Mark V. Williams, MD

The patient‐physician relationship is fundamental to safe and effective care. Hospital settings present unique challenges to this partnership, including the lack of a prior relationship for hospital‐based physicians, rapid pace of clinical care, and dynamic nature of inpatient medical teams. Prior studies document that a majority of hospitalized patients are unable to correctly identify their physicians or nurses, and patients in teaching hospitals have difficulty understanding their physicians' level of training.[1, 2, 3, 4] Acknowledging these deficits, professional societies and the Accreditation Council for Graduate Medical Education (ACMGE) have issued policies stating that patients and caregivers need to know who is responsible at every point during patient care.[5, 6] These policies do not, however, make recommendations on methods to achieve better understanding.

Simple interventions improve patients' ability to correctly identify the names and roles of their hospital physicians. Maniaci and colleagues found that patients were better able to identify attending physicians when their names were written on the dry‐erase board in the room.[7] Arora and colleagues asked hospital physicians to give facecards, which included their picture and a description of their role, to patients.[8] Patients were more likely to correctly identify 1 physicians, but, surprisingly, less likely to understand physicians' roles. In a similar study, Francis and colleagues placed photographs with names of the attending and resident physicians on the wall in patient rooms.[9] Patients who had photographs of their physicians on the wall were more likely to correctly identify physicians on their team compared with patients who had no photographs. Additionally, patients who were able to identify more physicians rated satisfaction with physicians higher in 2 of 6 survey questions used. However, the study was limited by the use of a nonvalidated instrument to assess patient satisfaction and the use of an intermediate outcome (ie, ability to identify physicians) as the independent variable rather than the intervention itself (ie, physician photographs).

Beyond satisfaction, lack of familiarity may negatively impact patients' trust and agreement with hospital physicians. Trust and agreement are important predictors of adherence to recommended treatment in outpatient settings[10, 11, 12, 13, 14, 15, 16, 17, 18] but have not been adequately evaluated in hospital settings. Therefore, we sought to pilot the use of physician facecards and assess their potential impact on patients' knowledge of physicians' names and roles as well as patient satisfaction, trust, and agreement with physicians.

METHODS

Physician Facecard

We created draft physician facecards featuring pictures of physicians and descriptions of their roles. We used Lexile analysis, a widely used measure of reading difficulty, to improve readability in an iterative fashion.[20, 21] We then sought feedback at hospitalist and resident meetings. Specifically, we asked for suggested revisions to content and recommendations on reliable methods to deliver facecards to patients. Teaching physicians felt strongly that each team member should be listed and shown on 1 card, which would fit easily into a lab‐coat pocket. We similarly engaged the NMH Patient and Family Advisory Council to seek recommended revisions to content and delivery of the facecards. The Council consists of 18 patient and caregiver members who meet regularly to provide input on hospital programs and proposals. Council members felt strongly that physicians should deliver the cards themselves during their initial introduction, rather than having patients receive cards by other means (eg, as part of unit orientation materials delivered by nonphysician staff members). We incorporated feedback from these stakeholder groups into a final version of the physician facecard and method for delivery (Figure 1).

Figure 1

We implemented the use of facecards from May to June 2012. Physicians on intervention units were informed of the study via email, and one of the co‐investigators (T.C.) distributed a supply of facecards to these physicians at the start of each rotation. This distribution was performed in person, and physicians were instructed to provide a facecard to each new patient during their first encounter. We also placed facecards in easily visible cardholders at the nurses' station on intervention units. Reminder emails were sent once each week to reinforce physician delivery of facecards.

RESULTS

DISCUSSION

We found that receipt of physician facecards significantly improved patients' knowledge of the names and roles of hospital physicians but had little to no impact on satisfaction, trust, or agreement with physicians. Our finding of improved knowledge of the names and roles of physician providers is consistent with prior studies using similar interventions.[7, 8, 9] Facecards may have prompted more effective introductions on the part of physicians and may have served as memory aids for patients to better retain information about their newly introduced hospital physicians.

Patient receipt of the facecard on intervention units was incomplete in our study. Despite engagement of physicians in designing cards that could easily fit into lab coats and a robust strategy to inform and motivate physician delivery of facecards, only 68% of intended patients received them. Although not explicitly reported, prior studies appear to have similarly struggled to deliver interventions consistently. Arora and colleagues reported that facecards were visible in only 59% of patients' rooms among those able to correctly identify 1 of their physicians.[8] A post hoc survey of physicians involved in our study revealed the biggest impediment to delivering facecards was simply forgetting to do so (data not shown). Technologic innovations may help by automating the identification of providers. For example, the University of Pittsburgh Medical Center has piloted smart rooms that use sensor technology to announce the name and role of providers as they enter patients' rooms.[26]

We hypothesized that facecards might improve other important aspects of the patient‐physicians relationship. Although levels of patient satisfaction were slightly higher in patients who had received facecards, the results were not statistically significant. Levels of trust and agreement were minimally higher in patients who received facecards, and the results were not statistically significant. Notably, baseline levels of trust and agreement were higher than we had expected. In fact, levels of trust were nearly identical to those seen in a prior study of outpatients who had been with the same physician for a median of 4 years.[22] Patients in our study may have had high levels of trust in the hospital and transferred this trust to their assigned physicians as representatives of the organization. The high level of agreement may relate to patients' tendency to prefer a more passive role as they encounter serious illness.[27, 28] Paradoxically, these findings may impede optimal patient care. The high levels of trust and agreement in the current study suggest that patients may not question their physicians to clarify plans and the rationale behind them. Prior research has shown that deficits in patients' comprehension of the care plan are often not apparent to patients or their physicians.[4, 29, 30]

Our study has several limitations. First, we assessed an intervention involving 4 units in a single hospital. Generalizability may be limited, as physician‐staffing models, hospitals, and the patients they serve vary. Second, as previously mentioned, patients in the intervention group did not receive physician facecards as consistently as intended. We conducted analyses based on treatment received in an effort to evaluate the impact of facecards if optimally delivered. Third, questions assessing satisfaction, trust, and agreement did not specifically ask patients to reflect on care provided by the primary physician team. It is possible that interactions with other physicians (ie, consultants) may have influenced these results. Fourth, we were underpowered to detect statistically significant improvements in satisfaction, trust, or agreement resulting from our intervention. Assuming the intervention might truly improve satisfaction with physicians from 54.2% to 63.6%, we would have needed 900 patients (ie, 450 each for the intervention and control groups) to have 80% power to detect a statistically significant difference. However, our results show that patients have high levels of trust and agreement with hospital physicians despite the relative lack of familiarity. Therefore, any existing deficits in hospitalized patients' comprehension of the care plan do not appear to be exacerbated by a lack of trust and/or agreement with treating physicians.

CONCLUSION

In summary, we found that physician facecards significantly improved patients' knowledge of the names and roles of hospital physicians but had little to no impact on satisfaction, trust, or agreement with physicians. Baseline levels of satisfaction, trust, and agreement were high, suggesting lack of familiarity with hospital physicians does not impede these important aspects of the patient‐physician relationship. Larger studies are needed to definitively assess the impact of facecards on satisfaction, trust, and agreement with physicians.

The patient‐physician relationship is fundamental to safe and effective care. Hospital settings present unique challenges to this partnership, including the lack of a prior relationship for hospital‐based physicians, rapid pace of clinical care, and dynamic nature of inpatient medical teams. Prior studies document that a majority of hospitalized patients are unable to correctly identify their physicians or nurses, and patients in teaching hospitals have difficulty understanding their physicians' level of training.[1, 2, 3, 4] Acknowledging these deficits, professional societies and the Accreditation Council for Graduate Medical Education (ACMGE) have issued policies stating that patients and caregivers need to know who is responsible at every point during patient care.[5, 6] These policies do not, however, make recommendations on methods to achieve better understanding.

Simple interventions improve patients' ability to correctly identify the names and roles of their hospital physicians. Maniaci and colleagues found that patients were better able to identify attending physicians when their names were written on the dry‐erase board in the room.[7] Arora and colleagues asked hospital physicians to give facecards, which included their picture and a description of their role, to patients.[8] Patients were more likely to correctly identify 1 physicians, but, surprisingly, less likely to understand physicians' roles. In a similar study, Francis and colleagues placed photographs with names of the attending and resident physicians on the wall in patient rooms.[9] Patients who had photographs of their physicians on the wall were more likely to correctly identify physicians on their team compared with patients who had no photographs. Additionally, patients who were able to identify more physicians rated satisfaction with physicians higher in 2 of 6 survey questions used. However, the study was limited by the use of a nonvalidated instrument to assess patient satisfaction and the use of an intermediate outcome (ie, ability to identify physicians) as the independent variable rather than the intervention itself (ie, physician photographs).

Beyond satisfaction, lack of familiarity may negatively impact patients' trust and agreement with hospital physicians. Trust and agreement are important predictors of adherence to recommended treatment in outpatient settings[10, 11, 12, 13, 14, 15, 16, 17, 18] but have not been adequately evaluated in hospital settings. Therefore, we sought to pilot the use of physician facecards and assess their potential impact on patients' knowledge of physicians' names and roles as well as patient satisfaction, trust, and agreement with physicians.

METHODS

Physician Facecard

We created draft physician facecards featuring pictures of physicians and descriptions of their roles. We used Lexile analysis, a widely used measure of reading difficulty, to improve readability in an iterative fashion.[20, 21] We then sought feedback at hospitalist and resident meetings. Specifically, we asked for suggested revisions to content and recommendations on reliable methods to deliver facecards to patients. Teaching physicians felt strongly that each team member should be listed and shown on 1 card, which would fit easily into a lab‐coat pocket. We similarly engaged the NMH Patient and Family Advisory Council to seek recommended revisions to content and delivery of the facecards. The Council consists of 18 patient and caregiver members who meet regularly to provide input on hospital programs and proposals. Council members felt strongly that physicians should deliver the cards themselves during their initial introduction, rather than having patients receive cards by other means (eg, as part of unit orientation materials delivered by nonphysician staff members). We incorporated feedback from these stakeholder groups into a final version of the physician facecard and method for delivery (Figure 1).

Figure 1

We implemented the use of facecards from May to June 2012. Physicians on intervention units were informed of the study via email, and one of the co‐investigators (T.C.) distributed a supply of facecards to these physicians at the start of each rotation. This distribution was performed in person, and physicians were instructed to provide a facecard to each new patient during their first encounter. We also placed facecards in easily visible cardholders at the nurses' station on intervention units. Reminder emails were sent once each week to reinforce physician delivery of facecards.

RESULTS

DISCUSSION

We found that receipt of physician facecards significantly improved patients' knowledge of the names and roles of hospital physicians but had little to no impact on satisfaction, trust, or agreement with physicians. Our finding of improved knowledge of the names and roles of physician providers is consistent with prior studies using similar interventions.[7, 8, 9] Facecards may have prompted more effective introductions on the part of physicians and may have served as memory aids for patients to better retain information about their newly introduced hospital physicians.

Patient receipt of the facecard on intervention units was incomplete in our study. Despite engagement of physicians in designing cards that could easily fit into lab coats and a robust strategy to inform and motivate physician delivery of facecards, only 68% of intended patients received them. Although not explicitly reported, prior studies appear to have similarly struggled to deliver interventions consistently. Arora and colleagues reported that facecards were visible in only 59% of patients' rooms among those able to correctly identify 1 of their physicians.[8] A post hoc survey of physicians involved in our study revealed the biggest impediment to delivering facecards was simply forgetting to do so (data not shown). Technologic innovations may help by automating the identification of providers. For example, the University of Pittsburgh Medical Center has piloted smart rooms that use sensor technology to announce the name and role of providers as they enter patients' rooms.[26]

We hypothesized that facecards might improve other important aspects of the patient‐physicians relationship. Although levels of patient satisfaction were slightly higher in patients who had received facecards, the results were not statistically significant. Levels of trust and agreement were minimally higher in patients who received facecards, and the results were not statistically significant. Notably, baseline levels of trust and agreement were higher than we had expected. In fact, levels of trust were nearly identical to those seen in a prior study of outpatients who had been with the same physician for a median of 4 years.[22] Patients in our study may have had high levels of trust in the hospital and transferred this trust to their assigned physicians as representatives of the organization. The high level of agreement may relate to patients' tendency to prefer a more passive role as they encounter serious illness.[27, 28] Paradoxically, these findings may impede optimal patient care. The high levels of trust and agreement in the current study suggest that patients may not question their physicians to clarify plans and the rationale behind them. Prior research has shown that deficits in patients' comprehension of the care plan are often not apparent to patients or their physicians.[4, 29, 30]

Our study has several limitations. First, we assessed an intervention involving 4 units in a single hospital. Generalizability may be limited, as physician‐staffing models, hospitals, and the patients they serve vary. Second, as previously mentioned, patients in the intervention group did not receive physician facecards as consistently as intended. We conducted analyses based on treatment received in an effort to evaluate the impact of facecards if optimally delivered. Third, questions assessing satisfaction, trust, and agreement did not specifically ask patients to reflect on care provided by the primary physician team. It is possible that interactions with other physicians (ie, consultants) may have influenced these results. Fourth, we were underpowered to detect statistically significant improvements in satisfaction, trust, or agreement resulting from our intervention. Assuming the intervention might truly improve satisfaction with physicians from 54.2% to 63.6%, we would have needed 900 patients (ie, 450 each for the intervention and control groups) to have 80% power to detect a statistically significant difference. However, our results show that patients have high levels of trust and agreement with hospital physicians despite the relative lack of familiarity. Therefore, any existing deficits in hospitalized patients' comprehension of the care plan do not appear to be exacerbated by a lack of trust and/or agreement with treating physicians.

CONCLUSION

In summary, we found that physician facecards significantly improved patients' knowledge of the names and roles of hospital physicians but had little to no impact on satisfaction, trust, or agreement with physicians. Baseline levels of satisfaction, trust, and agreement were high, suggesting lack of familiarity with hospital physicians does not impede these important aspects of the patient‐physician relationship. Larger studies are needed to definitively assess the impact of facecards on satisfaction, trust, and agreement with physicians.

The patient‐physician relationship is fundamental to safe and effective care. Hospital settings present unique challenges to this partnership, including the lack of a prior relationship for hospital‐based physicians, rapid pace of clinical care, and dynamic nature of inpatient medical teams. Prior studies document that a majority of hospitalized patients are unable to correctly identify their physicians or nurses, and patients in teaching hospitals have difficulty understanding their physicians' level of training.[1, 2, 3, 4] Acknowledging these deficits, professional societies and the Accreditation Council for Graduate Medical Education (ACMGE) have issued policies stating that patients and caregivers need to know who is responsible at every point during patient care.[5, 6] These policies do not, however, make recommendations on methods to achieve better understanding.

Simple interventions improve patients' ability to correctly identify the names and roles of their hospital physicians. Maniaci and colleagues found that patients were better able to identify attending physicians when their names were written on the dry‐erase board in the room.[7] Arora and colleagues asked hospital physicians to give facecards, which included their picture and a description of their role, to patients.[8] Patients were more likely to correctly identify 1 physicians, but, surprisingly, less likely to understand physicians' roles. In a similar study, Francis and colleagues placed photographs with names of the attending and resident physicians on the wall in patient rooms.[9] Patients who had photographs of their physicians on the wall were more likely to correctly identify physicians on their team compared with patients who had no photographs. Additionally, patients who were able to identify more physicians rated satisfaction with physicians higher in 2 of 6 survey questions used. However, the study was limited by the use of a nonvalidated instrument to assess patient satisfaction and the use of an intermediate outcome (ie, ability to identify physicians) as the independent variable rather than the intervention itself (ie, physician photographs).

Beyond satisfaction, lack of familiarity may negatively impact patients' trust and agreement with hospital physicians. Trust and agreement are important predictors of adherence to recommended treatment in outpatient settings[10, 11, 12, 13, 14, 15, 16, 17, 18] but have not been adequately evaluated in hospital settings. Therefore, we sought to pilot the use of physician facecards and assess their potential impact on patients' knowledge of physicians' names and roles as well as patient satisfaction, trust, and agreement with physicians.

METHODS

Physician Facecard

We created draft physician facecards featuring pictures of physicians and descriptions of their roles. We used Lexile analysis, a widely used measure of reading difficulty, to improve readability in an iterative fashion.[20, 21] We then sought feedback at hospitalist and resident meetings. Specifically, we asked for suggested revisions to content and recommendations on reliable methods to deliver facecards to patients. Teaching physicians felt strongly that each team member should be listed and shown on 1 card, which would fit easily into a lab‐coat pocket. We similarly engaged the NMH Patient and Family Advisory Council to seek recommended revisions to content and delivery of the facecards. The Council consists of 18 patient and caregiver members who meet regularly to provide input on hospital programs and proposals. Council members felt strongly that physicians should deliver the cards themselves during their initial introduction, rather than having patients receive cards by other means (eg, as part of unit orientation materials delivered by nonphysician staff members). We incorporated feedback from these stakeholder groups into a final version of the physician facecard and method for delivery (Figure 1).

Figure 1

We implemented the use of facecards from May to June 2012. Physicians on intervention units were informed of the study via email, and one of the co‐investigators (T.C.) distributed a supply of facecards to these physicians at the start of each rotation. This distribution was performed in person, and physicians were instructed to provide a facecard to each new patient during their first encounter. We also placed facecards in easily visible cardholders at the nurses' station on intervention units. Reminder emails were sent once each week to reinforce physician delivery of facecards.

RESULTS

DISCUSSION

We found that receipt of physician facecards significantly improved patients' knowledge of the names and roles of hospital physicians but had little to no impact on satisfaction, trust, or agreement with physicians. Our finding of improved knowledge of the names and roles of physician providers is consistent with prior studies using similar interventions.[7, 8, 9] Facecards may have prompted more effective introductions on the part of physicians and may have served as memory aids for patients to better retain information about their newly introduced hospital physicians.

Patient receipt of the facecard on intervention units was incomplete in our study. Despite engagement of physicians in designing cards that could easily fit into lab coats and a robust strategy to inform and motivate physician delivery of facecards, only 68% of intended patients received them. Although not explicitly reported, prior studies appear to have similarly struggled to deliver interventions consistently. Arora and colleagues reported that facecards were visible in only 59% of patients' rooms among those able to correctly identify 1 of their physicians.[8] A post hoc survey of physicians involved in our study revealed the biggest impediment to delivering facecards was simply forgetting to do so (data not shown). Technologic innovations may help by automating the identification of providers. For example, the University of Pittsburgh Medical Center has piloted smart rooms that use sensor technology to announce the name and role of providers as they enter patients' rooms.[26]

We hypothesized that facecards might improve other important aspects of the patient‐physicians relationship. Although levels of patient satisfaction were slightly higher in patients who had received facecards, the results were not statistically significant. Levels of trust and agreement were minimally higher in patients who received facecards, and the results were not statistically significant. Notably, baseline levels of trust and agreement were higher than we had expected. In fact, levels of trust were nearly identical to those seen in a prior study of outpatients who had been with the same physician for a median of 4 years.[22] Patients in our study may have had high levels of trust in the hospital and transferred this trust to their assigned physicians as representatives of the organization. The high level of agreement may relate to patients' tendency to prefer a more passive role as they encounter serious illness.[27, 28] Paradoxically, these findings may impede optimal patient care. The high levels of trust and agreement in the current study suggest that patients may not question their physicians to clarify plans and the rationale behind them. Prior research has shown that deficits in patients' comprehension of the care plan are often not apparent to patients or their physicians.[4, 29, 30]

Our study has several limitations. First, we assessed an intervention involving 4 units in a single hospital. Generalizability may be limited, as physician‐staffing models, hospitals, and the patients they serve vary. Second, as previously mentioned, patients in the intervention group did not receive physician facecards as consistently as intended. We conducted analyses based on treatment received in an effort to evaluate the impact of facecards if optimally delivered. Third, questions assessing satisfaction, trust, and agreement did not specifically ask patients to reflect on care provided by the primary physician team. It is possible that interactions with other physicians (ie, consultants) may have influenced these results. Fourth, we were underpowered to detect statistically significant improvements in satisfaction, trust, or agreement resulting from our intervention. Assuming the intervention might truly improve satisfaction with physicians from 54.2% to 63.6%, we would have needed 900 patients (ie, 450 each for the intervention and control groups) to have 80% power to detect a statistically significant difference. However, our results show that patients have high levels of trust and agreement with hospital physicians despite the relative lack of familiarity. Therefore, any existing deficits in hospitalized patients' comprehension of the care plan do not appear to be exacerbated by a lack of trust and/or agreement with treating physicians.

CONCLUSION

In summary, we found that physician facecards significantly improved patients' knowledge of the names and roles of hospital physicians but had little to no impact on satisfaction, trust, or agreement with physicians. Baseline levels of satisfaction, trust, and agreement were high, suggesting lack of familiarity with hospital physicians does not impede these important aspects of the patient‐physician relationship. Larger studies are needed to definitively assess the impact of facecards on satisfaction, trust, and agreement with physicians.

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.

Hospital settings present unique challenges to patient‐clinician communication and collaboration. Patients frequently have multiple, active conditions. Interprofessional teams are large and care for multiple patients at the same time, and team membership is dynamic and dispersed. Moreover, physicians spend relatively little time with patients[1, 2] and seldom receive training in communication skills after medical school.

The Agency for Healthcare Research and Quality (AHRQ) has developed the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey to assess hospitalized patients' experiences with care.[3, 4, 5] Results are publicly reported on the US Department of Health and Human Services Hospital Compare Web site[6] and now affect hospital payment through the Center for Medicare and Medicaid Services Hospital Value‐Based Purchasing Program.[7]

Despite this increased transparency and accountability for performance related to the patient experience, little research has been conducted on how hospitals or clinicians might improve performance. Although interventions to enhance physician communication skills have shown improvements in observed behaviors, few studies have assessed benefit from the patient's perspective and few interventions have been integrated into practice.[8] We sought to assess the impact of a communication‐skills training program, based on a common framework used by hospitals, on patient satisfaction with doctor communication and overall hospital care.

METHODS

RESULTS

Overall, 61 (97%) of 63 hospitalists completed the first session, 44 (70%) completed the second session, and 25 (40%) completed the third session of program. Patient‐satisfaction data were available for 278 patients during the preintervention period and 186 patients during the postintervention period. Patient demographic characteristics were similar for the 2 periods (Table 2).

DISCUSSION

We found no significant improvement in patient satisfaction with doctor communication or overall rating of hospital care after implementation of a communication‐skills training program for hospitalists. There are several potential explanations for our results. First, though we used sound educational methods and attempted to replicate common clinical scenarios during simulation exercises, our program may not have resulted in improved communication behaviors during actual clinical care. We attempted to balance instructional methods that would result in behavioral change with a feasible investment of time and effort on the part of our learners (ie, practicing hospitalists). It is possible that additional time, feedback, and practice of communication skills would be necessary to change behaviors in the clinical setting. However, prior communication‐skills interventions have similarly struggled to show an impact on patient satisfaction.[13, 14] Second, we had incomplete participation in the program, with only 40% of hospitalists completing all 3 planned sessions. We encouraged all hospitalists, regardless of job type, to participate in the program. Participation rates were lower for 1‐year hospitalists compared with career hospitalists. The results of our analyses based on level of hospitalist participation in the training program, although not achieving statistical significance, suggest a greater effect of the program with higher degrees of participation.

Most important, the study was likely underpowered to detect a statistically significant difference in satisfaction results. Leaders were committed to providing communication‐skills training throughout our organization. We did not know the magnitude of potential improvement in satisfaction scores that might arise from our efforts, and therefore we did not conduct power calculations before designing and implementing the training program. Our HCAHPS composite doctor‐communication domain performance was 76% during the preintervention period and 79% during the postintervention period. Assuming an absolute 3% improvement is indeed possible, we would have needed >3000 patients in each period to have 80% power to detect a significant difference. Similarly, we would have needed >2000 patients during each period to have 80% power to detect an absolute 4% improvement in global rating of hospital care.

In an attempt to discern whether our favorable results were due to secular trends, we conducted post hoc analyses of HCAHPS nurse‐communication and hospital‐environment domains for the preintervention vs postintervention periods. Two of the 3 nurse‐communication items were rated lower during the postintervention period, but no result was statistically significant. Both hospital‐environment domain items were rated lower during the postintervention period, and 1 result was statistically significant (quiet at night). This post hoc evaluation lends additional support to the potential benefit of the communication‐skills training program.

The findings from this study represent an important issue for leaders attempting to improve quality performance within their organizations. What level of proof is needed before investing time and effort in implementing an intervention? With mounting pressure to improve performance, leaders are often left to make informed decisions based on data that fall short of scientifically rigorous evidence. Importantly, an increase in composite doctor‐communication ratings from 76% to 79% would translate into an improvement from the 25th percentile to 50th‐percentile performance in the fiscal‐year 2011 Press Ganey University Healthcare Consortium benchmark comparison (based on surveys received from September 1, 2010, to August 31, 2011).[15]

Our study has several limitations. First, we assessed an intervention on a single service in a single hospital. Generalizability may be limited, as hospital medicine groups, hospitals, and the patients they serve vary. Second, our intervention was based on a framework (ie, AIDET) that has face validity but has not undergone extensive study to confirm that the underlying constructs, and the behaviors related to them, are tightly linked to patient satisfaction. Third, as previously mentioned, we were likely underpowered to detect a significant improvement in satisfaction resulting from our intervention. Incomplete participation in the training program may have also limited the effect of our intervention. Finally, our comparisons by hospitalist level of participation were based on the discharging physician. Attribution of a patient response to a single physician is problematic because many patients encounter more than 1 hospitalist and 1 or more specialist physicians during their stay.

CONCLUSION

In summary, we found improvements in patient satisfaction with doctor communication, which were not statistically significant, after implementation of a communication‐skills training program for hospitalists. Larger studies are needed to assess whether a communication‐skills training program can truly improve patient satisfaction with doctor communication and overall hospital care.

Hospital settings present unique challenges to patient‐clinician communication and collaboration. Patients frequently have multiple, active conditions. Interprofessional teams are large and care for multiple patients at the same time, and team membership is dynamic and dispersed. Moreover, physicians spend relatively little time with patients[1, 2] and seldom receive training in communication skills after medical school.

The Agency for Healthcare Research and Quality (AHRQ) has developed the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey to assess hospitalized patients' experiences with care.[3, 4, 5] Results are publicly reported on the US Department of Health and Human Services Hospital Compare Web site[6] and now affect hospital payment through the Center for Medicare and Medicaid Services Hospital Value‐Based Purchasing Program.[7]

Despite this increased transparency and accountability for performance related to the patient experience, little research has been conducted on how hospitals or clinicians might improve performance. Although interventions to enhance physician communication skills have shown improvements in observed behaviors, few studies have assessed benefit from the patient's perspective and few interventions have been integrated into practice.[8] We sought to assess the impact of a communication‐skills training program, based on a common framework used by hospitals, on patient satisfaction with doctor communication and overall hospital care.

METHODS

RESULTS

Overall, 61 (97%) of 63 hospitalists completed the first session, 44 (70%) completed the second session, and 25 (40%) completed the third session of program. Patient‐satisfaction data were available for 278 patients during the preintervention period and 186 patients during the postintervention period. Patient demographic characteristics were similar for the 2 periods (Table 2).

DISCUSSION

We found no significant improvement in patient satisfaction with doctor communication or overall rating of hospital care after implementation of a communication‐skills training program for hospitalists. There are several potential explanations for our results. First, though we used sound educational methods and attempted to replicate common clinical scenarios during simulation exercises, our program may not have resulted in improved communication behaviors during actual clinical care. We attempted to balance instructional methods that would result in behavioral change with a feasible investment of time and effort on the part of our learners (ie, practicing hospitalists). It is possible that additional time, feedback, and practice of communication skills would be necessary to change behaviors in the clinical setting. However, prior communication‐skills interventions have similarly struggled to show an impact on patient satisfaction.[13, 14] Second, we had incomplete participation in the program, with only 40% of hospitalists completing all 3 planned sessions. We encouraged all hospitalists, regardless of job type, to participate in the program. Participation rates were lower for 1‐year hospitalists compared with career hospitalists. The results of our analyses based on level of hospitalist participation in the training program, although not achieving statistical significance, suggest a greater effect of the program with higher degrees of participation.

Most important, the study was likely underpowered to detect a statistically significant difference in satisfaction results. Leaders were committed to providing communication‐skills training throughout our organization. We did not know the magnitude of potential improvement in satisfaction scores that might arise from our efforts, and therefore we did not conduct power calculations before designing and implementing the training program. Our HCAHPS composite doctor‐communication domain performance was 76% during the preintervention period and 79% during the postintervention period. Assuming an absolute 3% improvement is indeed possible, we would have needed >3000 patients in each period to have 80% power to detect a significant difference. Similarly, we would have needed >2000 patients during each period to have 80% power to detect an absolute 4% improvement in global rating of hospital care.

In an attempt to discern whether our favorable results were due to secular trends, we conducted post hoc analyses of HCAHPS nurse‐communication and hospital‐environment domains for the preintervention vs postintervention periods. Two of the 3 nurse‐communication items were rated lower during the postintervention period, but no result was statistically significant. Both hospital‐environment domain items were rated lower during the postintervention period, and 1 result was statistically significant (quiet at night). This post hoc evaluation lends additional support to the potential benefit of the communication‐skills training program.

The findings from this study represent an important issue for leaders attempting to improve quality performance within their organizations. What level of proof is needed before investing time and effort in implementing an intervention? With mounting pressure to improve performance, leaders are often left to make informed decisions based on data that fall short of scientifically rigorous evidence. Importantly, an increase in composite doctor‐communication ratings from 76% to 79% would translate into an improvement from the 25th percentile to 50th‐percentile performance in the fiscal‐year 2011 Press Ganey University Healthcare Consortium benchmark comparison (based on surveys received from September 1, 2010, to August 31, 2011).[15]

Our study has several limitations. First, we assessed an intervention on a single service in a single hospital. Generalizability may be limited, as hospital medicine groups, hospitals, and the patients they serve vary. Second, our intervention was based on a framework (ie, AIDET) that has face validity but has not undergone extensive study to confirm that the underlying constructs, and the behaviors related to them, are tightly linked to patient satisfaction. Third, as previously mentioned, we were likely underpowered to detect a significant improvement in satisfaction resulting from our intervention. Incomplete participation in the training program may have also limited the effect of our intervention. Finally, our comparisons by hospitalist level of participation were based on the discharging physician. Attribution of a patient response to a single physician is problematic because many patients encounter more than 1 hospitalist and 1 or more specialist physicians during their stay.

CONCLUSION

In summary, we found improvements in patient satisfaction with doctor communication, which were not statistically significant, after implementation of a communication‐skills training program for hospitalists. Larger studies are needed to assess whether a communication‐skills training program can truly improve patient satisfaction with doctor communication and overall hospital care.

Hospital settings present unique challenges to patient‐clinician communication and collaboration. Patients frequently have multiple, active conditions. Interprofessional teams are large and care for multiple patients at the same time, and team membership is dynamic and dispersed. Moreover, physicians spend relatively little time with patients[1, 2] and seldom receive training in communication skills after medical school.

The Agency for Healthcare Research and Quality (AHRQ) has developed the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey to assess hospitalized patients' experiences with care.[3, 4, 5] Results are publicly reported on the US Department of Health and Human Services Hospital Compare Web site[6] and now affect hospital payment through the Center for Medicare and Medicaid Services Hospital Value‐Based Purchasing Program.[7]

Despite this increased transparency and accountability for performance related to the patient experience, little research has been conducted on how hospitals or clinicians might improve performance. Although interventions to enhance physician communication skills have shown improvements in observed behaviors, few studies have assessed benefit from the patient's perspective and few interventions have been integrated into practice.[8] We sought to assess the impact of a communication‐skills training program, based on a common framework used by hospitals, on patient satisfaction with doctor communication and overall hospital care.

METHODS

RESULTS

Overall, 61 (97%) of 63 hospitalists completed the first session, 44 (70%) completed the second session, and 25 (40%) completed the third session of program. Patient‐satisfaction data were available for 278 patients during the preintervention period and 186 patients during the postintervention period. Patient demographic characteristics were similar for the 2 periods (Table 2).

DISCUSSION

We found no significant improvement in patient satisfaction with doctor communication or overall rating of hospital care after implementation of a communication‐skills training program for hospitalists. There are several potential explanations for our results. First, though we used sound educational methods and attempted to replicate common clinical scenarios during simulation exercises, our program may not have resulted in improved communication behaviors during actual clinical care. We attempted to balance instructional methods that would result in behavioral change with a feasible investment of time and effort on the part of our learners (ie, practicing hospitalists). It is possible that additional time, feedback, and practice of communication skills would be necessary to change behaviors in the clinical setting. However, prior communication‐skills interventions have similarly struggled to show an impact on patient satisfaction.[13, 14] Second, we had incomplete participation in the program, with only 40% of hospitalists completing all 3 planned sessions. We encouraged all hospitalists, regardless of job type, to participate in the program. Participation rates were lower for 1‐year hospitalists compared with career hospitalists. The results of our analyses based on level of hospitalist participation in the training program, although not achieving statistical significance, suggest a greater effect of the program with higher degrees of participation.

Most important, the study was likely underpowered to detect a statistically significant difference in satisfaction results. Leaders were committed to providing communication‐skills training throughout our organization. We did not know the magnitude of potential improvement in satisfaction scores that might arise from our efforts, and therefore we did not conduct power calculations before designing and implementing the training program. Our HCAHPS composite doctor‐communication domain performance was 76% during the preintervention period and 79% during the postintervention period. Assuming an absolute 3% improvement is indeed possible, we would have needed >3000 patients in each period to have 80% power to detect a significant difference. Similarly, we would have needed >2000 patients during each period to have 80% power to detect an absolute 4% improvement in global rating of hospital care.

In an attempt to discern whether our favorable results were due to secular trends, we conducted post hoc analyses of HCAHPS nurse‐communication and hospital‐environment domains for the preintervention vs postintervention periods. Two of the 3 nurse‐communication items were rated lower during the postintervention period, but no result was statistically significant. Both hospital‐environment domain items were rated lower during the postintervention period, and 1 result was statistically significant (quiet at night). This post hoc evaluation lends additional support to the potential benefit of the communication‐skills training program.

The findings from this study represent an important issue for leaders attempting to improve quality performance within their organizations. What level of proof is needed before investing time and effort in implementing an intervention? With mounting pressure to improve performance, leaders are often left to make informed decisions based on data that fall short of scientifically rigorous evidence. Importantly, an increase in composite doctor‐communication ratings from 76% to 79% would translate into an improvement from the 25th percentile to 50th‐percentile performance in the fiscal‐year 2011 Press Ganey University Healthcare Consortium benchmark comparison (based on surveys received from September 1, 2010, to August 31, 2011).[15]

Our study has several limitations. First, we assessed an intervention on a single service in a single hospital. Generalizability may be limited, as hospital medicine groups, hospitals, and the patients they serve vary. Second, our intervention was based on a framework (ie, AIDET) that has face validity but has not undergone extensive study to confirm that the underlying constructs, and the behaviors related to them, are tightly linked to patient satisfaction. Third, as previously mentioned, we were likely underpowered to detect a significant improvement in satisfaction resulting from our intervention. Incomplete participation in the training program may have also limited the effect of our intervention. Finally, our comparisons by hospitalist level of participation were based on the discharging physician. Attribution of a patient response to a single physician is problematic because many patients encounter more than 1 hospitalist and 1 or more specialist physicians during their stay.

CONCLUSION

In summary, we found improvements in patient satisfaction with doctor communication, which were not statistically significant, after implementation of a communication‐skills training program for hospitalists. Larger studies are needed to assess whether a communication‐skills training program can truly improve patient satisfaction with doctor communication and overall hospital care.