Eric Howell, MD

FIELD HOSPITALS DURING THE COVID-19 PANDEMIC

CASES

ABOUT THE BCCFH

ROLE OF THE RRT IN A FIELD HOSPITAL

A FOUR-STEP RAPID RESPONSE FRAMEWORK: CASE CORRELATION

LESSONS FROM THE FIELD HOSPITAL: IMPROVING CLINICAL PERFORMANCE

CONCLUSION

FIELD HOSPITALS DURING THE COVID-19 PANDEMIC

CASES

ABOUT THE BCCFH

ROLE OF THE RRT IN A FIELD HOSPITAL

A FOUR-STEP RAPID RESPONSE FRAMEWORK: CASE CORRELATION

LESSONS FROM THE FIELD HOSPITAL: IMPROVING CLINICAL PERFORMANCE

CONCLUSION

FIELD HOSPITALS DURING THE COVID-19 PANDEMIC

CASES

ABOUT THE BCCFH

ROLE OF THE RRT IN A FIELD HOSPITAL

A FOUR-STEP RAPID RESPONSE FRAMEWORK: CASE CORRELATION

LESSONS FROM THE FIELD HOSPITAL: IMPROVING CLINICAL PERFORMANCE

CONCLUSION

Approximately 70 million adults within the United States have sleep disorders,[1] and up to 30% of adults report sleeping less than 6 hours per night.[2] Poor sleep has been associated with undesirable health outcomes.[1] Suboptimal sleep duration and sleep quality has been associated with a higher prevalence of chronic health conditions including hypertension, type 2 diabetes, coronary artery disease, stroke, and obesity, as well as increased overall mortality.[3, 4, 5, 6, 7]

Sleep plays an important role in restoration of wellness. Poor sleep is associated with physiological disturbances that may result in poor healing.[8, 9, 10] In the literature, prevalence of insomnia among elderly hospitalized patients was 36.7%,[11] whereas in younger hospitalized patients it was 50%.[12] Hospitalized patients frequently cite their acute illness, hospital‐related environmental factors, and disruptions that are part of routine care as causes for poor sleep during hospitalization.[13, 14, 15] Although the pervasiveness of poor sleep among hospitalized patients is high, interventions that prioritize sleep optimization as routine care, are uncommon. Few studies have reviewed the effect of sleep‐promoting measures on both sleep quality and sleep duration among patients hospitalized on general medicine units.

In this study, we aimed to assess the feasibility of incorporating sleep‐promoting interventions on a general medicine unit. We sought to identify differences in sleep measures between intervention and control groups. The primary outcome that we hoped to influence and lengthen in the intervention group was sleep duration. This outcome was measured both by sleep diary and with actigraphy. Secondary outcomes that we hypothesized should improve in the intervention group included feeling more refreshed in the mornings, sleep efficiency, and fewer sleep disruptions. As a feasibility pilot, we also wanted to explore the ease or difficulty with which sleep‐promoting interventions could be incorporated to the team's workflow.

METHODS

RESULTS

Of the 112 study patients, 48 were in the intervention unit and 64 in the control unit. Eighty‐five percent of study participants endorsed poor sleep prior to hospital admission on the PSQI sleep quality measure, which was similar in both groups (Table 1).

Participants completed 1 to 8 sleep diary entries (mean = 2.5, standard deviation = 1.1). Because only 6 participants completed 5 or more diaries, we constrained the number of diaries included in the inferential analysis to 4 to avoid influential outliers identified by scatterplots. Fifty‐seven percent of participants had 1 night of valid actigraphy data (n = 64); 29%, 2 nights (n = 32), 8% had 3 or 4 nights, and 9 participants did not have any usable actigraphy data. The extent to which the intervention was accepted by patients in the intervention group was highly variable. Unit‐wide patient adherence with the 10 _pm lights off, telephone off, and TV off policy was 87%, 67%, and 64% of intervention patients, respectively. Uptake of sleep menu items was also highly variable, and not a single element was used by more than half of patients (acceptance rates ranged from 11% to 44%). Eye masks (44%) and ear plugs (32%) were the most commonly utilized items.

A greater proportion of patients in the control arm (33%) had been taking sleep medications prior to hospitalization compared to the intervention arm (15%; ² = 4.6, P < 0.05). However, hypnotic medication use in the hospital was similar across the both groups (intervention unit patients: 25% and controls: 21%, P = 0.49).

Intraclass correlations for the 7 sleep outcomes ranged from 0.59 to 0.76 on sleep diary outcomes, and from 0.61 to 0.85 on actigraphy. Dependency of sleep measures within patients accounted for 59% to 85% of variance in sleep outcomes. The best‐fit mixed models included random intercepts only. The results of mixed models testing the main effect of intervention versus comparison arm on sleep outcome measures, adjusted for covariates, are presented in Table 2. Total sleep time was the only outcome that was significantly different between groups; the average total sleep time, calculated from sleep diary data, was longer in the intervention group by 49 minutes.

Table 3 lists slopes representing average change in sleep measures over hospital days in both groups. The P values represent z tests of interaction terms in mixed models, after adjustment for covariates, testing whether slopes significantly differed between groups. Of the 7 outcomes, 3 sleep diary measures had significant interaction terms. For ratings of sleep quality, refreshing sleep, and sleep disruptions, slopes in the control group were flat, whereas slopes in the intervention group demonstrated improvements in ratings of sleep quality and refreshed sleep, and a decrease in the impact of sleep disruptions over the course of subsequent nights in the hospital. Figure 1 illustrates a plot of the adjusted average slopes for the refreshed sleep score across hospital days in intervention and control groups.

Figure 1

DISCUSSION

Poor sleep is common among hospitalized adults, both at home prior to the admission and especially when in the hospital. This pilot study demonstrated the feasibility of rolling out a sleep‐promoting intervention on a hospital's general medicine unit. Although participants on the intervention unit reported improved sleep quality and feeling more refreshed, this was not supported by actigraphy data (such as sleep time or sleep efficiency). Although care team engagement and implementation of unit‐wide interventions were high, patient use of individual components was imperfect. Of particular interest, however, the intervention group actually began to have improved sleep quality and fewer disruptions with subsequent nights sleeping in the hospital.

Our findings of the high prevalence of poor sleep among hospitalized patients is congruent with prior studies and supports the great need to screen for and address poor sleep within the hospital setting.[24, 25, 26] Attempts to promote sleep among hospitalized patients may be effective. Prior literature on sleep‐promoting intervention studies demonstrated relaxation techniques improved sleep quality by almost 38%,[27] and ear plugs and eye masks showed some benefit in promoting sleep within the hospital.[28] Our study's multicomponent intervention that attempted to minimize disruptions led to improvement in sleep quality, more restorative sleep, and decreased report of sleep disruptions, especially among patients who had a longer length of stay. As suggested by Thomas et al.[29] and seen in our data, this temporal relationship with improvement across subsequent nights suggests there may be an adaptation to the new environment and that it may take time for the sleep intervention to work.

Hospitalized patients often fail to reclaim the much‐needed restorative sleep at the time when they are most vulnerable. Patients cite routine care as the primary cause of sleep disruption, and often recognize the way that the hospital environment interferes with their ability to sleep.[30, 31, 32] The sleep‐promoting interventions used in our study would be characterized by most as low effort[33] and a potential for high yield, even though our patients only appreciated modest improvements in sleep outcomes.

Several limitations of this study should be considered. First, although we had hoped to collect substantial amounts of objective data, the average time of actigraphy observation was less than 48 hours. This may have constrained the group by time interaction analysis with actigraphy data, as studies have shown increased accuracy in actigraphy measures with longer wear.[34] By contrast, the sleep diary survey collected throughout hospitalization yielded significant improvements in consecutive daily measurements. Second, the proximity of the study units raised concern for study contamination, which could have reduced the differences in the outcome measures that may have been observed. Although the physicians work on both units, the nursing and support care teams are distinct and unit dependent. Finally, this was not a randomized trial. Patient assignment to the treatment arms was haphazard and occurred within the hospital's admitting strategy. Allocation of patients to either the intervention or the control group was based on bed availability at the time of admission. Although both groups were similar in most characteristics, more of the control participants reported taking more sleep medications prior to admission as compared to the intervention participants. Fortunately, hypnotic use was not different between groups during the admission, the time when sleep data were being captured.

Overall, this pilot study suggests that patients admitted to general medical ward fail to realize sufficient restorative sleep when they are in the hospital. Sleep disruption is rather frequent. This study demonstrates the opportunity for and feasibility of sleep‐promoting interventions where facilitating sleep is considered to be a top priority and vital component of the healthcare delivery. When trying to improve patients' sleep in the hospital, it may take several consecutive nights to realize a return on investment.

Approximately 70 million adults within the United States have sleep disorders,[1] and up to 30% of adults report sleeping less than 6 hours per night.[2] Poor sleep has been associated with undesirable health outcomes.[1] Suboptimal sleep duration and sleep quality has been associated with a higher prevalence of chronic health conditions including hypertension, type 2 diabetes, coronary artery disease, stroke, and obesity, as well as increased overall mortality.[3, 4, 5, 6, 7]

Sleep plays an important role in restoration of wellness. Poor sleep is associated with physiological disturbances that may result in poor healing.[8, 9, 10] In the literature, prevalence of insomnia among elderly hospitalized patients was 36.7%,[11] whereas in younger hospitalized patients it was 50%.[12] Hospitalized patients frequently cite their acute illness, hospital‐related environmental factors, and disruptions that are part of routine care as causes for poor sleep during hospitalization.[13, 14, 15] Although the pervasiveness of poor sleep among hospitalized patients is high, interventions that prioritize sleep optimization as routine care, are uncommon. Few studies have reviewed the effect of sleep‐promoting measures on both sleep quality and sleep duration among patients hospitalized on general medicine units.

In this study, we aimed to assess the feasibility of incorporating sleep‐promoting interventions on a general medicine unit. We sought to identify differences in sleep measures between intervention and control groups. The primary outcome that we hoped to influence and lengthen in the intervention group was sleep duration. This outcome was measured both by sleep diary and with actigraphy. Secondary outcomes that we hypothesized should improve in the intervention group included feeling more refreshed in the mornings, sleep efficiency, and fewer sleep disruptions. As a feasibility pilot, we also wanted to explore the ease or difficulty with which sleep‐promoting interventions could be incorporated to the team's workflow.

METHODS

RESULTS

Of the 112 study patients, 48 were in the intervention unit and 64 in the control unit. Eighty‐five percent of study participants endorsed poor sleep prior to hospital admission on the PSQI sleep quality measure, which was similar in both groups (Table 1).

Participants completed 1 to 8 sleep diary entries (mean = 2.5, standard deviation = 1.1). Because only 6 participants completed 5 or more diaries, we constrained the number of diaries included in the inferential analysis to 4 to avoid influential outliers identified by scatterplots. Fifty‐seven percent of participants had 1 night of valid actigraphy data (n = 64); 29%, 2 nights (n = 32), 8% had 3 or 4 nights, and 9 participants did not have any usable actigraphy data. The extent to which the intervention was accepted by patients in the intervention group was highly variable. Unit‐wide patient adherence with the 10 _pm lights off, telephone off, and TV off policy was 87%, 67%, and 64% of intervention patients, respectively. Uptake of sleep menu items was also highly variable, and not a single element was used by more than half of patients (acceptance rates ranged from 11% to 44%). Eye masks (44%) and ear plugs (32%) were the most commonly utilized items.

A greater proportion of patients in the control arm (33%) had been taking sleep medications prior to hospitalization compared to the intervention arm (15%; ² = 4.6, P < 0.05). However, hypnotic medication use in the hospital was similar across the both groups (intervention unit patients: 25% and controls: 21%, P = 0.49).

Intraclass correlations for the 7 sleep outcomes ranged from 0.59 to 0.76 on sleep diary outcomes, and from 0.61 to 0.85 on actigraphy. Dependency of sleep measures within patients accounted for 59% to 85% of variance in sleep outcomes. The best‐fit mixed models included random intercepts only. The results of mixed models testing the main effect of intervention versus comparison arm on sleep outcome measures, adjusted for covariates, are presented in Table 2. Total sleep time was the only outcome that was significantly different between groups; the average total sleep time, calculated from sleep diary data, was longer in the intervention group by 49 minutes.

Table 3 lists slopes representing average change in sleep measures over hospital days in both groups. The P values represent z tests of interaction terms in mixed models, after adjustment for covariates, testing whether slopes significantly differed between groups. Of the 7 outcomes, 3 sleep diary measures had significant interaction terms. For ratings of sleep quality, refreshing sleep, and sleep disruptions, slopes in the control group were flat, whereas slopes in the intervention group demonstrated improvements in ratings of sleep quality and refreshed sleep, and a decrease in the impact of sleep disruptions over the course of subsequent nights in the hospital. Figure 1 illustrates a plot of the adjusted average slopes for the refreshed sleep score across hospital days in intervention and control groups.

Figure 1

DISCUSSION

Poor sleep is common among hospitalized adults, both at home prior to the admission and especially when in the hospital. This pilot study demonstrated the feasibility of rolling out a sleep‐promoting intervention on a hospital's general medicine unit. Although participants on the intervention unit reported improved sleep quality and feeling more refreshed, this was not supported by actigraphy data (such as sleep time or sleep efficiency). Although care team engagement and implementation of unit‐wide interventions were high, patient use of individual components was imperfect. Of particular interest, however, the intervention group actually began to have improved sleep quality and fewer disruptions with subsequent nights sleeping in the hospital.

Our findings of the high prevalence of poor sleep among hospitalized patients is congruent with prior studies and supports the great need to screen for and address poor sleep within the hospital setting.[24, 25, 26] Attempts to promote sleep among hospitalized patients may be effective. Prior literature on sleep‐promoting intervention studies demonstrated relaxation techniques improved sleep quality by almost 38%,[27] and ear plugs and eye masks showed some benefit in promoting sleep within the hospital.[28] Our study's multicomponent intervention that attempted to minimize disruptions led to improvement in sleep quality, more restorative sleep, and decreased report of sleep disruptions, especially among patients who had a longer length of stay. As suggested by Thomas et al.[29] and seen in our data, this temporal relationship with improvement across subsequent nights suggests there may be an adaptation to the new environment and that it may take time for the sleep intervention to work.

Hospitalized patients often fail to reclaim the much‐needed restorative sleep at the time when they are most vulnerable. Patients cite routine care as the primary cause of sleep disruption, and often recognize the way that the hospital environment interferes with their ability to sleep.[30, 31, 32] The sleep‐promoting interventions used in our study would be characterized by most as low effort[33] and a potential for high yield, even though our patients only appreciated modest improvements in sleep outcomes.

Several limitations of this study should be considered. First, although we had hoped to collect substantial amounts of objective data, the average time of actigraphy observation was less than 48 hours. This may have constrained the group by time interaction analysis with actigraphy data, as studies have shown increased accuracy in actigraphy measures with longer wear.[34] By contrast, the sleep diary survey collected throughout hospitalization yielded significant improvements in consecutive daily measurements. Second, the proximity of the study units raised concern for study contamination, which could have reduced the differences in the outcome measures that may have been observed. Although the physicians work on both units, the nursing and support care teams are distinct and unit dependent. Finally, this was not a randomized trial. Patient assignment to the treatment arms was haphazard and occurred within the hospital's admitting strategy. Allocation of patients to either the intervention or the control group was based on bed availability at the time of admission. Although both groups were similar in most characteristics, more of the control participants reported taking more sleep medications prior to admission as compared to the intervention participants. Fortunately, hypnotic use was not different between groups during the admission, the time when sleep data were being captured.

Overall, this pilot study suggests that patients admitted to general medical ward fail to realize sufficient restorative sleep when they are in the hospital. Sleep disruption is rather frequent. This study demonstrates the opportunity for and feasibility of sleep‐promoting interventions where facilitating sleep is considered to be a top priority and vital component of the healthcare delivery. When trying to improve patients' sleep in the hospital, it may take several consecutive nights to realize a return on investment.

Approximately 70 million adults within the United States have sleep disorders,[1] and up to 30% of adults report sleeping less than 6 hours per night.[2] Poor sleep has been associated with undesirable health outcomes.[1] Suboptimal sleep duration and sleep quality has been associated with a higher prevalence of chronic health conditions including hypertension, type 2 diabetes, coronary artery disease, stroke, and obesity, as well as increased overall mortality.[3, 4, 5, 6, 7]

Sleep plays an important role in restoration of wellness. Poor sleep is associated with physiological disturbances that may result in poor healing.[8, 9, 10] In the literature, prevalence of insomnia among elderly hospitalized patients was 36.7%,[11] whereas in younger hospitalized patients it was 50%.[12] Hospitalized patients frequently cite their acute illness, hospital‐related environmental factors, and disruptions that are part of routine care as causes for poor sleep during hospitalization.[13, 14, 15] Although the pervasiveness of poor sleep among hospitalized patients is high, interventions that prioritize sleep optimization as routine care, are uncommon. Few studies have reviewed the effect of sleep‐promoting measures on both sleep quality and sleep duration among patients hospitalized on general medicine units.

In this study, we aimed to assess the feasibility of incorporating sleep‐promoting interventions on a general medicine unit. We sought to identify differences in sleep measures between intervention and control groups. The primary outcome that we hoped to influence and lengthen in the intervention group was sleep duration. This outcome was measured both by sleep diary and with actigraphy. Secondary outcomes that we hypothesized should improve in the intervention group included feeling more refreshed in the mornings, sleep efficiency, and fewer sleep disruptions. As a feasibility pilot, we also wanted to explore the ease or difficulty with which sleep‐promoting interventions could be incorporated to the team's workflow.

METHODS

RESULTS

Of the 112 study patients, 48 were in the intervention unit and 64 in the control unit. Eighty‐five percent of study participants endorsed poor sleep prior to hospital admission on the PSQI sleep quality measure, which was similar in both groups (Table 1).

Participants completed 1 to 8 sleep diary entries (mean = 2.5, standard deviation = 1.1). Because only 6 participants completed 5 or more diaries, we constrained the number of diaries included in the inferential analysis to 4 to avoid influential outliers identified by scatterplots. Fifty‐seven percent of participants had 1 night of valid actigraphy data (n = 64); 29%, 2 nights (n = 32), 8% had 3 or 4 nights, and 9 participants did not have any usable actigraphy data. The extent to which the intervention was accepted by patients in the intervention group was highly variable. Unit‐wide patient adherence with the 10 _pm lights off, telephone off, and TV off policy was 87%, 67%, and 64% of intervention patients, respectively. Uptake of sleep menu items was also highly variable, and not a single element was used by more than half of patients (acceptance rates ranged from 11% to 44%). Eye masks (44%) and ear plugs (32%) were the most commonly utilized items.

A greater proportion of patients in the control arm (33%) had been taking sleep medications prior to hospitalization compared to the intervention arm (15%; ² = 4.6, P < 0.05). However, hypnotic medication use in the hospital was similar across the both groups (intervention unit patients: 25% and controls: 21%, P = 0.49).

Intraclass correlations for the 7 sleep outcomes ranged from 0.59 to 0.76 on sleep diary outcomes, and from 0.61 to 0.85 on actigraphy. Dependency of sleep measures within patients accounted for 59% to 85% of variance in sleep outcomes. The best‐fit mixed models included random intercepts only. The results of mixed models testing the main effect of intervention versus comparison arm on sleep outcome measures, adjusted for covariates, are presented in Table 2. Total sleep time was the only outcome that was significantly different between groups; the average total sleep time, calculated from sleep diary data, was longer in the intervention group by 49 minutes.

Table 3 lists slopes representing average change in sleep measures over hospital days in both groups. The P values represent z tests of interaction terms in mixed models, after adjustment for covariates, testing whether slopes significantly differed between groups. Of the 7 outcomes, 3 sleep diary measures had significant interaction terms. For ratings of sleep quality, refreshing sleep, and sleep disruptions, slopes in the control group were flat, whereas slopes in the intervention group demonstrated improvements in ratings of sleep quality and refreshed sleep, and a decrease in the impact of sleep disruptions over the course of subsequent nights in the hospital. Figure 1 illustrates a plot of the adjusted average slopes for the refreshed sleep score across hospital days in intervention and control groups.

Figure 1

DISCUSSION

Poor sleep is common among hospitalized adults, both at home prior to the admission and especially when in the hospital. This pilot study demonstrated the feasibility of rolling out a sleep‐promoting intervention on a hospital's general medicine unit. Although participants on the intervention unit reported improved sleep quality and feeling more refreshed, this was not supported by actigraphy data (such as sleep time or sleep efficiency). Although care team engagement and implementation of unit‐wide interventions were high, patient use of individual components was imperfect. Of particular interest, however, the intervention group actually began to have improved sleep quality and fewer disruptions with subsequent nights sleeping in the hospital.

Our findings of the high prevalence of poor sleep among hospitalized patients is congruent with prior studies and supports the great need to screen for and address poor sleep within the hospital setting.[24, 25, 26] Attempts to promote sleep among hospitalized patients may be effective. Prior literature on sleep‐promoting intervention studies demonstrated relaxation techniques improved sleep quality by almost 38%,[27] and ear plugs and eye masks showed some benefit in promoting sleep within the hospital.[28] Our study's multicomponent intervention that attempted to minimize disruptions led to improvement in sleep quality, more restorative sleep, and decreased report of sleep disruptions, especially among patients who had a longer length of stay. As suggested by Thomas et al.[29] and seen in our data, this temporal relationship with improvement across subsequent nights suggests there may be an adaptation to the new environment and that it may take time for the sleep intervention to work.

Hospitalized patients often fail to reclaim the much‐needed restorative sleep at the time when they are most vulnerable. Patients cite routine care as the primary cause of sleep disruption, and often recognize the way that the hospital environment interferes with their ability to sleep.[30, 31, 32] The sleep‐promoting interventions used in our study would be characterized by most as low effort[33] and a potential for high yield, even though our patients only appreciated modest improvements in sleep outcomes.

Several limitations of this study should be considered. First, although we had hoped to collect substantial amounts of objective data, the average time of actigraphy observation was less than 48 hours. This may have constrained the group by time interaction analysis with actigraphy data, as studies have shown increased accuracy in actigraphy measures with longer wear.[34] By contrast, the sleep diary survey collected throughout hospitalization yielded significant improvements in consecutive daily measurements. Second, the proximity of the study units raised concern for study contamination, which could have reduced the differences in the outcome measures that may have been observed. Although the physicians work on both units, the nursing and support care teams are distinct and unit dependent. Finally, this was not a randomized trial. Patient assignment to the treatment arms was haphazard and occurred within the hospital's admitting strategy. Allocation of patients to either the intervention or the control group was based on bed availability at the time of admission. Although both groups were similar in most characteristics, more of the control participants reported taking more sleep medications prior to admission as compared to the intervention participants. Fortunately, hypnotic use was not different between groups during the admission, the time when sleep data were being captured.

Overall, this pilot study suggests that patients admitted to general medical ward fail to realize sufficient restorative sleep when they are in the hospital. Sleep disruption is rather frequent. This study demonstrates the opportunity for and feasibility of sleep‐promoting interventions where facilitating sleep is considered to be a top priority and vital component of the healthcare delivery. When trying to improve patients' sleep in the hospital, it may take several consecutive nights to realize a return on investment.

Patient satisfaction scores are being reported publicly and will affect hospital reimbursement rates under Hospital Value Based Purchasing.[1] Patient satisfaction scores are currently obtained through metrics such as Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS)[2] and Press Ganey (PG)[3] surveys. Such surveys are mailed to a variable proportion of patients following their discharge from the hospital, and ask patients about the quality of care they received during their admission. Domains assessed regarding the patients' inpatient experiences range from room cleanliness to the amount of time the physician spent with them.

The Society of Hospital Medicine (SHM), the largest professional medical society representing hospitalists, encourages the use of patient satisfaction surveys to measure hospitalist providers' quality of patient care.[4] Ideally, accurate information would be delivered as feedback to individual providers in a timely manner in hopes of improving performance; however, the current methodology has shortcomings that limit its usefulness. First, several hospitalists and consultants may be involved in the care of 1 patient during the hospital stay, but the score can only be tied to a single physician. Current survey methods attribute all responses to that particular doctor, usually the attending of record, although patients may very well be thinking of other physicians when responding to questions. Second, only a few questions on the surveys ask about doctors' performance. Aforementioned surveys have 3 to 8 questions about doctors' care, which limits the ability to assess physician performance comprehensively. Finally, the surveys are mailed approximately 1 week after the patient's discharge, usually without a name or photograph of the physician to facilitate patient/caregiver recall. This time lag and lack of information to prompt patient recall likely lead to impreciseness in assessment. In addition, the response rates to these surveys are typically low, around 25% (personal oral communication with our division's service excellence stakeholder Dr. L.P. in September 2013). These deficiencies limit the usefulness of such data in coaching individual providers about their performance because they cannot be delivered in a timely fashion, and the reliability of the attribution is suspect.

With these considerations in mind, we developed and validated a new survey metric, the Tool to Assess Inpatient Satisfaction with Care from Hospitalists (TAISCH). We hypothesized that the results would be different from those collected using conventional methodologies.

PATIENTS AND METHODS

RESULTS

A total of 330 patients were considered to be eligible through medical record screening. Of those patients, 73 (22%) were already discharged by the time the research assistant attempted to enroll them after 2 days of care by a single physician. Of 257 inpatients approached, 30 patients (12%) refused to participate. Among the 227 consented patients, 24 (9%) were excluded as they were unable to correctly identify their hospitalist provider. A total of 203 patients were enrolled, and each patient rated a single hospitalist; a total of 29 unique hospitalists were assessed by these patients. The patients' mean age was 60 years, 114 (56%) were female, and 61 (30%) were of nonwhite race (Table 1). The hospitalist physicians' demographic information is also shown in Table 1. Two hospitalists with fewer than 4 surveys collected were excluded from the analysis. Thus, final analysis included 200 unique patients assessing 1 of the 27 hospitalists (mean=7.4 surveys per hospitalist).

DISCUSSION

Our new metric, TAISCH, was found to be a reliable and valid measurement tool to assess patient satisfaction with the hospitalist physician's care. Because we only surveyed patients who could correctly identify their hospitalist physicians after interacting for at least 2 consecutive days, the attribution of the data to the individual hospitalist is almost certainly correct. The high participation rate indicates that the patients were not hesitant about rating their hospitalist provider's quality of care, even when asked while they were still in the hospital.

The majority of the patients approached were able to correctly identify their hospitalist provider. This rate (91%) was much higher than the rate previously reported in the literature where a picture card was used to improve provider recognition.[22] It is also likely that 1 physician, rather than a team of physicians, taking care of patients make it easier for patients to recall the name and recognize the face of their inpatient provider.

The CFA of TAISCH showed good fit but suggests that 2 variables, both from Kahn's etiquette‐based medicine (EtBM) checklist,[9] may not load in the same way as the other items. Tackett and colleagues reported that hospitalists who performed more EtBM behaviors scored higher on PG evaluations.[23] Such results, along with the comparable explanation of variance and reliability, convinced us to retain these 2 items in the final 15‐item TAISCH as dictated by the CFA. Although the literature supports the fact that physician etiquette is related to perception of high‐quality care, it is possible that these 2 questions were answered differently (and thereby failed to load the same way), because environmental limitations may be preventing physicians' ability to perform them consistently. We prefer the 15‐item version of TAISCH and future studies may provide additional information about its performance as compared to the 13‐item adaptation.

The significantly negative association between the Wong‐Baker pain scale and TAISCH stresses the importance of adequately addressing and treating the patient's pain. Hanna et al. showed that the patients' perceptions of pain control was associated with their overall satisfaction score measured by HCAHPS.[24] The association seen in our study was not unexpected, because TAISCH is administered while the patients are acutely ill in the hospital, when pain is likely more prevalent and severe than it is during the postdischarge settings (when the HCAHPS or PG surveys are administered). Interestingly, Hanna et al. discovered that the team's attention to controlling pain was more strongly correlated with overall satisfaction than was the actual pain control.[24] These data, now confirmed by our study, should serve to remind us that a hospitalist's concern and effort to relieve pain may augment patient satisfaction with the quality of care, even when eliminating the pain may be difficult or impossible.

TAISCH was found not to be correlated with PG scores. Several explanations for this deserve consideration. First, the postdischarge PG survey that is used for our institution does not list the name of the specific hospitalist providers for the patients to evaluate. Because patients encounter multiple physicians during their hospital stay (eg, emergency department physicians, hospitalist providers, consultants), it is possible that patients are not reflecting on the named doctor when assessing the the attending of record on the PG mailed questionnaire. Second, the representation of patients who responded to TAISCH and PG were different; almost all patients completed TAISCH as opposed to a small minority who decide to respond to the PG survey. Third, TAISCH measures the physicians' performance more comprehensively with a larger number of variables. Last, it is possible that we were underpowered to detect significant correlation, because there were only 24 providers who had data from both TAISCH and PG. However, our results endorse using caution in interpreting PG scores for individual hospitalist's performance, particularly for high‐stakes consequences (including the provision of incentives to high performer and the insistence on remediation for low performers).

Several limitations of this study should be considered. First, only hospitalist providers from a single division were assessed. This may limit the generalizability of our findings. Second, although patients were assured about confidentiality of their responses, they might have provided more favorable answers, because they may have felt uncomfortable rating their physician poorly. One review article of the measurement of healthcare satisfaction indicated that impersonal (mailed) methods result in more criticism and lower satisfaction than assessments made in person using interviews. As the trade‐off, the mailed surveys yield lower response rates that may introduce other forms of bias.[25] Even on the HCHAPS survey report for the same period from our institution, 78% of patients gave top box ratings for our doctors' communication skills, which is at the state average.[26] Similarly, a study that used postdischarge telephone interviews to collect patients' satisfaction with hospitalists' care quality reported an average score of 4.20 out of 5.[27] These findings confirm that highly skewed ratings are common for these types of surveys, irrespective of how or when the data are collected.

Despite the aforementioned limitations, TAISCH use need not be limited to hospitalist physicians. It may also be used to assess allied health professionals or trainees performance, which cannot be assessed by HCHAPS or PG. Applying TAISCH in different hospital settings (eg, emergency department or critical care units), assessing hospitalists' reactions to TAISCH, learning whether TAISCH leads to hospitalists' behavior changes or appraising whether performance can improve in response to coaching interventions for those performing poorly are all research questions that merit additional consideration.

CONCLUSION

TAISCH allows for obtaining patient satisfaction data that are highly attributable to specific hospitalist providers. The data collection method also permits high response rates so that input comes from almost all patients. The timeliness of the TAISCH assessments also makes it possible for real‐time service recovery, which is impossible with other commonly used metrics assessing patient satisfaction. Our next step will include testing the most effective way to provide feedback to providers and to coach these individuals so as to improve performance.

Patient satisfaction scores are being reported publicly and will affect hospital reimbursement rates under Hospital Value Based Purchasing.[1] Patient satisfaction scores are currently obtained through metrics such as Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS)[2] and Press Ganey (PG)[3] surveys. Such surveys are mailed to a variable proportion of patients following their discharge from the hospital, and ask patients about the quality of care they received during their admission. Domains assessed regarding the patients' inpatient experiences range from room cleanliness to the amount of time the physician spent with them.

The Society of Hospital Medicine (SHM), the largest professional medical society representing hospitalists, encourages the use of patient satisfaction surveys to measure hospitalist providers' quality of patient care.[4] Ideally, accurate information would be delivered as feedback to individual providers in a timely manner in hopes of improving performance; however, the current methodology has shortcomings that limit its usefulness. First, several hospitalists and consultants may be involved in the care of 1 patient during the hospital stay, but the score can only be tied to a single physician. Current survey methods attribute all responses to that particular doctor, usually the attending of record, although patients may very well be thinking of other physicians when responding to questions. Second, only a few questions on the surveys ask about doctors' performance. Aforementioned surveys have 3 to 8 questions about doctors' care, which limits the ability to assess physician performance comprehensively. Finally, the surveys are mailed approximately 1 week after the patient's discharge, usually without a name or photograph of the physician to facilitate patient/caregiver recall. This time lag and lack of information to prompt patient recall likely lead to impreciseness in assessment. In addition, the response rates to these surveys are typically low, around 25% (personal oral communication with our division's service excellence stakeholder Dr. L.P. in September 2013). These deficiencies limit the usefulness of such data in coaching individual providers about their performance because they cannot be delivered in a timely fashion, and the reliability of the attribution is suspect.

With these considerations in mind, we developed and validated a new survey metric, the Tool to Assess Inpatient Satisfaction with Care from Hospitalists (TAISCH). We hypothesized that the results would be different from those collected using conventional methodologies.

PATIENTS AND METHODS

RESULTS

A total of 330 patients were considered to be eligible through medical record screening. Of those patients, 73 (22%) were already discharged by the time the research assistant attempted to enroll them after 2 days of care by a single physician. Of 257 inpatients approached, 30 patients (12%) refused to participate. Among the 227 consented patients, 24 (9%) were excluded as they were unable to correctly identify their hospitalist provider. A total of 203 patients were enrolled, and each patient rated a single hospitalist; a total of 29 unique hospitalists were assessed by these patients. The patients' mean age was 60 years, 114 (56%) were female, and 61 (30%) were of nonwhite race (Table 1). The hospitalist physicians' demographic information is also shown in Table 1. Two hospitalists with fewer than 4 surveys collected were excluded from the analysis. Thus, final analysis included 200 unique patients assessing 1 of the 27 hospitalists (mean=7.4 surveys per hospitalist).

DISCUSSION

Our new metric, TAISCH, was found to be a reliable and valid measurement tool to assess patient satisfaction with the hospitalist physician's care. Because we only surveyed patients who could correctly identify their hospitalist physicians after interacting for at least 2 consecutive days, the attribution of the data to the individual hospitalist is almost certainly correct. The high participation rate indicates that the patients were not hesitant about rating their hospitalist provider's quality of care, even when asked while they were still in the hospital.

The majority of the patients approached were able to correctly identify their hospitalist provider. This rate (91%) was much higher than the rate previously reported in the literature where a picture card was used to improve provider recognition.[22] It is also likely that 1 physician, rather than a team of physicians, taking care of patients make it easier for patients to recall the name and recognize the face of their inpatient provider.

The CFA of TAISCH showed good fit but suggests that 2 variables, both from Kahn's etiquette‐based medicine (EtBM) checklist,[9] may not load in the same way as the other items. Tackett and colleagues reported that hospitalists who performed more EtBM behaviors scored higher on PG evaluations.[23] Such results, along with the comparable explanation of variance and reliability, convinced us to retain these 2 items in the final 15‐item TAISCH as dictated by the CFA. Although the literature supports the fact that physician etiquette is related to perception of high‐quality care, it is possible that these 2 questions were answered differently (and thereby failed to load the same way), because environmental limitations may be preventing physicians' ability to perform them consistently. We prefer the 15‐item version of TAISCH and future studies may provide additional information about its performance as compared to the 13‐item adaptation.

The significantly negative association between the Wong‐Baker pain scale and TAISCH stresses the importance of adequately addressing and treating the patient's pain. Hanna et al. showed that the patients' perceptions of pain control was associated with their overall satisfaction score measured by HCAHPS.[24] The association seen in our study was not unexpected, because TAISCH is administered while the patients are acutely ill in the hospital, when pain is likely more prevalent and severe than it is during the postdischarge settings (when the HCAHPS or PG surveys are administered). Interestingly, Hanna et al. discovered that the team's attention to controlling pain was more strongly correlated with overall satisfaction than was the actual pain control.[24] These data, now confirmed by our study, should serve to remind us that a hospitalist's concern and effort to relieve pain may augment patient satisfaction with the quality of care, even when eliminating the pain may be difficult or impossible.

TAISCH was found not to be correlated with PG scores. Several explanations for this deserve consideration. First, the postdischarge PG survey that is used for our institution does not list the name of the specific hospitalist providers for the patients to evaluate. Because patients encounter multiple physicians during their hospital stay (eg, emergency department physicians, hospitalist providers, consultants), it is possible that patients are not reflecting on the named doctor when assessing the the attending of record on the PG mailed questionnaire. Second, the representation of patients who responded to TAISCH and PG were different; almost all patients completed TAISCH as opposed to a small minority who decide to respond to the PG survey. Third, TAISCH measures the physicians' performance more comprehensively with a larger number of variables. Last, it is possible that we were underpowered to detect significant correlation, because there were only 24 providers who had data from both TAISCH and PG. However, our results endorse using caution in interpreting PG scores for individual hospitalist's performance, particularly for high‐stakes consequences (including the provision of incentives to high performer and the insistence on remediation for low performers).

Several limitations of this study should be considered. First, only hospitalist providers from a single division were assessed. This may limit the generalizability of our findings. Second, although patients were assured about confidentiality of their responses, they might have provided more favorable answers, because they may have felt uncomfortable rating their physician poorly. One review article of the measurement of healthcare satisfaction indicated that impersonal (mailed) methods result in more criticism and lower satisfaction than assessments made in person using interviews. As the trade‐off, the mailed surveys yield lower response rates that may introduce other forms of bias.[25] Even on the HCHAPS survey report for the same period from our institution, 78% of patients gave top box ratings for our doctors' communication skills, which is at the state average.[26] Similarly, a study that used postdischarge telephone interviews to collect patients' satisfaction with hospitalists' care quality reported an average score of 4.20 out of 5.[27] These findings confirm that highly skewed ratings are common for these types of surveys, irrespective of how or when the data are collected.

Despite the aforementioned limitations, TAISCH use need not be limited to hospitalist physicians. It may also be used to assess allied health professionals or trainees performance, which cannot be assessed by HCHAPS or PG. Applying TAISCH in different hospital settings (eg, emergency department or critical care units), assessing hospitalists' reactions to TAISCH, learning whether TAISCH leads to hospitalists' behavior changes or appraising whether performance can improve in response to coaching interventions for those performing poorly are all research questions that merit additional consideration.

CONCLUSION

TAISCH allows for obtaining patient satisfaction data that are highly attributable to specific hospitalist providers. The data collection method also permits high response rates so that input comes from almost all patients. The timeliness of the TAISCH assessments also makes it possible for real‐time service recovery, which is impossible with other commonly used metrics assessing patient satisfaction. Our next step will include testing the most effective way to provide feedback to providers and to coach these individuals so as to improve performance.

Patient satisfaction scores are being reported publicly and will affect hospital reimbursement rates under Hospital Value Based Purchasing.[1] Patient satisfaction scores are currently obtained through metrics such as Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS)[2] and Press Ganey (PG)[3] surveys. Such surveys are mailed to a variable proportion of patients following their discharge from the hospital, and ask patients about the quality of care they received during their admission. Domains assessed regarding the patients' inpatient experiences range from room cleanliness to the amount of time the physician spent with them.

The Society of Hospital Medicine (SHM), the largest professional medical society representing hospitalists, encourages the use of patient satisfaction surveys to measure hospitalist providers' quality of patient care.[4] Ideally, accurate information would be delivered as feedback to individual providers in a timely manner in hopes of improving performance; however, the current methodology has shortcomings that limit its usefulness. First, several hospitalists and consultants may be involved in the care of 1 patient during the hospital stay, but the score can only be tied to a single physician. Current survey methods attribute all responses to that particular doctor, usually the attending of record, although patients may very well be thinking of other physicians when responding to questions. Second, only a few questions on the surveys ask about doctors' performance. Aforementioned surveys have 3 to 8 questions about doctors' care, which limits the ability to assess physician performance comprehensively. Finally, the surveys are mailed approximately 1 week after the patient's discharge, usually without a name or photograph of the physician to facilitate patient/caregiver recall. This time lag and lack of information to prompt patient recall likely lead to impreciseness in assessment. In addition, the response rates to these surveys are typically low, around 25% (personal oral communication with our division's service excellence stakeholder Dr. L.P. in September 2013). These deficiencies limit the usefulness of such data in coaching individual providers about their performance because they cannot be delivered in a timely fashion, and the reliability of the attribution is suspect.

With these considerations in mind, we developed and validated a new survey metric, the Tool to Assess Inpatient Satisfaction with Care from Hospitalists (TAISCH). We hypothesized that the results would be different from those collected using conventional methodologies.

PATIENTS AND METHODS

RESULTS

A total of 330 patients were considered to be eligible through medical record screening. Of those patients, 73 (22%) were already discharged by the time the research assistant attempted to enroll them after 2 days of care by a single physician. Of 257 inpatients approached, 30 patients (12%) refused to participate. Among the 227 consented patients, 24 (9%) were excluded as they were unable to correctly identify their hospitalist provider. A total of 203 patients were enrolled, and each patient rated a single hospitalist; a total of 29 unique hospitalists were assessed by these patients. The patients' mean age was 60 years, 114 (56%) were female, and 61 (30%) were of nonwhite race (Table 1). The hospitalist physicians' demographic information is also shown in Table 1. Two hospitalists with fewer than 4 surveys collected were excluded from the analysis. Thus, final analysis included 200 unique patients assessing 1 of the 27 hospitalists (mean=7.4 surveys per hospitalist).

DISCUSSION

Our new metric, TAISCH, was found to be a reliable and valid measurement tool to assess patient satisfaction with the hospitalist physician's care. Because we only surveyed patients who could correctly identify their hospitalist physicians after interacting for at least 2 consecutive days, the attribution of the data to the individual hospitalist is almost certainly correct. The high participation rate indicates that the patients were not hesitant about rating their hospitalist provider's quality of care, even when asked while they were still in the hospital.

The majority of the patients approached were able to correctly identify their hospitalist provider. This rate (91%) was much higher than the rate previously reported in the literature where a picture card was used to improve provider recognition.[22] It is also likely that 1 physician, rather than a team of physicians, taking care of patients make it easier for patients to recall the name and recognize the face of their inpatient provider.

The CFA of TAISCH showed good fit but suggests that 2 variables, both from Kahn's etiquette‐based medicine (EtBM) checklist,[9] may not load in the same way as the other items. Tackett and colleagues reported that hospitalists who performed more EtBM behaviors scored higher on PG evaluations.[23] Such results, along with the comparable explanation of variance and reliability, convinced us to retain these 2 items in the final 15‐item TAISCH as dictated by the CFA. Although the literature supports the fact that physician etiquette is related to perception of high‐quality care, it is possible that these 2 questions were answered differently (and thereby failed to load the same way), because environmental limitations may be preventing physicians' ability to perform them consistently. We prefer the 15‐item version of TAISCH and future studies may provide additional information about its performance as compared to the 13‐item adaptation.

The significantly negative association between the Wong‐Baker pain scale and TAISCH stresses the importance of adequately addressing and treating the patient's pain. Hanna et al. showed that the patients' perceptions of pain control was associated with their overall satisfaction score measured by HCAHPS.[24] The association seen in our study was not unexpected, because TAISCH is administered while the patients are acutely ill in the hospital, when pain is likely more prevalent and severe than it is during the postdischarge settings (when the HCAHPS or PG surveys are administered). Interestingly, Hanna et al. discovered that the team's attention to controlling pain was more strongly correlated with overall satisfaction than was the actual pain control.[24] These data, now confirmed by our study, should serve to remind us that a hospitalist's concern and effort to relieve pain may augment patient satisfaction with the quality of care, even when eliminating the pain may be difficult or impossible.

TAISCH was found not to be correlated with PG scores. Several explanations for this deserve consideration. First, the postdischarge PG survey that is used for our institution does not list the name of the specific hospitalist providers for the patients to evaluate. Because patients encounter multiple physicians during their hospital stay (eg, emergency department physicians, hospitalist providers, consultants), it is possible that patients are not reflecting on the named doctor when assessing the the attending of record on the PG mailed questionnaire. Second, the representation of patients who responded to TAISCH and PG were different; almost all patients completed TAISCH as opposed to a small minority who decide to respond to the PG survey. Third, TAISCH measures the physicians' performance more comprehensively with a larger number of variables. Last, it is possible that we were underpowered to detect significant correlation, because there were only 24 providers who had data from both TAISCH and PG. However, our results endorse using caution in interpreting PG scores for individual hospitalist's performance, particularly for high‐stakes consequences (including the provision of incentives to high performer and the insistence on remediation for low performers).

Several limitations of this study should be considered. First, only hospitalist providers from a single division were assessed. This may limit the generalizability of our findings. Second, although patients were assured about confidentiality of their responses, they might have provided more favorable answers, because they may have felt uncomfortable rating their physician poorly. One review article of the measurement of healthcare satisfaction indicated that impersonal (mailed) methods result in more criticism and lower satisfaction than assessments made in person using interviews. As the trade‐off, the mailed surveys yield lower response rates that may introduce other forms of bias.[25] Even on the HCHAPS survey report for the same period from our institution, 78% of patients gave top box ratings for our doctors' communication skills, which is at the state average.[26] Similarly, a study that used postdischarge telephone interviews to collect patients' satisfaction with hospitalists' care quality reported an average score of 4.20 out of 5.[27] These findings confirm that highly skewed ratings are common for these types of surveys, irrespective of how or when the data are collected.

Despite the aforementioned limitations, TAISCH use need not be limited to hospitalist physicians. It may also be used to assess allied health professionals or trainees performance, which cannot be assessed by HCHAPS or PG. Applying TAISCH in different hospital settings (eg, emergency department or critical care units), assessing hospitalists' reactions to TAISCH, learning whether TAISCH leads to hospitalists' behavior changes or appraising whether performance can improve in response to coaching interventions for those performing poorly are all research questions that merit additional consideration.

CONCLUSION

TAISCH allows for obtaining patient satisfaction data that are highly attributable to specific hospitalist providers. The data collection method also permits high response rates so that input comes from almost all patients. The timeliness of the TAISCH assessments also makes it possible for real‐time service recovery, which is impossible with other commonly used metrics assessing patient satisfaction. Our next step will include testing the most effective way to provide feedback to providers and to coach these individuals so as to improve performance.

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.