Luke O. Hansen, 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.