was used to calculate the regression coefficients for the logistic model. The likelihood ratio criterion was used to determine the significance of individual factors in the regression model.[13] Factors with a significance level of 0.15 or less were retained in the final model, in addition to age and sex.

RESULTS

The 260 patients discharged alive from their index admissions had a 1‐year mortality rate of 29% (75/260). Of the 146/260 patients discharged on APs, 60 (41%) patients experienced at least 1 readmission (mean = 2 readmissions per patient; range, 18, with 111 total readmissions for 60 patients) within 1 year from discharge (Figure 2). Most common diagnoses at the time of readmissions were related neurological and psychiatric disorders (14%), cardiovascular and circulation disorders (13%), renal injury and electrolyte disorders (11%), and infections (6%). Among patients with at least 1 readmission, the mean age was 81.3 (range, 65.599.7), 60% were male, and 45% were admitted from a skilled nursing facility or rehabilitation facility (Table 1). Median time to readmission was 43.5 days (range, 1343 days), and 79% were readmitted to a medical service. The remaining 20% were admitted to a surgical service. Inpatient mortality during first readmissions was 8% (5/60). At the time of first readmission, 39/60 (65%) of patients were still on the same APs on which they had been discharged, and the APs were continued during the hospitalization in 79% of the patients (61% quetiapine, 19% olanzapine, and 13% risperidone). About half of patients whose APs were discontinued prior to readmission received a new AP during their hospital stays (9/20; 45%). One patient had been started on quetiapine in the outpatient setting. No patients were found to have new benzodiazepines, nonbenzodiazepine hypnotic, or antihistamines on their admission medication list.

Figure 2

Eighteen patients received 1 or more new APs during the readmission hospitalizations. These included haloperidol (89%) and quetiapine (39%). Delirium was the main reported indication for starting APs (78%), but in 17% of cases no indication was documented. An electrocardiogram (ECG) was performed in 94% prior to APs administration and for 22% after APs administration. Corrected QT interval (QTc) of >500 ms was present in 18% of patients in pretreatment ECG and 50% of patients in post‐AP ECG. Of patients who survived readmission, 58% (32/55) were discharged to postacute facilities. Of the 39 patients who were on the same APs from index admission, 27 (69%) patients were eventually discharged on the same APs or new APs started during the readmission.

In the multivariable model (Table 2), predictors of death at 1 year included discharge to postacute facilities after index admission (odds ratio [OR]: 2.28; 95% confidence interval [CI]: 1.10‐4.73, P = 0.03) and QTc prolongation >500 ms during index admission (OR: 3.41; 95% CI: 1.34‐8.67, P = 0.01). Age and gender were not associated with 1‐year mortality.

DISCUSSION

In a cohort of elderly patients who were discharged on APs, nearly one‐third (29%) died within 1 year of the hospitalization in which APs were initiated. Nearly half of the survivors from the index admission (41%) experienced at least 1 admission within 1 year from discharge. Of readmitted patients, two‐thirds were taking the same APs that had been started during the index hospitalization. Half of the patients not on APs on readmission were started on an AP during the hospitalization, most often because they became delirious on return to the acute care setting. Compared to patients discharged home after an index admission, patients who were discharged to postacute facilities were almost 4 times as likely to die during the year subsequent to the admission. These data suggest that once patients are started on APs, most are continued on them until the next admission or are restarted during that readmission. Moreover, hospitalized elders who require an AP are at high risk for mortality in the coming year.

Prior studies have reported that patients with delirium have elevated 1‐year mortality rates.[14, 15, 16, 17, 18, 19] A secondary analysis of the Delirium Prevention Trial, which included 437 hospitalized older patients, revealed a 1‐year mortality rate of 20% in those who were never delirious during hospitalization, compared to 26% to 38% in patients with delirium.[19] Additionally, 1‐year mortality in hospitalized older patients with delirium (36%) was shown to be higher than patients with dementia (29%) or depression (26%).[17] Unlike these studies, not all of the patients in our study had documented delirium, but all received an AP. Still, it is notable that the 1‐year mortality rate for delirium in general is similar to what we found in this study.

The literature has also reported that long‐term AP use is associated with excess mortality in elder patients, especially those with dementia.[20, 21, 22] In a retrospective cohort study, older patients with dementia who were taking antipsychotics had significantly higher 1‐year mortality rates (23%29%) than patients not taking antipsychotic medications (15%). In a large Canadian propensity score‐matched cohort study that included over 13,000 demented older adults, the mortality was higher in the community‐dwelling elders who received atypical APs compared to no APs, with a difference of 1.1% in 180‐day mortality rate after initiation of APs.[21] The absolute mortality rate was 2.6% higher in patients who received typical compared to atypical APs. Unlike these studies, not every patient in our cohort had a diagnosis of dementia, but again, mortality rates in these studies appear similar to our cohort.

In contrast, other observational studies have not found an increased risk associated with receipt of APs. For example, a prospective study that enrolled approximately 950 patients with probable dementia showed that AP use was not associated with time to death after adjustment for comorbidities, demographic and cognitive variables.[23] These conflicting results highlight the difficulties of attributing outcomes in high‐risk populations. Although the excess mortality observed in patients taking APs may be related to the risks of APs, it is quite possible that patients who require APs (most often for delirium or agitated dementia) are at higher risk of death. This confounding by indication may be nearly impossible to adjust for retrospectively, even using techniques such as propensity matching.

Our report adds to the literature; we know of no studies to date describing a cohort of patients, most with delirium, who were started on APs in the hospital. We also attempted to identify the reasons that patients were started on APs, which have been infrequently reported. As noted above, our 1‐year mortality rate of 29% among older patients prescribed APs in the hospital was quite similar to mortality rates both for patients with delirium who were not necessarily treated with APs and patients with dementia who were treated with APs. This finding further supports the argument that risk factors for mortality, including dementia, delirium, and AP use are very difficult to tease apart. It is possible that the reasons that APs are prescribed (agitated delirium or dementia) have as much to do with the excess mortality reported in observational studies of APs as the use of APs themselves.

The high rate of continued AP use we observed (two‐thirds of readmitted patients) may reflect limited pharmacological alternatives to these medications with little evidence to support treating the symptoms of delirium with other drug classes, along with suboptimal environmental and behavioral modifications in postacute facilities and hospitals. This is unfortunate given that delirium is often preventable. Systematic implementation of well‐documented strategies to decrease delirium in hospitals and postacute facilities would likely reduce the prescription of APs and has the potential to slow the decline in this vulnerable population. A meta‐analysis incorporating both randomized and nonrandomized trials of medical and surgical patients showed that multicomponent nonpharmacologic interventions decreased delirium by 50%.[24] Thus, simple interventions such as reorientation, early mobilization, optimizing vision and hearing, sleepwake cycle preservation, and hydration might avoid roughly 1 million cases of delirium in hospitalized older adults annually.[24] The Hospital Elder Life Program and Acute Care for Elders units are examples of programs that have been shown to decrease the incidence of delirium.[25, 26]

Despite vigorous efforts to prevent delirium, a subgroup of patients still will become delirious. These patients are at high risk for death. Our mortality prediction model revealed that patients who were discharged to postacute facilities were 4 times more likely to die during the subsequent year compared to patients who were discharged home. Patients discharged to postacute facilities are likely to have a higher burden of disease, greater functional and cognitive impairment, and more frailty than those who are able to return to the community. Very ill and/or frail patients receiving APs in the hospital and requiring APs on discharge to postacute care facilities have limited survival and may benefit from expedited palliative care interventions to clarify prognosis and goals, and relieve suffering. At a minimum, our study identifies a need for further study to identify this very high‐risk group of elders. It is notable that 50% of patients were found to have a post‐treatment ECG with a QTc of >500 ms, a finding that has not been previously described. This would put these patients at higher risk of mortality, and as such we suggest that current guidelines should continue to emphasize the importance of post‐treatment ECGs and set clear criteria for discontinuation in elderly patients.

Our study is limited by its retrospective, single‐center design and small sample size, therefore limiting the interpretation and generalizability of the results to other hospitals. Quetiapine was the most common AP medication used in our hospital; therefore, our findings cannot be generalized to hospitals that utilize other AP agents. Future studies should examine antipsychotic use across hospitals to determine variation in prescribing patterns and outcomes. Nevertheless, the care of these patients were transitioned to a large number of geriatricians and primary care and nursing home physicians after discharge, and the reflected practice patterns extended beyond our hospital. Additionally, we were unable to determine when and why APs were discontinued or started in the outpatient setting. We were only able to detect readmissions to the 3 hospitals within our health system and therefore may have missed some readmissions to other institutions, although the majority of patients in our region tend to return to the same hospital. For patients who were not readmitted, we were also unable to identify whether they remained on the APs initiated during their index hospitalizations. Any retrospective study is limited by the difficulty of distinguishing delirium from the behavioral and psychiatric symptoms of dementia, but we identified delirium using standard terms described in previous literature.[10] We were unable to determine the types of delirium (hyperactive vs hypoactive) given that the documentations on behavioral symptoms were largely missing from the charts. The number of patients with preexisting diagnosis of dementia was likely underestimated, as we were only able to verify the diagnosis from the medical history. Additionally, the retrospective design based on chart review limited the factors that we could detect and grade accurately for inclusion in our mortality prediction model. Of note, our model did not contain objective measures of cognition, agitation, function, and markers for frailty such as walking speed, weak grip strength, weight loss, and low physical activity.

CONCLUSION

Initiating an AP (eg, haloperidol, quetiapine, olanzapine, and risperidone) in the hospital is likely to result in long‐term use of these medications despite the fact that AP use has been associated with multiple risks including falls, fractures, stroke, cardiovascular disease, and increased mortality in those with underlying dementia.[27] When possible, behavioral interventions to prevent delirium and slow the trajectory of decline should be implemented to reduce AP use. If patients with delirium are started on antipsychotics, it is important to monitor for prolonged QTc given the associated risk of mortality. In a subgroup of patients at high risk for death in the upcoming year, occurrence of delirium or use of APs during a hospitalization should both be considered triggers for early advance care planning and possibly palliative care and end‐of‐life discussions, with an emphasis on quality of life.

Disclosures: The research was supported by the Department of Medicine, Baystate Medical Center/Tufts University School of Medicine. Dr. Lagu is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Drs. Lagu and Loh had full access to all of the data in the study. They take responsibility for the integrity of the data and the accuracy of the analysis. Drs. Loh, Brennan, Lindenauer, and Lagu conceived of the study. Drs. Loh, Ramdass, and Ms. Garb acquired the data. Ms Garb analyzed and interpreted the data. Drs. Loh, Ramdass, and Thim drafted the manuscript. Drs. Brennan, Lindenauer, and Lagu and Ms. Garb critically reviewed the manuscript for important intellectual content. Dr. Lagu has received consulting fees from the Institute for Healthcare Improvement, under contract to the Centers for Medicare and Medicaid Services, for her work on a project to help health systems achieve disability competence. Dr. Brennan is supported by a Geriatric Work Force Enhancement Grant from the US Department of Health and Human Services award number 1 U1QHP287020100. The authors report no conflicts of interest.

Delirium, a clinical syndrome characterized by inattention and acute cognitive dysfunction, is very common in older hospitalized patients, with a reported incidence of 18% to 35% at time of admission and overall occurrence rates of 29% to 64%.[1] Previous studies have reported that a diagnosis of delirium is not benign and is associated with other adverse outcomes including prolonged hospitalization, institutionalization, increased cost, and mortality. These outcomes occurred independent of age, prior cognitive functioning, and comorbidities.[2] Guidelines recommend that management of inpatient delirium should be focused on addressing the underlying etiology and managed with nonpharmacological interventions whenever possible.[3, 4, 5] However, implementing these recommendations can prove to be very challenging in hospital settings. Providers frequently have to resort to medical therapies, including antipsychotics (APs). Although these medications are commonly used to treat delirium in elderly patients, there is limited evidence to support their efficacy, and there are currently no proven pharmacological alternatives to these medications.[6] Furthermore, previous studies have demonstrated an increased risk of stroke, infection, cognitive impairment, and mortality in elders with dementia who receive long‐term AP therapy.[7, 8, 9] Yet as many as 48% of hospitalized elders who were newly started on APs had these drugs continued at time of discharge.[10]

There have been few studies describing the long‐term outcomes of elderly patient who are started on APs in the hospital. Most information on outcomes comes from patients with dementia. Therefore, we studied the 1‐year outcomes of a cohort of patients with and without dementia who were started on APs in the hospital and then discharged on these medications. In this cohort, we aimed to describe the number of readmissions, reasons for readmissions, duration of AP therapy, use of other sedating medications such as anxiolytics, hypnotics, and antihistamines as well as the incidence of readmission and death 1 year after the index hospital discharge.

METHODS

We previously described a retrospective cohort of 300 elders (65 years old) admitted to a tertiary care hospital between October 1, 2012 and September 31, 2013 who were newly prescribed APs while hospitalized.[10] Of patients alive at the time of discharge (260), 56% (146 patients) were discharged on APs. Two investigators extracted these 148 patient charts independently to identify and quantify the number of readmissions to the index hospital. We then limited the sample to only the first readmission per patient following the index admission and extracted this readmission for each patient. We first determined if APs were present on the admission medication reconciliation. If APs were not present on admission, we examined whether they were resumed during the hospitalization using the electronic medication administration summary. If they were present on admission, we looked to see if they were discontinued during the readmission and if additional new APs were started during the hospitalizations. We documented the circumstances around APs use and identified patients who died during their hospitalizations. We identified delirium using the same terms that were described in our prior study on the same cohort of patients.[10] We determined if patients were delirious using a predetermined algorithm (Figure 1). Briefly, we first determined delirium was documented. We then examined whether there was a Confusion Assessment Method (CAM) instrument included in the record. If a CAM instrument was not documented, we then looked for documentation using specific terms (eg, disorientation, confusions). We identified patients with dementia by determining whether dementia was documented along with other admission medical comorbidities. If it was not, we determined whether dementia was newly diagnosed during the hospital stay using progress notes or consultation notes. We did not objectively define criteria for diagnosis of dementia. We used the National Death Index (NDI) to determine mortality for all patients 1 year after discharge from the index hospitalization. The NDI is a national database of death records maintained by the National Center for Health Statistics. It has shown consistently high sensitivity and specificity for detection of death.[11]

Figure 1

We used descriptive statistics (means, standard deviations, range, and percents as appropriate to the scale of measurement) to describe the patient sample. We then used multiple logistic regression to identify significant predictors of death within 1 year of discharge.[12] Univariate analysis was used to select candidates for the logistic model (t tests for continuous factors and ² for discrete factors). All factors with a significance level 0.2 on univariate analysis were included in the logistic regression, in addition to age and sex (regardless of significance). A maximum likelihood procedure was used to calculate the regression coefficients for the logistic model. The likelihood ratio criterion was used to determine the significance of individual factors in the regression model.[13] Factors with a significance level of 0.15 or less were retained in the final model, in addition to age and sex.

RESULTS

The 260 patients discharged alive from their index admissions had a 1‐year mortality rate of 29% (75/260). Of the 146/260 patients discharged on APs, 60 (41%) patients experienced at least 1 readmission (mean = 2 readmissions per patient; range, 18, with 111 total readmissions for 60 patients) within 1 year from discharge (Figure 2). Most common diagnoses at the time of readmissions were related neurological and psychiatric disorders (14%), cardiovascular and circulation disorders (13%), renal injury and electrolyte disorders (11%), and infections (6%). Among patients with at least 1 readmission, the mean age was 81.3 (range, 65.599.7), 60% were male, and 45% were admitted from a skilled nursing facility or rehabilitation facility (Table 1). Median time to readmission was 43.5 days (range, 1343 days), and 79% were readmitted to a medical service. The remaining 20% were admitted to a surgical service. Inpatient mortality during first readmissions was 8% (5/60). At the time of first readmission, 39/60 (65%) of patients were still on the same APs on which they had been discharged, and the APs were continued during the hospitalization in 79% of the patients (61% quetiapine, 19% olanzapine, and 13% risperidone). About half of patients whose APs were discontinued prior to readmission received a new AP during their hospital stays (9/20; 45%). One patient had been started on quetiapine in the outpatient setting. No patients were found to have new benzodiazepines, nonbenzodiazepine hypnotic, or antihistamines on their admission medication list.

Figure 2

Eighteen patients received 1 or more new APs during the readmission hospitalizations. These included haloperidol (89%) and quetiapine (39%). Delirium was the main reported indication for starting APs (78%), but in 17% of cases no indication was documented. An electrocardiogram (ECG) was performed in 94% prior to APs administration and for 22% after APs administration. Corrected QT interval (QTc) of >500 ms was present in 18% of patients in pretreatment ECG and 50% of patients in post‐AP ECG. Of patients who survived readmission, 58% (32/55) were discharged to postacute facilities. Of the 39 patients who were on the same APs from index admission, 27 (69%) patients were eventually discharged on the same APs or new APs started during the readmission.

In the multivariable model (Table 2), predictors of death at 1 year included discharge to postacute facilities after index admission (odds ratio [OR]: 2.28; 95% confidence interval [CI]: 1.10‐4.73, P = 0.03) and QTc prolongation >500 ms during index admission (OR: 3.41; 95% CI: 1.34‐8.67, P = 0.01). Age and gender were not associated with 1‐year mortality.

DISCUSSION

In a cohort of elderly patients who were discharged on APs, nearly one‐third (29%) died within 1 year of the hospitalization in which APs were initiated. Nearly half of the survivors from the index admission (41%) experienced at least 1 admission within 1 year from discharge. Of readmitted patients, two‐thirds were taking the same APs that had been started during the index hospitalization. Half of the patients not on APs on readmission were started on an AP during the hospitalization, most often because they became delirious on return to the acute care setting. Compared to patients discharged home after an index admission, patients who were discharged to postacute facilities were almost 4 times as likely to die during the year subsequent to the admission. These data suggest that once patients are started on APs, most are continued on them until the next admission or are restarted during that readmission. Moreover, hospitalized elders who require an AP are at high risk for mortality in the coming year.

Prior studies have reported that patients with delirium have elevated 1‐year mortality rates.[14, 15, 16, 17, 18, 19] A secondary analysis of the Delirium Prevention Trial, which included 437 hospitalized older patients, revealed a 1‐year mortality rate of 20% in those who were never delirious during hospitalization, compared to 26% to 38% in patients with delirium.[19] Additionally, 1‐year mortality in hospitalized older patients with delirium (36%) was shown to be higher than patients with dementia (29%) or depression (26%).[17] Unlike these studies, not all of the patients in our study had documented delirium, but all received an AP. Still, it is notable that the 1‐year mortality rate for delirium in general is similar to what we found in this study.

The literature has also reported that long‐term AP use is associated with excess mortality in elder patients, especially those with dementia.[20, 21, 22] In a retrospective cohort study, older patients with dementia who were taking antipsychotics had significantly higher 1‐year mortality rates (23%29%) than patients not taking antipsychotic medications (15%). In a large Canadian propensity score‐matched cohort study that included over 13,000 demented older adults, the mortality was higher in the community‐dwelling elders who received atypical APs compared to no APs, with a difference of 1.1% in 180‐day mortality rate after initiation of APs.[21] The absolute mortality rate was 2.6% higher in patients who received typical compared to atypical APs. Unlike these studies, not every patient in our cohort had a diagnosis of dementia, but again, mortality rates in these studies appear similar to our cohort.

In contrast, other observational studies have not found an increased risk associated with receipt of APs. For example, a prospective study that enrolled approximately 950 patients with probable dementia showed that AP use was not associated with time to death after adjustment for comorbidities, demographic and cognitive variables.[23] These conflicting results highlight the difficulties of attributing outcomes in high‐risk populations. Although the excess mortality observed in patients taking APs may be related to the risks of APs, it is quite possible that patients who require APs (most often for delirium or agitated dementia) are at higher risk of death. This confounding by indication may be nearly impossible to adjust for retrospectively, even using techniques such as propensity matching.

Our report adds to the literature; we know of no studies to date describing a cohort of patients, most with delirium, who were started on APs in the hospital. We also attempted to identify the reasons that patients were started on APs, which have been infrequently reported. As noted above, our 1‐year mortality rate of 29% among older patients prescribed APs in the hospital was quite similar to mortality rates both for patients with delirium who were not necessarily treated with APs and patients with dementia who were treated with APs. This finding further supports the argument that risk factors for mortality, including dementia, delirium, and AP use are very difficult to tease apart. It is possible that the reasons that APs are prescribed (agitated delirium or dementia) have as much to do with the excess mortality reported in observational studies of APs as the use of APs themselves.

The high rate of continued AP use we observed (two‐thirds of readmitted patients) may reflect limited pharmacological alternatives to these medications with little evidence to support treating the symptoms of delirium with other drug classes, along with suboptimal environmental and behavioral modifications in postacute facilities and hospitals. This is unfortunate given that delirium is often preventable. Systematic implementation of well‐documented strategies to decrease delirium in hospitals and postacute facilities would likely reduce the prescription of APs and has the potential to slow the decline in this vulnerable population. A meta‐analysis incorporating both randomized and nonrandomized trials of medical and surgical patients showed that multicomponent nonpharmacologic interventions decreased delirium by 50%.[24] Thus, simple interventions such as reorientation, early mobilization, optimizing vision and hearing, sleepwake cycle preservation, and hydration might avoid roughly 1 million cases of delirium in hospitalized older adults annually.[24] The Hospital Elder Life Program and Acute Care for Elders units are examples of programs that have been shown to decrease the incidence of delirium.[25, 26]

Despite vigorous efforts to prevent delirium, a subgroup of patients still will become delirious. These patients are at high risk for death. Our mortality prediction model revealed that patients who were discharged to postacute facilities were 4 times more likely to die during the subsequent year compared to patients who were discharged home. Patients discharged to postacute facilities are likely to have a higher burden of disease, greater functional and cognitive impairment, and more frailty than those who are able to return to the community. Very ill and/or frail patients receiving APs in the hospital and requiring APs on discharge to postacute care facilities have limited survival and may benefit from expedited palliative care interventions to clarify prognosis and goals, and relieve suffering. At a minimum, our study identifies a need for further study to identify this very high‐risk group of elders. It is notable that 50% of patients were found to have a post‐treatment ECG with a QTc of >500 ms, a finding that has not been previously described. This would put these patients at higher risk of mortality, and as such we suggest that current guidelines should continue to emphasize the importance of post‐treatment ECGs and set clear criteria for discontinuation in elderly patients.

Our study is limited by its retrospective, single‐center design and small sample size, therefore limiting the interpretation and generalizability of the results to other hospitals. Quetiapine was the most common AP medication used in our hospital; therefore, our findings cannot be generalized to hospitals that utilize other AP agents. Future studies should examine antipsychotic use across hospitals to determine variation in prescribing patterns and outcomes. Nevertheless, the care of these patients were transitioned to a large number of geriatricians and primary care and nursing home physicians after discharge, and the reflected practice patterns extended beyond our hospital. Additionally, we were unable to determine when and why APs were discontinued or started in the outpatient setting. We were only able to detect readmissions to the 3 hospitals within our health system and therefore may have missed some readmissions to other institutions, although the majority of patients in our region tend to return to the same hospital. For patients who were not readmitted, we were also unable to identify whether they remained on the APs initiated during their index hospitalizations. Any retrospective study is limited by the difficulty of distinguishing delirium from the behavioral and psychiatric symptoms of dementia, but we identified delirium using standard terms described in previous literature.[10] We were unable to determine the types of delirium (hyperactive vs hypoactive) given that the documentations on behavioral symptoms were largely missing from the charts. The number of patients with preexisting diagnosis of dementia was likely underestimated, as we were only able to verify the diagnosis from the medical history. Additionally, the retrospective design based on chart review limited the factors that we could detect and grade accurately for inclusion in our mortality prediction model. Of note, our model did not contain objective measures of cognition, agitation, function, and markers for frailty such as walking speed, weak grip strength, weight loss, and low physical activity.

CONCLUSION

Initiating an AP (eg, haloperidol, quetiapine, olanzapine, and risperidone) in the hospital is likely to result in long‐term use of these medications despite the fact that AP use has been associated with multiple risks including falls, fractures, stroke, cardiovascular disease, and increased mortality in those with underlying dementia.[27] When possible, behavioral interventions to prevent delirium and slow the trajectory of decline should be implemented to reduce AP use. If patients with delirium are started on antipsychotics, it is important to monitor for prolonged QTc given the associated risk of mortality. In a subgroup of patients at high risk for death in the upcoming year, occurrence of delirium or use of APs during a hospitalization should both be considered triggers for early advance care planning and possibly palliative care and end‐of‐life discussions, with an emphasis on quality of life.

Disclosures: The research was supported by the Department of Medicine, Baystate Medical Center/Tufts University School of Medicine. Dr. Lagu is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Drs. Lagu and Loh had full access to all of the data in the study. They take responsibility for the integrity of the data and the accuracy of the analysis. Drs. Loh, Brennan, Lindenauer, and Lagu conceived of the study. Drs. Loh, Ramdass, and Ms. Garb acquired the data. Ms Garb analyzed and interpreted the data. Drs. Loh, Ramdass, and Thim drafted the manuscript. Drs. Brennan, Lindenauer, and Lagu and Ms. Garb critically reviewed the manuscript for important intellectual content. Dr. Lagu has received consulting fees from the Institute for Healthcare Improvement, under contract to the Centers for Medicare and Medicaid Services, for her work on a project to help health systems achieve disability competence. Dr. Brennan is supported by a Geriatric Work Force Enhancement Grant from the US Department of Health and Human Services award number 1 U1QHP287020100. The authors report no conflicts of interest.

Delirium, a clinical syndrome characterized by inattention and acute cognitive dysfunction, is very common in older hospitalized patients, with a reported incidence of 18% to 35% at time of admission and overall occurrence rates of 29% to 64%.[1] Previous studies have reported that a diagnosis of delirium is not benign and is associated with other adverse outcomes including prolonged hospitalization, institutionalization, increased cost, and mortality. These outcomes occurred independent of age, prior cognitive functioning, and comorbidities.[2] Guidelines recommend that management of inpatient delirium should be focused on addressing the underlying etiology and managed with nonpharmacological interventions whenever possible.[3, 4, 5] However, implementing these recommendations can prove to be very challenging in hospital settings. Providers frequently have to resort to medical therapies, including antipsychotics (APs). Although these medications are commonly used to treat delirium in elderly patients, there is limited evidence to support their efficacy, and there are currently no proven pharmacological alternatives to these medications.[6] Furthermore, previous studies have demonstrated an increased risk of stroke, infection, cognitive impairment, and mortality in elders with dementia who receive long‐term AP therapy.[7, 8, 9] Yet as many as 48% of hospitalized elders who were newly started on APs had these drugs continued at time of discharge.[10]

There have been few studies describing the long‐term outcomes of elderly patient who are started on APs in the hospital. Most information on outcomes comes from patients with dementia. Therefore, we studied the 1‐year outcomes of a cohort of patients with and without dementia who were started on APs in the hospital and then discharged on these medications. In this cohort, we aimed to describe the number of readmissions, reasons for readmissions, duration of AP therapy, use of other sedating medications such as anxiolytics, hypnotics, and antihistamines as well as the incidence of readmission and death 1 year after the index hospital discharge.

METHODS

We previously described a retrospective cohort of 300 elders (65 years old) admitted to a tertiary care hospital between October 1, 2012 and September 31, 2013 who were newly prescribed APs while hospitalized.[10] Of patients alive at the time of discharge (260), 56% (146 patients) were discharged on APs. Two investigators extracted these 148 patient charts independently to identify and quantify the number of readmissions to the index hospital. We then limited the sample to only the first readmission per patient following the index admission and extracted this readmission for each patient. We first determined if APs were present on the admission medication reconciliation. If APs were not present on admission, we examined whether they were resumed during the hospitalization using the electronic medication administration summary. If they were present on admission, we looked to see if they were discontinued during the readmission and if additional new APs were started during the hospitalizations. We documented the circumstances around APs use and identified patients who died during their hospitalizations. We identified delirium using the same terms that were described in our prior study on the same cohort of patients.[10] We determined if patients were delirious using a predetermined algorithm (Figure 1). Briefly, we first determined delirium was documented. We then examined whether there was a Confusion Assessment Method (CAM) instrument included in the record. If a CAM instrument was not documented, we then looked for documentation using specific terms (eg, disorientation, confusions). We identified patients with dementia by determining whether dementia was documented along with other admission medical comorbidities. If it was not, we determined whether dementia was newly diagnosed during the hospital stay using progress notes or consultation notes. We did not objectively define criteria for diagnosis of dementia. We used the National Death Index (NDI) to determine mortality for all patients 1 year after discharge from the index hospitalization. The NDI is a national database of death records maintained by the National Center for Health Statistics. It has shown consistently high sensitivity and specificity for detection of death.[11]

Figure 1

We used descriptive statistics (means, standard deviations, range, and percents as appropriate to the scale of measurement) to describe the patient sample. We then used multiple logistic regression to identify significant predictors of death within 1 year of discharge.[12] Univariate analysis was used to select candidates for the logistic model (t tests for continuous factors and ² for discrete factors). All factors with a significance level 0.2 on univariate analysis were included in the logistic regression, in addition to age and sex (regardless of significance). A maximum likelihood procedure was used to calculate the regression coefficients for the logistic model. The likelihood ratio criterion was used to determine the significance of individual factors in the regression model.[13] Factors with a significance level of 0.15 or less were retained in the final model, in addition to age and sex.

RESULTS

The 260 patients discharged alive from their index admissions had a 1‐year mortality rate of 29% (75/260). Of the 146/260 patients discharged on APs, 60 (41%) patients experienced at least 1 readmission (mean = 2 readmissions per patient; range, 18, with 111 total readmissions for 60 patients) within 1 year from discharge (Figure 2). Most common diagnoses at the time of readmissions were related neurological and psychiatric disorders (14%), cardiovascular and circulation disorders (13%), renal injury and electrolyte disorders (11%), and infections (6%). Among patients with at least 1 readmission, the mean age was 81.3 (range, 65.599.7), 60% were male, and 45% were admitted from a skilled nursing facility or rehabilitation facility (Table 1). Median time to readmission was 43.5 days (range, 1343 days), and 79% were readmitted to a medical service. The remaining 20% were admitted to a surgical service. Inpatient mortality during first readmissions was 8% (5/60). At the time of first readmission, 39/60 (65%) of patients were still on the same APs on which they had been discharged, and the APs were continued during the hospitalization in 79% of the patients (61% quetiapine, 19% olanzapine, and 13% risperidone). About half of patients whose APs were discontinued prior to readmission received a new AP during their hospital stays (9/20; 45%). One patient had been started on quetiapine in the outpatient setting. No patients were found to have new benzodiazepines, nonbenzodiazepine hypnotic, or antihistamines on their admission medication list.

Figure 2

Eighteen patients received 1 or more new APs during the readmission hospitalizations. These included haloperidol (89%) and quetiapine (39%). Delirium was the main reported indication for starting APs (78%), but in 17% of cases no indication was documented. An electrocardiogram (ECG) was performed in 94% prior to APs administration and for 22% after APs administration. Corrected QT interval (QTc) of >500 ms was present in 18% of patients in pretreatment ECG and 50% of patients in post‐AP ECG. Of patients who survived readmission, 58% (32/55) were discharged to postacute facilities. Of the 39 patients who were on the same APs from index admission, 27 (69%) patients were eventually discharged on the same APs or new APs started during the readmission.

In the multivariable model (Table 2), predictors of death at 1 year included discharge to postacute facilities after index admission (odds ratio [OR]: 2.28; 95% confidence interval [CI]: 1.10‐4.73, P = 0.03) and QTc prolongation >500 ms during index admission (OR: 3.41; 95% CI: 1.34‐8.67, P = 0.01). Age and gender were not associated with 1‐year mortality.

DISCUSSION

In a cohort of elderly patients who were discharged on APs, nearly one‐third (29%) died within 1 year of the hospitalization in which APs were initiated. Nearly half of the survivors from the index admission (41%) experienced at least 1 admission within 1 year from discharge. Of readmitted patients, two‐thirds were taking the same APs that had been started during the index hospitalization. Half of the patients not on APs on readmission were started on an AP during the hospitalization, most often because they became delirious on return to the acute care setting. Compared to patients discharged home after an index admission, patients who were discharged to postacute facilities were almost 4 times as likely to die during the year subsequent to the admission. These data suggest that once patients are started on APs, most are continued on them until the next admission or are restarted during that readmission. Moreover, hospitalized elders who require an AP are at high risk for mortality in the coming year.

Prior studies have reported that patients with delirium have elevated 1‐year mortality rates.[14, 15, 16, 17, 18, 19] A secondary analysis of the Delirium Prevention Trial, which included 437 hospitalized older patients, revealed a 1‐year mortality rate of 20% in those who were never delirious during hospitalization, compared to 26% to 38% in patients with delirium.[19] Additionally, 1‐year mortality in hospitalized older patients with delirium (36%) was shown to be higher than patients with dementia (29%) or depression (26%).[17] Unlike these studies, not all of the patients in our study had documented delirium, but all received an AP. Still, it is notable that the 1‐year mortality rate for delirium in general is similar to what we found in this study.

The literature has also reported that long‐term AP use is associated with excess mortality in elder patients, especially those with dementia.[20, 21, 22] In a retrospective cohort study, older patients with dementia who were taking antipsychotics had significantly higher 1‐year mortality rates (23%29%) than patients not taking antipsychotic medications (15%). In a large Canadian propensity score‐matched cohort study that included over 13,000 demented older adults, the mortality was higher in the community‐dwelling elders who received atypical APs compared to no APs, with a difference of 1.1% in 180‐day mortality rate after initiation of APs.[21] The absolute mortality rate was 2.6% higher in patients who received typical compared to atypical APs. Unlike these studies, not every patient in our cohort had a diagnosis of dementia, but again, mortality rates in these studies appear similar to our cohort.

In contrast, other observational studies have not found an increased risk associated with receipt of APs. For example, a prospective study that enrolled approximately 950 patients with probable dementia showed that AP use was not associated with time to death after adjustment for comorbidities, demographic and cognitive variables.[23] These conflicting results highlight the difficulties of attributing outcomes in high‐risk populations. Although the excess mortality observed in patients taking APs may be related to the risks of APs, it is quite possible that patients who require APs (most often for delirium or agitated dementia) are at higher risk of death. This confounding by indication may be nearly impossible to adjust for retrospectively, even using techniques such as propensity matching.

Our report adds to the literature; we know of no studies to date describing a cohort of patients, most with delirium, who were started on APs in the hospital. We also attempted to identify the reasons that patients were started on APs, which have been infrequently reported. As noted above, our 1‐year mortality rate of 29% among older patients prescribed APs in the hospital was quite similar to mortality rates both for patients with delirium who were not necessarily treated with APs and patients with dementia who were treated with APs. This finding further supports the argument that risk factors for mortality, including dementia, delirium, and AP use are very difficult to tease apart. It is possible that the reasons that APs are prescribed (agitated delirium or dementia) have as much to do with the excess mortality reported in observational studies of APs as the use of APs themselves.

The high rate of continued AP use we observed (two‐thirds of readmitted patients) may reflect limited pharmacological alternatives to these medications with little evidence to support treating the symptoms of delirium with other drug classes, along with suboptimal environmental and behavioral modifications in postacute facilities and hospitals. This is unfortunate given that delirium is often preventable. Systematic implementation of well‐documented strategies to decrease delirium in hospitals and postacute facilities would likely reduce the prescription of APs and has the potential to slow the decline in this vulnerable population. A meta‐analysis incorporating both randomized and nonrandomized trials of medical and surgical patients showed that multicomponent nonpharmacologic interventions decreased delirium by 50%.[24] Thus, simple interventions such as reorientation, early mobilization, optimizing vision and hearing, sleepwake cycle preservation, and hydration might avoid roughly 1 million cases of delirium in hospitalized older adults annually.[24] The Hospital Elder Life Program and Acute Care for Elders units are examples of programs that have been shown to decrease the incidence of delirium.[25, 26]

Despite vigorous efforts to prevent delirium, a subgroup of patients still will become delirious. These patients are at high risk for death. Our mortality prediction model revealed that patients who were discharged to postacute facilities were 4 times more likely to die during the subsequent year compared to patients who were discharged home. Patients discharged to postacute facilities are likely to have a higher burden of disease, greater functional and cognitive impairment, and more frailty than those who are able to return to the community. Very ill and/or frail patients receiving APs in the hospital and requiring APs on discharge to postacute care facilities have limited survival and may benefit from expedited palliative care interventions to clarify prognosis and goals, and relieve suffering. At a minimum, our study identifies a need for further study to identify this very high‐risk group of elders. It is notable that 50% of patients were found to have a post‐treatment ECG with a QTc of >500 ms, a finding that has not been previously described. This would put these patients at higher risk of mortality, and as such we suggest that current guidelines should continue to emphasize the importance of post‐treatment ECGs and set clear criteria for discontinuation in elderly patients.

Our study is limited by its retrospective, single‐center design and small sample size, therefore limiting the interpretation and generalizability of the results to other hospitals. Quetiapine was the most common AP medication used in our hospital; therefore, our findings cannot be generalized to hospitals that utilize other AP agents. Future studies should examine antipsychotic use across hospitals to determine variation in prescribing patterns and outcomes. Nevertheless, the care of these patients were transitioned to a large number of geriatricians and primary care and nursing home physicians after discharge, and the reflected practice patterns extended beyond our hospital. Additionally, we were unable to determine when and why APs were discontinued or started in the outpatient setting. We were only able to detect readmissions to the 3 hospitals within our health system and therefore may have missed some readmissions to other institutions, although the majority of patients in our region tend to return to the same hospital. For patients who were not readmitted, we were also unable to identify whether they remained on the APs initiated during their index hospitalizations. Any retrospective study is limited by the difficulty of distinguishing delirium from the behavioral and psychiatric symptoms of dementia, but we identified delirium using standard terms described in previous literature.[10] We were unable to determine the types of delirium (hyperactive vs hypoactive) given that the documentations on behavioral symptoms were largely missing from the charts. The number of patients with preexisting diagnosis of dementia was likely underestimated, as we were only able to verify the diagnosis from the medical history. Additionally, the retrospective design based on chart review limited the factors that we could detect and grade accurately for inclusion in our mortality prediction model. Of note, our model did not contain objective measures of cognition, agitation, function, and markers for frailty such as walking speed, weak grip strength, weight loss, and low physical activity.

CONCLUSION

Initiating an AP (eg, haloperidol, quetiapine, olanzapine, and risperidone) in the hospital is likely to result in long‐term use of these medications despite the fact that AP use has been associated with multiple risks including falls, fractures, stroke, cardiovascular disease, and increased mortality in those with underlying dementia.[27] When possible, behavioral interventions to prevent delirium and slow the trajectory of decline should be implemented to reduce AP use. If patients with delirium are started on antipsychotics, it is important to monitor for prolonged QTc given the associated risk of mortality. In a subgroup of patients at high risk for death in the upcoming year, occurrence of delirium or use of APs during a hospitalization should both be considered triggers for early advance care planning and possibly palliative care and end‐of‐life discussions, with an emphasis on quality of life.

Disclosures: The research was supported by the Department of Medicine, Baystate Medical Center/Tufts University School of Medicine. Dr. Lagu is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Drs. Lagu and Loh had full access to all of the data in the study. They take responsibility for the integrity of the data and the accuracy of the analysis. Drs. Loh, Brennan, Lindenauer, and Lagu conceived of the study. Drs. Loh, Ramdass, and Ms. Garb acquired the data. Ms Garb analyzed and interpreted the data. Drs. Loh, Ramdass, and Thim drafted the manuscript. Drs. Brennan, Lindenauer, and Lagu and Ms. Garb critically reviewed the manuscript for important intellectual content. Dr. Lagu has received consulting fees from the Institute for Healthcare Improvement, under contract to the Centers for Medicare and Medicaid Services, for her work on a project to help health systems achieve disability competence. Dr. Brennan is supported by a Geriatric Work Force Enhancement Grant from the US Department of Health and Human Services award number 1 U1QHP287020100. The authors report no conflicts of interest.