Arpana Vidyarthi, MD

Enactment of federal legislation imposing hospital reimbursement penalties for excess rates of rehospitalizations among Medicare fee for service beneficiaries markedly increased interest in hospital quality improvement (QI) efforts to reduce the observed 30‐day rehospitalization of 19.6% in this elderly population.[1, 2] The Congressional Budget Office estimated that reimbursement penalties to hospitals for high readmission rates are expected to save the Medicare program approximately $7 billion between 2010 and 2019.[3] These penalties are complemented by resources from the Center for Medicare and Medicaid Innovation aiming to reduce hospital readmissions by 20% by the end of 2013 through the Partnership for Patients campaign.[4] Although potential financial penalties and provision of resources for QI intensified efforts to enhance the quality of the hospital discharge transition, patient safety risks associated with hospital discharge are well documented.[5, 6] Approximately 20% of patients discharged from the hospital may suffer adverse events,[7, 8] of which up to three‐quarters (72%) are medication related,[9] and over one‐third of required follow‐up testing after discharge is not completed.[10] Such findings indicate opportunities for improvement in the discharge process.[11]

Numerous publications describe studies aiming to improve the hospital discharge process and mitigate these hazards, though a systematic review of interventions to reduce 30‐day rehospitalization indicated that the existing evidence base for the effectiveness of transition interventions demonstrates irregular effectiveness and limitations to generalizability.[12] Most studies showing effectiveness are confined to single academic medical centers. Existing evidence supports multifaceted interventions implemented in both the pre‐ and postdischarge periods and focused on risk assessment and tailored, patient‐centered application of interventions to mitigate risk. For example Project RED (Re‐Engineered Discharge) applied a bundled intervention consisting of intensified patient education and discharge planning, improved medication reconciliation and discharge instructions, and longitudinal patient contact with follow‐up phone calls and a dedicated discharge advocate.[13] However, the mean age of patients participating in the study was 50 years, and it excluded patients admitted from or discharged to skilled nursing facilities, making generalizability to the geriatric population uncertain.

An integral aspect of QI projects is the contribution of local context to translation of best practices to disparate settings.[14, 15, 16] Most available reports of successful interventions to reduce rehospitalization have not fully described the specifics of either the intervention context or design. Moreover, the available evidence base for common interventions to reduce rehospitalization was developed in the academic setting. Validation of single academic center studies in a broader healthcare context is necessary.

Project BOOST (Better Outcomes for Older adults through Safe Transitions) recruited a diverse national cohort of both academic and nonacademic hospitals to participate in a QI effort to implement best practices for hospital discharge care transitions using a national collaborative approach facilitated by external expert mentorship. This study aimed to determine the effectiveness of BOOST in lowering hospital readmission rates and impact on length of stay.

METHODS

The study of Project BOOST was undertaken in accordance with the SQUIRE (Standards for Quality Improvement Reporting Excellence) Guidelines.[17]

RESULTS

Eleven hospitals provided rehospitalization and length‐of‐stay outcome data for both a BOOST and control unit for the pre‐ and postimplementation periods. Compared to the 19 sites that did not participate in the analysis, these 11 sites were significantly larger (559188 beds vs 350205 beds, P=0.003), more likely to be located in an urban area (100.0% [n=11] vs 78.9% [n=15], P=0.035), and more likely to be academic medical centers (45.5% [n=5] vs 26.3% [n=5], P=0.036) (Table 1).

The mean number of tools implemented by sites participating in the analysis was 3.50.9. All sites implemented at least 2 tools. The duration between attendance at the BOOST kickoff event and first tool implementation ranged from 3 months (first tool implemented prior to attending the kickoff) and 9 months (mean duration, 3.34.3 months) (Table 2).

The average rate of 30‐day rehospitalization among BOOST units was 14.7% in the preimplementation period and 12.7% during the postimplementation period (P=0.010) (Figure 1). Rehospitalization rates for matched control units were 14.0% in the preintervention period and 14.1% in the postintervention period (P=0.831). The mean absolute reduction in readmission rates over the 1‐year study period in BOOST units compared to control units was 2.0%, or a relative reduction of 13.6% (P=0.054 for signed rank test comparing differences in readmission rate reduction in BOOST units compared to site‐matched control units). Length of stay in BOOST and control units decreased an average of 0.5 days and 0.3 days, respectively. There was no difference in length of stay change between BOOST units and control units (P=0.966).

Figure 1

DISCUSSION

As hospitals strive to reduce their readmission rates to avoid Centers for Medicare and Medicaid Services penalties, Project BOOST may be a viable QI approach to achieve their goals. This initial evaluation of participation in Project BOOST by 11 hospitals of varying sizes across the United States showed an associated reduction in rehospitalization rates (absolute=2.0% and relative=13.6%, P=0.054). We did not find any significant change in length of stay among these hospitals implementing BOOST tools.

The tools provided to participating hospitals were developed from evidence found in peer‐reviewed literature established through experimental methods in well‐controlled academic settings. Further tool development was informed by recommendations of an advisory board consisting of expert representatives and advocates involved in the hospital discharge process: patients, caregivers, physicians, nurses, case managers, social workers, insurers, and regulatory and research agencies.[19] The toolkit components address multiple aspects of hospital discharge and follow‐up with the goal of improving health by optimizing the safety of care transitions. Our observation that readmission rates appeared to improve in a diverse hospital sample including nonacademic and community hospitals engaged in Project BOOST is reassuring that the benefits seen in existing research literature, developed in distinctly academic settings, can be replicated in diverse acute‐care settings.

The effect size observed in our study was modest but consistent with several studies identified in a recent review of trials measuring interventions to reduce rehospitalization, where 7 of 16 studies showing a significant improvement registered change in the 0% to 5% absolute range.[12] Impact of this project may have been tempered by the need to translate external QI content to the local setting. Additionally, in contrast to experimental studies that are limited in scope and timing and often scaled to a research budget, BOOST sites were encouraged to implement Project BOOST in the clinical setting even if no new funds were available to support the effort.[12]

The recruitment of a national sample of both academic and nonacademic hospital participants imposed several limitations on our study and analysis. We recognize that intervention units selected by hospitals may have had unmeasured unit and patient characteristics that facilitated successful change and contributed to the observed improvements. However, because external pressure to reduce readmission is present across all hospitals independent of the BOOST intervention, we felt site‐matched controls were essential to understanding effects attributable to the BOOST tools. Differences between units would be expected to be stable over the course of the study period, and comparison of outcome differences between 2 different time periods would be reasonable. Additionally, we could not collect data on readmissions to other hospitals. Theoretically, patients discharged from BOOST units might be more likely to have been rehospitalized elsewhere, but the fraction of rehospitalizations occurring at alternate facilities would also be expected to be similar on the matched control unit.

We report findings from a voluntary cohort willing and capable of designating a comparison clinical unit and contributing the requested data outcomes. Pilot sites that did not report outcomes were not analyzed, but comparison of hospital characteristics shows that participating hospitals were more likely to be large, urban, academic medical centers. Although barriers to data submission were not formally analyzed, reports from nonparticipating sites describe data submission limited by local implementation design (no geographic rollout or simultaneous rollout on all appropriate clinical units), site specific inability to generate unit level outcome statistics, and competing organizational priorities for data analyst time (electronic medical record deployment, alternative QI initiatives). The external validity of our results may be limited to organizations capable of analytics at the level of the individual clinical unit as well as those with sufficient QI resources to support reporting to a national database in the absence of a payer mandate. It is possible that additional financial support for on‐site data collection would have bolstered participation, making the example of participation rates we present potentially informative to organizations hoping to widely disseminate a QI agenda.

Nonetheless, the effectiveness demonstrated in the 11 sites that did participate is encouraging, and ongoing collaboration with subsequent BOOST cohorts has been designed to further facilitate data collection. Among the insights gained from this pilot experience, and incorporated into ongoing BOOST cohorts, is the importance of intensive mentor engagement to foster accountability among participant sites, assist with implementation troubleshooting, and offer expertise that is often particularly effective in gaining local support. We now encourage sites to have 2 mentor site visits to further these roles and more frequent conference calls. Further research to understand the marginal benefit of the mentored implementation approach is ongoing.

The limitations in data submission we experienced with the pilot cohort likely reflect resource constraints not uncommon at many hospitals. Increasing pressure placed on hospitals as a result of the Readmission Reduction Program within the Affordable Care Act as well as increasing interest from private and Medicaid payors to incorporate similar readmission‐based penalties provide encouragement for hospitals to enhance their data and analytic skills. National incentives for implementation of electronic health records (EHR) should also foster such capabilities, though we often saw EHRs as a barrier to QI, especially rapid cycle trials. Fortunately, hospitals are increasingly being afforded access to comprehensive claims databases to assist in tracking readmission rates to other facilities, and these data are becoming available in a more timely fashion. This more robust data collection, facilitated by private payors, state QI organizations, and state hospital associations, will support additional analytic methods such as multivariate regression models and interrupted time series designs to appreciate the experience of current BOOST participants.

Additional research is needed to understand the role of organizational context in the effectiveness of Project BOOST. Differences in rates of tool implementation and changes in clinical outcomes are likely dependent on local implementation context at the level of the healthcare organization and individual clinical unit.[20] Progress reports from site mentors and previously described experiences of QI implementation indicate that successful implementation of a multidimensional bundle of interventions may have reflected a higher level of institutional support, more robust team engagement in the work of reducing readmissions, increased clinical staff support for change, the presence of an effective project champion, or a key facilitating role of external mentorship.[21, 22] Ongoing data collection will continue to measure the sustainability of tool use and observed outcome changes to inform strategies to maintain gains associated with implementation. The role of mentored implementation in facilitating gains also requires further study.

Increasing attention to the problem of avoidable rehospitalization is driving hospitals, insurers, and policy makers to pursue QI efforts that favorably impact readmission rates. Our analysis of the BOOST intervention suggests that modest gains can be achieved following evidence‐based hospital process change facilitated by a mentored implementation model. However, realization of the goal of a 20% reduction in rehospitalization proposed by the Center for Medicare and Medicaid Services' Partnership for Patients initiative may be difficult to achieve on a national scale,[23] especially if efforts focus on just the hospital.

Disclosures

Project BOOST was funded by a grant from The John A. Hartford Foundation. Project BOOST is administered by the Society of Hospital Medicine (SHM). The development of the Project BOOST toolkit, recruitment of sites for this study, mentorship of the pilot cohort, project evaluation planning, and collection of pilot data were funded by a grant from The John A. Harford Foundation. Additional funding for continued data collection and analysis was funded by the SHM through funds from hospitals to participate in Project BOOST, specifically with funding support for Dr. Hansen. Dr. Williams has received funding to serve as Principal Investigator for Project BOOST. Since the time of initial cohort participation, approximately 125 additional hospitals have participated in the mentored implementation of Project BOOST. This participation was funded through a combination of site‐based tuition, third‐party payor support from private insurers, foundations, and federal funding through the Center for Medicare and Medicaid Innovation Partnership for Patients program. Drs. Greenwald, Hansen, and Williams are Project BOOST mentors for current Project BOOST sites and receive financial support through the SHM for this work. Dr. Howell has previously received funding as a Project BOOST mentor. Ms. Budnitz is the BOOST Project Director and is Chief Strategy and Development Officer for the HM. Dr. Maynard is the Senior Vice President of the SHM's Center for Hospital Innovation and Improvement.

Enactment of federal legislation imposing hospital reimbursement penalties for excess rates of rehospitalizations among Medicare fee for service beneficiaries markedly increased interest in hospital quality improvement (QI) efforts to reduce the observed 30‐day rehospitalization of 19.6% in this elderly population.[1, 2] The Congressional Budget Office estimated that reimbursement penalties to hospitals for high readmission rates are expected to save the Medicare program approximately $7 billion between 2010 and 2019.[3] These penalties are complemented by resources from the Center for Medicare and Medicaid Innovation aiming to reduce hospital readmissions by 20% by the end of 2013 through the Partnership for Patients campaign.[4] Although potential financial penalties and provision of resources for QI intensified efforts to enhance the quality of the hospital discharge transition, patient safety risks associated with hospital discharge are well documented.[5, 6] Approximately 20% of patients discharged from the hospital may suffer adverse events,[7, 8] of which up to three‐quarters (72%) are medication related,[9] and over one‐third of required follow‐up testing after discharge is not completed.[10] Such findings indicate opportunities for improvement in the discharge process.[11]

Numerous publications describe studies aiming to improve the hospital discharge process and mitigate these hazards, though a systematic review of interventions to reduce 30‐day rehospitalization indicated that the existing evidence base for the effectiveness of transition interventions demonstrates irregular effectiveness and limitations to generalizability.[12] Most studies showing effectiveness are confined to single academic medical centers. Existing evidence supports multifaceted interventions implemented in both the pre‐ and postdischarge periods and focused on risk assessment and tailored, patient‐centered application of interventions to mitigate risk. For example Project RED (Re‐Engineered Discharge) applied a bundled intervention consisting of intensified patient education and discharge planning, improved medication reconciliation and discharge instructions, and longitudinal patient contact with follow‐up phone calls and a dedicated discharge advocate.[13] However, the mean age of patients participating in the study was 50 years, and it excluded patients admitted from or discharged to skilled nursing facilities, making generalizability to the geriatric population uncertain.

An integral aspect of QI projects is the contribution of local context to translation of best practices to disparate settings.[14, 15, 16] Most available reports of successful interventions to reduce rehospitalization have not fully described the specifics of either the intervention context or design. Moreover, the available evidence base for common interventions to reduce rehospitalization was developed in the academic setting. Validation of single academic center studies in a broader healthcare context is necessary.

Project BOOST (Better Outcomes for Older adults through Safe Transitions) recruited a diverse national cohort of both academic and nonacademic hospitals to participate in a QI effort to implement best practices for hospital discharge care transitions using a national collaborative approach facilitated by external expert mentorship. This study aimed to determine the effectiveness of BOOST in lowering hospital readmission rates and impact on length of stay.

METHODS

The study of Project BOOST was undertaken in accordance with the SQUIRE (Standards for Quality Improvement Reporting Excellence) Guidelines.[17]

RESULTS

Eleven hospitals provided rehospitalization and length‐of‐stay outcome data for both a BOOST and control unit for the pre‐ and postimplementation periods. Compared to the 19 sites that did not participate in the analysis, these 11 sites were significantly larger (559188 beds vs 350205 beds, P=0.003), more likely to be located in an urban area (100.0% [n=11] vs 78.9% [n=15], P=0.035), and more likely to be academic medical centers (45.5% [n=5] vs 26.3% [n=5], P=0.036) (Table 1).

The mean number of tools implemented by sites participating in the analysis was 3.50.9. All sites implemented at least 2 tools. The duration between attendance at the BOOST kickoff event and first tool implementation ranged from 3 months (first tool implemented prior to attending the kickoff) and 9 months (mean duration, 3.34.3 months) (Table 2).

The average rate of 30‐day rehospitalization among BOOST units was 14.7% in the preimplementation period and 12.7% during the postimplementation period (P=0.010) (Figure 1). Rehospitalization rates for matched control units were 14.0% in the preintervention period and 14.1% in the postintervention period (P=0.831). The mean absolute reduction in readmission rates over the 1‐year study period in BOOST units compared to control units was 2.0%, or a relative reduction of 13.6% (P=0.054 for signed rank test comparing differences in readmission rate reduction in BOOST units compared to site‐matched control units). Length of stay in BOOST and control units decreased an average of 0.5 days and 0.3 days, respectively. There was no difference in length of stay change between BOOST units and control units (P=0.966).

Figure 1

DISCUSSION

As hospitals strive to reduce their readmission rates to avoid Centers for Medicare and Medicaid Services penalties, Project BOOST may be a viable QI approach to achieve their goals. This initial evaluation of participation in Project BOOST by 11 hospitals of varying sizes across the United States showed an associated reduction in rehospitalization rates (absolute=2.0% and relative=13.6%, P=0.054). We did not find any significant change in length of stay among these hospitals implementing BOOST tools.

The tools provided to participating hospitals were developed from evidence found in peer‐reviewed literature established through experimental methods in well‐controlled academic settings. Further tool development was informed by recommendations of an advisory board consisting of expert representatives and advocates involved in the hospital discharge process: patients, caregivers, physicians, nurses, case managers, social workers, insurers, and regulatory and research agencies.[19] The toolkit components address multiple aspects of hospital discharge and follow‐up with the goal of improving health by optimizing the safety of care transitions. Our observation that readmission rates appeared to improve in a diverse hospital sample including nonacademic and community hospitals engaged in Project BOOST is reassuring that the benefits seen in existing research literature, developed in distinctly academic settings, can be replicated in diverse acute‐care settings.

The effect size observed in our study was modest but consistent with several studies identified in a recent review of trials measuring interventions to reduce rehospitalization, where 7 of 16 studies showing a significant improvement registered change in the 0% to 5% absolute range.[12] Impact of this project may have been tempered by the need to translate external QI content to the local setting. Additionally, in contrast to experimental studies that are limited in scope and timing and often scaled to a research budget, BOOST sites were encouraged to implement Project BOOST in the clinical setting even if no new funds were available to support the effort.[12]

The recruitment of a national sample of both academic and nonacademic hospital participants imposed several limitations on our study and analysis. We recognize that intervention units selected by hospitals may have had unmeasured unit and patient characteristics that facilitated successful change and contributed to the observed improvements. However, because external pressure to reduce readmission is present across all hospitals independent of the BOOST intervention, we felt site‐matched controls were essential to understanding effects attributable to the BOOST tools. Differences between units would be expected to be stable over the course of the study period, and comparison of outcome differences between 2 different time periods would be reasonable. Additionally, we could not collect data on readmissions to other hospitals. Theoretically, patients discharged from BOOST units might be more likely to have been rehospitalized elsewhere, but the fraction of rehospitalizations occurring at alternate facilities would also be expected to be similar on the matched control unit.

We report findings from a voluntary cohort willing and capable of designating a comparison clinical unit and contributing the requested data outcomes. Pilot sites that did not report outcomes were not analyzed, but comparison of hospital characteristics shows that participating hospitals were more likely to be large, urban, academic medical centers. Although barriers to data submission were not formally analyzed, reports from nonparticipating sites describe data submission limited by local implementation design (no geographic rollout or simultaneous rollout on all appropriate clinical units), site specific inability to generate unit level outcome statistics, and competing organizational priorities for data analyst time (electronic medical record deployment, alternative QI initiatives). The external validity of our results may be limited to organizations capable of analytics at the level of the individual clinical unit as well as those with sufficient QI resources to support reporting to a national database in the absence of a payer mandate. It is possible that additional financial support for on‐site data collection would have bolstered participation, making the example of participation rates we present potentially informative to organizations hoping to widely disseminate a QI agenda.

Nonetheless, the effectiveness demonstrated in the 11 sites that did participate is encouraging, and ongoing collaboration with subsequent BOOST cohorts has been designed to further facilitate data collection. Among the insights gained from this pilot experience, and incorporated into ongoing BOOST cohorts, is the importance of intensive mentor engagement to foster accountability among participant sites, assist with implementation troubleshooting, and offer expertise that is often particularly effective in gaining local support. We now encourage sites to have 2 mentor site visits to further these roles and more frequent conference calls. Further research to understand the marginal benefit of the mentored implementation approach is ongoing.

The limitations in data submission we experienced with the pilot cohort likely reflect resource constraints not uncommon at many hospitals. Increasing pressure placed on hospitals as a result of the Readmission Reduction Program within the Affordable Care Act as well as increasing interest from private and Medicaid payors to incorporate similar readmission‐based penalties provide encouragement for hospitals to enhance their data and analytic skills. National incentives for implementation of electronic health records (EHR) should also foster such capabilities, though we often saw EHRs as a barrier to QI, especially rapid cycle trials. Fortunately, hospitals are increasingly being afforded access to comprehensive claims databases to assist in tracking readmission rates to other facilities, and these data are becoming available in a more timely fashion. This more robust data collection, facilitated by private payors, state QI organizations, and state hospital associations, will support additional analytic methods such as multivariate regression models and interrupted time series designs to appreciate the experience of current BOOST participants.

Additional research is needed to understand the role of organizational context in the effectiveness of Project BOOST. Differences in rates of tool implementation and changes in clinical outcomes are likely dependent on local implementation context at the level of the healthcare organization and individual clinical unit.[20] Progress reports from site mentors and previously described experiences of QI implementation indicate that successful implementation of a multidimensional bundle of interventions may have reflected a higher level of institutional support, more robust team engagement in the work of reducing readmissions, increased clinical staff support for change, the presence of an effective project champion, or a key facilitating role of external mentorship.[21, 22] Ongoing data collection will continue to measure the sustainability of tool use and observed outcome changes to inform strategies to maintain gains associated with implementation. The role of mentored implementation in facilitating gains also requires further study.

Increasing attention to the problem of avoidable rehospitalization is driving hospitals, insurers, and policy makers to pursue QI efforts that favorably impact readmission rates. Our analysis of the BOOST intervention suggests that modest gains can be achieved following evidence‐based hospital process change facilitated by a mentored implementation model. However, realization of the goal of a 20% reduction in rehospitalization proposed by the Center for Medicare and Medicaid Services' Partnership for Patients initiative may be difficult to achieve on a national scale,[23] especially if efforts focus on just the hospital.

Disclosures

Project BOOST was funded by a grant from The John A. Hartford Foundation. Project BOOST is administered by the Society of Hospital Medicine (SHM). The development of the Project BOOST toolkit, recruitment of sites for this study, mentorship of the pilot cohort, project evaluation planning, and collection of pilot data were funded by a grant from The John A. Harford Foundation. Additional funding for continued data collection and analysis was funded by the SHM through funds from hospitals to participate in Project BOOST, specifically with funding support for Dr. Hansen. Dr. Williams has received funding to serve as Principal Investigator for Project BOOST. Since the time of initial cohort participation, approximately 125 additional hospitals have participated in the mentored implementation of Project BOOST. This participation was funded through a combination of site‐based tuition, third‐party payor support from private insurers, foundations, and federal funding through the Center for Medicare and Medicaid Innovation Partnership for Patients program. Drs. Greenwald, Hansen, and Williams are Project BOOST mentors for current Project BOOST sites and receive financial support through the SHM for this work. Dr. Howell has previously received funding as a Project BOOST mentor. Ms. Budnitz is the BOOST Project Director and is Chief Strategy and Development Officer for the HM. Dr. Maynard is the Senior Vice President of the SHM's Center for Hospital Innovation and Improvement.

Enactment of federal legislation imposing hospital reimbursement penalties for excess rates of rehospitalizations among Medicare fee for service beneficiaries markedly increased interest in hospital quality improvement (QI) efforts to reduce the observed 30‐day rehospitalization of 19.6% in this elderly population.[1, 2] The Congressional Budget Office estimated that reimbursement penalties to hospitals for high readmission rates are expected to save the Medicare program approximately $7 billion between 2010 and 2019.[3] These penalties are complemented by resources from the Center for Medicare and Medicaid Innovation aiming to reduce hospital readmissions by 20% by the end of 2013 through the Partnership for Patients campaign.[4] Although potential financial penalties and provision of resources for QI intensified efforts to enhance the quality of the hospital discharge transition, patient safety risks associated with hospital discharge are well documented.[5, 6] Approximately 20% of patients discharged from the hospital may suffer adverse events,[7, 8] of which up to three‐quarters (72%) are medication related,[9] and over one‐third of required follow‐up testing after discharge is not completed.[10] Such findings indicate opportunities for improvement in the discharge process.[11]

Numerous publications describe studies aiming to improve the hospital discharge process and mitigate these hazards, though a systematic review of interventions to reduce 30‐day rehospitalization indicated that the existing evidence base for the effectiveness of transition interventions demonstrates irregular effectiveness and limitations to generalizability.[12] Most studies showing effectiveness are confined to single academic medical centers. Existing evidence supports multifaceted interventions implemented in both the pre‐ and postdischarge periods and focused on risk assessment and tailored, patient‐centered application of interventions to mitigate risk. For example Project RED (Re‐Engineered Discharge) applied a bundled intervention consisting of intensified patient education and discharge planning, improved medication reconciliation and discharge instructions, and longitudinal patient contact with follow‐up phone calls and a dedicated discharge advocate.[13] However, the mean age of patients participating in the study was 50 years, and it excluded patients admitted from or discharged to skilled nursing facilities, making generalizability to the geriatric population uncertain.

An integral aspect of QI projects is the contribution of local context to translation of best practices to disparate settings.[14, 15, 16] Most available reports of successful interventions to reduce rehospitalization have not fully described the specifics of either the intervention context or design. Moreover, the available evidence base for common interventions to reduce rehospitalization was developed in the academic setting. Validation of single academic center studies in a broader healthcare context is necessary.

Project BOOST (Better Outcomes for Older adults through Safe Transitions) recruited a diverse national cohort of both academic and nonacademic hospitals to participate in a QI effort to implement best practices for hospital discharge care transitions using a national collaborative approach facilitated by external expert mentorship. This study aimed to determine the effectiveness of BOOST in lowering hospital readmission rates and impact on length of stay.

METHODS

The study of Project BOOST was undertaken in accordance with the SQUIRE (Standards for Quality Improvement Reporting Excellence) Guidelines.[17]

RESULTS

Eleven hospitals provided rehospitalization and length‐of‐stay outcome data for both a BOOST and control unit for the pre‐ and postimplementation periods. Compared to the 19 sites that did not participate in the analysis, these 11 sites were significantly larger (559188 beds vs 350205 beds, P=0.003), more likely to be located in an urban area (100.0% [n=11] vs 78.9% [n=15], P=0.035), and more likely to be academic medical centers (45.5% [n=5] vs 26.3% [n=5], P=0.036) (Table 1).

The mean number of tools implemented by sites participating in the analysis was 3.50.9. All sites implemented at least 2 tools. The duration between attendance at the BOOST kickoff event and first tool implementation ranged from 3 months (first tool implemented prior to attending the kickoff) and 9 months (mean duration, 3.34.3 months) (Table 2).

The average rate of 30‐day rehospitalization among BOOST units was 14.7% in the preimplementation period and 12.7% during the postimplementation period (P=0.010) (Figure 1). Rehospitalization rates for matched control units were 14.0% in the preintervention period and 14.1% in the postintervention period (P=0.831). The mean absolute reduction in readmission rates over the 1‐year study period in BOOST units compared to control units was 2.0%, or a relative reduction of 13.6% (P=0.054 for signed rank test comparing differences in readmission rate reduction in BOOST units compared to site‐matched control units). Length of stay in BOOST and control units decreased an average of 0.5 days and 0.3 days, respectively. There was no difference in length of stay change between BOOST units and control units (P=0.966).

Figure 1

DISCUSSION

As hospitals strive to reduce their readmission rates to avoid Centers for Medicare and Medicaid Services penalties, Project BOOST may be a viable QI approach to achieve their goals. This initial evaluation of participation in Project BOOST by 11 hospitals of varying sizes across the United States showed an associated reduction in rehospitalization rates (absolute=2.0% and relative=13.6%, P=0.054). We did not find any significant change in length of stay among these hospitals implementing BOOST tools.

The tools provided to participating hospitals were developed from evidence found in peer‐reviewed literature established through experimental methods in well‐controlled academic settings. Further tool development was informed by recommendations of an advisory board consisting of expert representatives and advocates involved in the hospital discharge process: patients, caregivers, physicians, nurses, case managers, social workers, insurers, and regulatory and research agencies.[19] The toolkit components address multiple aspects of hospital discharge and follow‐up with the goal of improving health by optimizing the safety of care transitions. Our observation that readmission rates appeared to improve in a diverse hospital sample including nonacademic and community hospitals engaged in Project BOOST is reassuring that the benefits seen in existing research literature, developed in distinctly academic settings, can be replicated in diverse acute‐care settings.

The effect size observed in our study was modest but consistent with several studies identified in a recent review of trials measuring interventions to reduce rehospitalization, where 7 of 16 studies showing a significant improvement registered change in the 0% to 5% absolute range.[12] Impact of this project may have been tempered by the need to translate external QI content to the local setting. Additionally, in contrast to experimental studies that are limited in scope and timing and often scaled to a research budget, BOOST sites were encouraged to implement Project BOOST in the clinical setting even if no new funds were available to support the effort.[12]

The recruitment of a national sample of both academic and nonacademic hospital participants imposed several limitations on our study and analysis. We recognize that intervention units selected by hospitals may have had unmeasured unit and patient characteristics that facilitated successful change and contributed to the observed improvements. However, because external pressure to reduce readmission is present across all hospitals independent of the BOOST intervention, we felt site‐matched controls were essential to understanding effects attributable to the BOOST tools. Differences between units would be expected to be stable over the course of the study period, and comparison of outcome differences between 2 different time periods would be reasonable. Additionally, we could not collect data on readmissions to other hospitals. Theoretically, patients discharged from BOOST units might be more likely to have been rehospitalized elsewhere, but the fraction of rehospitalizations occurring at alternate facilities would also be expected to be similar on the matched control unit.

We report findings from a voluntary cohort willing and capable of designating a comparison clinical unit and contributing the requested data outcomes. Pilot sites that did not report outcomes were not analyzed, but comparison of hospital characteristics shows that participating hospitals were more likely to be large, urban, academic medical centers. Although barriers to data submission were not formally analyzed, reports from nonparticipating sites describe data submission limited by local implementation design (no geographic rollout or simultaneous rollout on all appropriate clinical units), site specific inability to generate unit level outcome statistics, and competing organizational priorities for data analyst time (electronic medical record deployment, alternative QI initiatives). The external validity of our results may be limited to organizations capable of analytics at the level of the individual clinical unit as well as those with sufficient QI resources to support reporting to a national database in the absence of a payer mandate. It is possible that additional financial support for on‐site data collection would have bolstered participation, making the example of participation rates we present potentially informative to organizations hoping to widely disseminate a QI agenda.

Nonetheless, the effectiveness demonstrated in the 11 sites that did participate is encouraging, and ongoing collaboration with subsequent BOOST cohorts has been designed to further facilitate data collection. Among the insights gained from this pilot experience, and incorporated into ongoing BOOST cohorts, is the importance of intensive mentor engagement to foster accountability among participant sites, assist with implementation troubleshooting, and offer expertise that is often particularly effective in gaining local support. We now encourage sites to have 2 mentor site visits to further these roles and more frequent conference calls. Further research to understand the marginal benefit of the mentored implementation approach is ongoing.

The limitations in data submission we experienced with the pilot cohort likely reflect resource constraints not uncommon at many hospitals. Increasing pressure placed on hospitals as a result of the Readmission Reduction Program within the Affordable Care Act as well as increasing interest from private and Medicaid payors to incorporate similar readmission‐based penalties provide encouragement for hospitals to enhance their data and analytic skills. National incentives for implementation of electronic health records (EHR) should also foster such capabilities, though we often saw EHRs as a barrier to QI, especially rapid cycle trials. Fortunately, hospitals are increasingly being afforded access to comprehensive claims databases to assist in tracking readmission rates to other facilities, and these data are becoming available in a more timely fashion. This more robust data collection, facilitated by private payors, state QI organizations, and state hospital associations, will support additional analytic methods such as multivariate regression models and interrupted time series designs to appreciate the experience of current BOOST participants.

Additional research is needed to understand the role of organizational context in the effectiveness of Project BOOST. Differences in rates of tool implementation and changes in clinical outcomes are likely dependent on local implementation context at the level of the healthcare organization and individual clinical unit.[20] Progress reports from site mentors and previously described experiences of QI implementation indicate that successful implementation of a multidimensional bundle of interventions may have reflected a higher level of institutional support, more robust team engagement in the work of reducing readmissions, increased clinical staff support for change, the presence of an effective project champion, or a key facilitating role of external mentorship.[21, 22] Ongoing data collection will continue to measure the sustainability of tool use and observed outcome changes to inform strategies to maintain gains associated with implementation. The role of mentored implementation in facilitating gains also requires further study.

Increasing attention to the problem of avoidable rehospitalization is driving hospitals, insurers, and policy makers to pursue QI efforts that favorably impact readmission rates. Our analysis of the BOOST intervention suggests that modest gains can be achieved following evidence‐based hospital process change facilitated by a mentored implementation model. However, realization of the goal of a 20% reduction in rehospitalization proposed by the Center for Medicare and Medicaid Services' Partnership for Patients initiative may be difficult to achieve on a national scale,[23] especially if efforts focus on just the hospital.

Disclosures

Project BOOST was funded by a grant from The John A. Hartford Foundation. Project BOOST is administered by the Society of Hospital Medicine (SHM). The development of the Project BOOST toolkit, recruitment of sites for this study, mentorship of the pilot cohort, project evaluation planning, and collection of pilot data were funded by a grant from The John A. Harford Foundation. Additional funding for continued data collection and analysis was funded by the SHM through funds from hospitals to participate in Project BOOST, specifically with funding support for Dr. Hansen. Dr. Williams has received funding to serve as Principal Investigator for Project BOOST. Since the time of initial cohort participation, approximately 125 additional hospitals have participated in the mentored implementation of Project BOOST. This participation was funded through a combination of site‐based tuition, third‐party payor support from private insurers, foundations, and federal funding through the Center for Medicare and Medicaid Innovation Partnership for Patients program. Drs. Greenwald, Hansen, and Williams are Project BOOST mentors for current Project BOOST sites and receive financial support through the SHM for this work. Dr. Howell has previously received funding as a Project BOOST mentor. Ms. Budnitz is the BOOST Project Director and is Chief Strategy and Development Officer for the HM. Dr. Maynard is the Senior Vice President of the SHM's Center for Hospital Innovation and Improvement.

Within Medicare recipients, an astounding one in five medical patients (19.6%) is readmitted within 30 days, accounting for $15 billion in spending.1, 2 Amidst the current healthcare system crisis, reducing these hospital readmissions has risen to the highest priority. Reducing readmissions is the newest addition to multiple quality dashboards, both institutional and national, as a measure of the care delivered during hospitalization.3 One of the most notable of these reporting entities, Hospital Compare, now publicly reports Medicare readmission rates for a few common diagnoses.4 While Medicare already withholds payment to hospitals for readmissions within 24 hours for the same diagnosis, Medicare may soon reduce payment to hospitals with the highest rates of readmission within 30 days, a powerful incentive for hospitals to intervene. Readmissions have also reached the radar of additional stakeholders, even making its way onto Obama's budget considerations, given the potential cost savings to the system overall.5

To develop systems which reduce readmissions, one must first gain understanding of the characteristics of readmissions. A few clinical risk factors (such as age, number of prior admissions, and comorbidities) have been well defined in subgroups of general medicine inpatients.612 Likewise, interventions aiming to reduce readmissions have also focused on subgroups, excluding a large portion of hospitalized patients (for example, non‐English speakers and younger patients).1320 Other data have been derived in veterans or within non‐US populations that have inherently different payer, race, ethnicity, and primary language composition, and may not be applicable outside those settings.7, 8, 10, 11, 21 Lastly, little is known regarding risk that may be associated with operational factors, such as weekend discharge or admission source. As a result, there are few data describing the clinical, operational, and demographic factors associated with readmission in a heterogeneous population of hospitalized general medicine patientsthe patient population of most generalists in the United States.

To understand the impact of a variety of risk factors in a diverse general medicine population, we evaluated the characteristics of readmitted patients in a large urban university medical center over a 2‐year period. We hypothesized that a number of clinical, operational, and sociodemographic factors would be associated with readmission.

Methods

Results

Discussion

In this retrospective observational study of hospitalized patients, we found that readmission was common and associated with a number of risk factors that could be easily identified early in hospitalization. Nonclinical factors associated with readmission were black race and Medicaid payer status (in the unadjusted model). Clinical factors were high risk medications including steroids and narcotics; and comorbidities including congestive heart failure, renal disease, cancer, anemia, and weight loss. In contrast, other potential riskssuch as discharge on a weekend and discharge to an SNFwere not independently associated with readmission. This cohortwith a mix of clinical scenarios, payers, age, etc.represents the inherently heterogeneous population of inpatient general medicine across the country and abroad. Hospitalists provided care for over 65% of the general medicine service, again representative of the trend in US inpatient medicine.27, 28 Lastly, our cohort did not have the benefit of a systematic and consistent discharge process with interventions focused on reducing readmissions. This gap, which is common across hospitals, highlights the utility of this data in targeting quality improvement efforts.

Reducing risks for readmission requires identification of patient populations at highest risk; in those patients, one can further identify factors which are potentially modifiable via education or patient‐engagement interventions. While in the hospital, more intensive predischarge counseling and efforts to increase mobility may be most useful if targeted early and often on those at highest risk.15, 16, 29, 30 Finally, broader‐based support in the form of better home services, more access to longitudinal care, or targeted postdischarge efforts may be required.14, 31

Though current strategies focus largely on clinical risk factors, this study shows that nonclinical factors play an equally important but underappreciated role in contributing to readmission. While prior studies have shown variable results on association of black race with readmission,2, 9, 11 none have evaluated or linked Medicaid to readmission, which just missed statistical significance in this study (OR, 1.15; 95% CI, 0.971.36). Both black race and Medicaid as payer are proxies for the underlying root cause aspects leading to readmission, such as access to longitudinal care. Following this trail to the root cause will require in‐depth qualitative evaluation that includes the patient perspective as a source of data.32 For example, risk for readmission may not stem solely from being on warfarin, but in combination with not having transportation to get an international normalized ratio (INR) checked, a suboptimal understanding of how to take the medication, or not recognizing potential side effects until too late to avoid inpatient admission.

Several of the strongest associations, and perhaps most conducive to targeted interventions, were high‐risk medications at discharge. Risk related to medications and adverse drug events following discharge have been a consistent theme in readmission literature.24, 33 Our current system, which includes mandated inpatient medicine reconciliation, does not encourage discontinuation of unnecessary medications to combat polypharmacy, address affordability of medications, provide consistent medication counseling, or focus on the highest risk medications. In fact, bundled interventions which implement pharmacists to focus on these measures have been successful in decreasing readmission,14, 16, 29 but unfortunately are not yet part of the standard of care. The challenge remains transforming a mandatory policy such as medicine reconciliation into a valuable and systematic tool in the discharge process.

Two factors were surprisingly protective against readmission: neurologic diagnosis and anticholinergic medications. This first may be explained by the presence of a separate neurology service at our institution which skews our data. For example, a patient with acute stroke, who has a 20% rate of bounce‐back to a higher level of care within 30 days of discharge,34 would be admitted to the neurology service, not general medicine, and therefore would not nr part of our cohort. Regarding anticholinergics, several factors may explain this unexpected result. First, use of anticholinergics was relatively rare in our sample (2.7% in readmitted patients, 4.1% in patients not readmitted), possibly creating a false positive result. Second, Hanlon et al.24 showed only a weak association at best between anticholinergics and postdischarge adverse drug reactions (hazard ratio, 1.11; 95% CI, 0.86‐1.43). Lastly, anticholinergics include a varied group of medications, therefore diluting possible relative risk of specific medications.

While this study allows providers to identify patients at increased risk of readmission, the identified factors do not fully account for readmission risk; we did not aim to produce a risk‐prediction rule with our study. Prior readmission studies have been unable to create a tool to predict which patients will be readmitted with much success.3537 These results underscore the complexities and variability of readmission, which often lack a clear single cause and effect relationship. Given the breadth of risk factors we identified, it seems likely that more intensive interventions will require a multidisciplinary approach, one which might be costly if applied broadly. Our study does not attempt to predict who will be readmitted and who will not, but rather provides a list of risk factors which might be used to deploy resources more efficiently.

This study had several limitations. We did not capture readmissions to outside hospitals, which account for 22% to 24% of all readmissions in prior studies, and therefore have underestimated the readmission rate in our population.2, 8 However, by limiting our data to 2 hospitals within 1 institution, we were able to include more detailed patient level data, which is not accurately available in other large databases. Also, while studies of risk factors in a managed care population (such as within Medicare, the Veterans Affairs medical centers, or countries with national integrated medical records) are able to capture all readmissions, this study is the first to evaluate readmissions risk factors in a truly heterogeneous U.S. inpatient medicine population without limitation by age or payer status. Second, we did not have access to outpatient medications lists; however use of these same medications within the last 48 hours of admission is likely a reasonable proxy for outpatient use and more conducive to potential interventions (such as medication reconciliation or patient education) that could flag patients prior to discharge. Payer data was limited to only the primary payer, so patients who were dual eligible (ie, have both Medicare and Medicaid) were categorized as Medicare. Regarding sociodemographic factors, while primary language other than English was not associated with readmission, language data was missing in 17.4% of admissions, thereby limiting our ability to evaluate this factor. Our data did not include access to outpatient or primary care, and therefore we were unable to evaluate access to postdischarge follow‐up care as a risk factor for readmission. Lastly, while this study did not include outpatient deaths, we did exclude patients who died in the hospital.

Conclusions

Readmission is common among general medicine patients, with approximately 1 in 5 patients being readmitted within 30 days. While the identified associated factors do not account for all the potential reasons for readmission, our study suggests a spectrum of risk factors which might be used to target more intensive multidisciplinary interventions. Specifically, the nonclinical factors of race and payer status merit further in depth research incorporating the patient experience to truly determine causation of readmission. Hospitalists, who are at nexus of the discharge process and uniquely invested in quality inpatient care, are ideally positioned to lead efforts to reduce readmissions. How to use our study's results to develop and implement effective interventions to reduce readmissions remains a subject for future studies.

Within Medicare recipients, an astounding one in five medical patients (19.6%) is readmitted within 30 days, accounting for $15 billion in spending.1, 2 Amidst the current healthcare system crisis, reducing these hospital readmissions has risen to the highest priority. Reducing readmissions is the newest addition to multiple quality dashboards, both institutional and national, as a measure of the care delivered during hospitalization.3 One of the most notable of these reporting entities, Hospital Compare, now publicly reports Medicare readmission rates for a few common diagnoses.4 While Medicare already withholds payment to hospitals for readmissions within 24 hours for the same diagnosis, Medicare may soon reduce payment to hospitals with the highest rates of readmission within 30 days, a powerful incentive for hospitals to intervene. Readmissions have also reached the radar of additional stakeholders, even making its way onto Obama's budget considerations, given the potential cost savings to the system overall.5

To develop systems which reduce readmissions, one must first gain understanding of the characteristics of readmissions. A few clinical risk factors (such as age, number of prior admissions, and comorbidities) have been well defined in subgroups of general medicine inpatients.612 Likewise, interventions aiming to reduce readmissions have also focused on subgroups, excluding a large portion of hospitalized patients (for example, non‐English speakers and younger patients).1320 Other data have been derived in veterans or within non‐US populations that have inherently different payer, race, ethnicity, and primary language composition, and may not be applicable outside those settings.7, 8, 10, 11, 21 Lastly, little is known regarding risk that may be associated with operational factors, such as weekend discharge or admission source. As a result, there are few data describing the clinical, operational, and demographic factors associated with readmission in a heterogeneous population of hospitalized general medicine patientsthe patient population of most generalists in the United States.

To understand the impact of a variety of risk factors in a diverse general medicine population, we evaluated the characteristics of readmitted patients in a large urban university medical center over a 2‐year period. We hypothesized that a number of clinical, operational, and sociodemographic factors would be associated with readmission.

Methods

Results

Discussion

In this retrospective observational study of hospitalized patients, we found that readmission was common and associated with a number of risk factors that could be easily identified early in hospitalization. Nonclinical factors associated with readmission were black race and Medicaid payer status (in the unadjusted model). Clinical factors were high risk medications including steroids and narcotics; and comorbidities including congestive heart failure, renal disease, cancer, anemia, and weight loss. In contrast, other potential riskssuch as discharge on a weekend and discharge to an SNFwere not independently associated with readmission. This cohortwith a mix of clinical scenarios, payers, age, etc.represents the inherently heterogeneous population of inpatient general medicine across the country and abroad. Hospitalists provided care for over 65% of the general medicine service, again representative of the trend in US inpatient medicine.27, 28 Lastly, our cohort did not have the benefit of a systematic and consistent discharge process with interventions focused on reducing readmissions. This gap, which is common across hospitals, highlights the utility of this data in targeting quality improvement efforts.

Reducing risks for readmission requires identification of patient populations at highest risk; in those patients, one can further identify factors which are potentially modifiable via education or patient‐engagement interventions. While in the hospital, more intensive predischarge counseling and efforts to increase mobility may be most useful if targeted early and often on those at highest risk.15, 16, 29, 30 Finally, broader‐based support in the form of better home services, more access to longitudinal care, or targeted postdischarge efforts may be required.14, 31

Though current strategies focus largely on clinical risk factors, this study shows that nonclinical factors play an equally important but underappreciated role in contributing to readmission. While prior studies have shown variable results on association of black race with readmission,2, 9, 11 none have evaluated or linked Medicaid to readmission, which just missed statistical significance in this study (OR, 1.15; 95% CI, 0.971.36). Both black race and Medicaid as payer are proxies for the underlying root cause aspects leading to readmission, such as access to longitudinal care. Following this trail to the root cause will require in‐depth qualitative evaluation that includes the patient perspective as a source of data.32 For example, risk for readmission may not stem solely from being on warfarin, but in combination with not having transportation to get an international normalized ratio (INR) checked, a suboptimal understanding of how to take the medication, or not recognizing potential side effects until too late to avoid inpatient admission.

Several of the strongest associations, and perhaps most conducive to targeted interventions, were high‐risk medications at discharge. Risk related to medications and adverse drug events following discharge have been a consistent theme in readmission literature.24, 33 Our current system, which includes mandated inpatient medicine reconciliation, does not encourage discontinuation of unnecessary medications to combat polypharmacy, address affordability of medications, provide consistent medication counseling, or focus on the highest risk medications. In fact, bundled interventions which implement pharmacists to focus on these measures have been successful in decreasing readmission,14, 16, 29 but unfortunately are not yet part of the standard of care. The challenge remains transforming a mandatory policy such as medicine reconciliation into a valuable and systematic tool in the discharge process.

Two factors were surprisingly protective against readmission: neurologic diagnosis and anticholinergic medications. This first may be explained by the presence of a separate neurology service at our institution which skews our data. For example, a patient with acute stroke, who has a 20% rate of bounce‐back to a higher level of care within 30 days of discharge,34 would be admitted to the neurology service, not general medicine, and therefore would not nr part of our cohort. Regarding anticholinergics, several factors may explain this unexpected result. First, use of anticholinergics was relatively rare in our sample (2.7% in readmitted patients, 4.1% in patients not readmitted), possibly creating a false positive result. Second, Hanlon et al.24 showed only a weak association at best between anticholinergics and postdischarge adverse drug reactions (hazard ratio, 1.11; 95% CI, 0.86‐1.43). Lastly, anticholinergics include a varied group of medications, therefore diluting possible relative risk of specific medications.

While this study allows providers to identify patients at increased risk of readmission, the identified factors do not fully account for readmission risk; we did not aim to produce a risk‐prediction rule with our study. Prior readmission studies have been unable to create a tool to predict which patients will be readmitted with much success.3537 These results underscore the complexities and variability of readmission, which often lack a clear single cause and effect relationship. Given the breadth of risk factors we identified, it seems likely that more intensive interventions will require a multidisciplinary approach, one which might be costly if applied broadly. Our study does not attempt to predict who will be readmitted and who will not, but rather provides a list of risk factors which might be used to deploy resources more efficiently.

This study had several limitations. We did not capture readmissions to outside hospitals, which account for 22% to 24% of all readmissions in prior studies, and therefore have underestimated the readmission rate in our population.2, 8 However, by limiting our data to 2 hospitals within 1 institution, we were able to include more detailed patient level data, which is not accurately available in other large databases. Also, while studies of risk factors in a managed care population (such as within Medicare, the Veterans Affairs medical centers, or countries with national integrated medical records) are able to capture all readmissions, this study is the first to evaluate readmissions risk factors in a truly heterogeneous U.S. inpatient medicine population without limitation by age or payer status. Second, we did not have access to outpatient medications lists; however use of these same medications within the last 48 hours of admission is likely a reasonable proxy for outpatient use and more conducive to potential interventions (such as medication reconciliation or patient education) that could flag patients prior to discharge. Payer data was limited to only the primary payer, so patients who were dual eligible (ie, have both Medicare and Medicaid) were categorized as Medicare. Regarding sociodemographic factors, while primary language other than English was not associated with readmission, language data was missing in 17.4% of admissions, thereby limiting our ability to evaluate this factor. Our data did not include access to outpatient or primary care, and therefore we were unable to evaluate access to postdischarge follow‐up care as a risk factor for readmission. Lastly, while this study did not include outpatient deaths, we did exclude patients who died in the hospital.

Conclusions

Readmission is common among general medicine patients, with approximately 1 in 5 patients being readmitted within 30 days. While the identified associated factors do not account for all the potential reasons for readmission, our study suggests a spectrum of risk factors which might be used to target more intensive multidisciplinary interventions. Specifically, the nonclinical factors of race and payer status merit further in depth research incorporating the patient experience to truly determine causation of readmission. Hospitalists, who are at nexus of the discharge process and uniquely invested in quality inpatient care, are ideally positioned to lead efforts to reduce readmissions. How to use our study's results to develop and implement effective interventions to reduce readmissions remains a subject for future studies.

Within Medicare recipients, an astounding one in five medical patients (19.6%) is readmitted within 30 days, accounting for $15 billion in spending.1, 2 Amidst the current healthcare system crisis, reducing these hospital readmissions has risen to the highest priority. Reducing readmissions is the newest addition to multiple quality dashboards, both institutional and national, as a measure of the care delivered during hospitalization.3 One of the most notable of these reporting entities, Hospital Compare, now publicly reports Medicare readmission rates for a few common diagnoses.4 While Medicare already withholds payment to hospitals for readmissions within 24 hours for the same diagnosis, Medicare may soon reduce payment to hospitals with the highest rates of readmission within 30 days, a powerful incentive for hospitals to intervene. Readmissions have also reached the radar of additional stakeholders, even making its way onto Obama's budget considerations, given the potential cost savings to the system overall.5

To develop systems which reduce readmissions, one must first gain understanding of the characteristics of readmissions. A few clinical risk factors (such as age, number of prior admissions, and comorbidities) have been well defined in subgroups of general medicine inpatients.612 Likewise, interventions aiming to reduce readmissions have also focused on subgroups, excluding a large portion of hospitalized patients (for example, non‐English speakers and younger patients).1320 Other data have been derived in veterans or within non‐US populations that have inherently different payer, race, ethnicity, and primary language composition, and may not be applicable outside those settings.7, 8, 10, 11, 21 Lastly, little is known regarding risk that may be associated with operational factors, such as weekend discharge or admission source. As a result, there are few data describing the clinical, operational, and demographic factors associated with readmission in a heterogeneous population of hospitalized general medicine patientsthe patient population of most generalists in the United States.

To understand the impact of a variety of risk factors in a diverse general medicine population, we evaluated the characteristics of readmitted patients in a large urban university medical center over a 2‐year period. We hypothesized that a number of clinical, operational, and sociodemographic factors would be associated with readmission.

Methods

Results

Discussion

In this retrospective observational study of hospitalized patients, we found that readmission was common and associated with a number of risk factors that could be easily identified early in hospitalization. Nonclinical factors associated with readmission were black race and Medicaid payer status (in the unadjusted model). Clinical factors were high risk medications including steroids and narcotics; and comorbidities including congestive heart failure, renal disease, cancer, anemia, and weight loss. In contrast, other potential riskssuch as discharge on a weekend and discharge to an SNFwere not independently associated with readmission. This cohortwith a mix of clinical scenarios, payers, age, etc.represents the inherently heterogeneous population of inpatient general medicine across the country and abroad. Hospitalists provided care for over 65% of the general medicine service, again representative of the trend in US inpatient medicine.27, 28 Lastly, our cohort did not have the benefit of a systematic and consistent discharge process with interventions focused on reducing readmissions. This gap, which is common across hospitals, highlights the utility of this data in targeting quality improvement efforts.

Reducing risks for readmission requires identification of patient populations at highest risk; in those patients, one can further identify factors which are potentially modifiable via education or patient‐engagement interventions. While in the hospital, more intensive predischarge counseling and efforts to increase mobility may be most useful if targeted early and often on those at highest risk.15, 16, 29, 30 Finally, broader‐based support in the form of better home services, more access to longitudinal care, or targeted postdischarge efforts may be required.14, 31

Though current strategies focus largely on clinical risk factors, this study shows that nonclinical factors play an equally important but underappreciated role in contributing to readmission. While prior studies have shown variable results on association of black race with readmission,2, 9, 11 none have evaluated or linked Medicaid to readmission, which just missed statistical significance in this study (OR, 1.15; 95% CI, 0.971.36). Both black race and Medicaid as payer are proxies for the underlying root cause aspects leading to readmission, such as access to longitudinal care. Following this trail to the root cause will require in‐depth qualitative evaluation that includes the patient perspective as a source of data.32 For example, risk for readmission may not stem solely from being on warfarin, but in combination with not having transportation to get an international normalized ratio (INR) checked, a suboptimal understanding of how to take the medication, or not recognizing potential side effects until too late to avoid inpatient admission.

Several of the strongest associations, and perhaps most conducive to targeted interventions, were high‐risk medications at discharge. Risk related to medications and adverse drug events following discharge have been a consistent theme in readmission literature.24, 33 Our current system, which includes mandated inpatient medicine reconciliation, does not encourage discontinuation of unnecessary medications to combat polypharmacy, address affordability of medications, provide consistent medication counseling, or focus on the highest risk medications. In fact, bundled interventions which implement pharmacists to focus on these measures have been successful in decreasing readmission,14, 16, 29 but unfortunately are not yet part of the standard of care. The challenge remains transforming a mandatory policy such as medicine reconciliation into a valuable and systematic tool in the discharge process.

Two factors were surprisingly protective against readmission: neurologic diagnosis and anticholinergic medications. This first may be explained by the presence of a separate neurology service at our institution which skews our data. For example, a patient with acute stroke, who has a 20% rate of bounce‐back to a higher level of care within 30 days of discharge,34 would be admitted to the neurology service, not general medicine, and therefore would not nr part of our cohort. Regarding anticholinergics, several factors may explain this unexpected result. First, use of anticholinergics was relatively rare in our sample (2.7% in readmitted patients, 4.1% in patients not readmitted), possibly creating a false positive result. Second, Hanlon et al.24 showed only a weak association at best between anticholinergics and postdischarge adverse drug reactions (hazard ratio, 1.11; 95% CI, 0.86‐1.43). Lastly, anticholinergics include a varied group of medications, therefore diluting possible relative risk of specific medications.

While this study allows providers to identify patients at increased risk of readmission, the identified factors do not fully account for readmission risk; we did not aim to produce a risk‐prediction rule with our study. Prior readmission studies have been unable to create a tool to predict which patients will be readmitted with much success.3537 These results underscore the complexities and variability of readmission, which often lack a clear single cause and effect relationship. Given the breadth of risk factors we identified, it seems likely that more intensive interventions will require a multidisciplinary approach, one which might be costly if applied broadly. Our study does not attempt to predict who will be readmitted and who will not, but rather provides a list of risk factors which might be used to deploy resources more efficiently.

This study had several limitations. We did not capture readmissions to outside hospitals, which account for 22% to 24% of all readmissions in prior studies, and therefore have underestimated the readmission rate in our population.2, 8 However, by limiting our data to 2 hospitals within 1 institution, we were able to include more detailed patient level data, which is not accurately available in other large databases. Also, while studies of risk factors in a managed care population (such as within Medicare, the Veterans Affairs medical centers, or countries with national integrated medical records) are able to capture all readmissions, this study is the first to evaluate readmissions risk factors in a truly heterogeneous U.S. inpatient medicine population without limitation by age or payer status. Second, we did not have access to outpatient medications lists; however use of these same medications within the last 48 hours of admission is likely a reasonable proxy for outpatient use and more conducive to potential interventions (such as medication reconciliation or patient education) that could flag patients prior to discharge. Payer data was limited to only the primary payer, so patients who were dual eligible (ie, have both Medicare and Medicaid) were categorized as Medicare. Regarding sociodemographic factors, while primary language other than English was not associated with readmission, language data was missing in 17.4% of admissions, thereby limiting our ability to evaluate this factor. Our data did not include access to outpatient or primary care, and therefore we were unable to evaluate access to postdischarge follow‐up care as a risk factor for readmission. Lastly, while this study did not include outpatient deaths, we did exclude patients who died in the hospital.

Conclusions

Readmission is common among general medicine patients, with approximately 1 in 5 patients being readmitted within 30 days. While the identified associated factors do not account for all the potential reasons for readmission, our study suggests a spectrum of risk factors which might be used to target more intensive multidisciplinary interventions. Specifically, the nonclinical factors of race and payer status merit further in depth research incorporating the patient experience to truly determine causation of readmission. Hospitalists, who are at nexus of the discharge process and uniquely invested in quality inpatient care, are ideally positioned to lead efforts to reduce readmissions. How to use our study's results to develop and implement effective interventions to reduce readmissions remains a subject for future studies.