In 1992, the Joint Commission on Accreditation of Healthcare Organizations (Joint Commission) introduced standards to make hospital buildings smoke‐free, resulting in the nation's first industry‐wide ban on smoking in the workplace. This hospital smoking ban has led to increased smoking cessation among employees.1 Since 2003, core measures from the Joint Commission and quality indicators from the Centers for Medicare and Medicaid Services have included inpatient smoking cessation counseling for acute myocardial infarction, pneumonia, and heart failure, as national guidelines strongly recommend smoking cessation counseling for patients with these diseases who smoke.25

The Department of Health and Human Services (DHHS) 2008 update on Clinical Practice Guidelines for Treating Tobacco Use and Dependence6 recommends that clinicians use hospitalization as an opportunity to promote smoking cessation and to prescribe medications to alleviate withdrawal symptoms. Hospitalization is an opportune time for smoking cessation because patients are restricted to a smoke‐free environment in the hospital and, increasingly, on hospital campuses.6 The illness leading to hospitalization may be attributable, at least in part, to tobacco use, thereby increasing the patient's receptivity to cessation counseling. Last, medications used in‐hospital to treat nicotine withdrawal symptoms may lead to continued or future use of these medications that, in turn, may ultimately lead to a successful quit attempt.

We report on the outcomes of our hospital's attempt to do this in the context of implementation of a smoke‐free medical campus.7 This study was designed to measure whether an inpatient smoking cessation intervention increases the likelihood of smoking cessation 6 months post‐hospital discharge. Because effectiveness studies are the next step to improving translation of research into health promotion practice,8 we set out to measure what the impact of this intervention would be in routine clinical practice as opposed to a carefully structured efficacy trial.

Methods

Results

From January 1, 2007 to May 30, 2008, 660 inpatients who were current smokers were recruited into the study. Figure 1 summarizes patient flow through the study and explains the final sample size of 607. Exclusions include 52 inpatients from the study who completed the 6‐month interview but who did not return a written HIPAA release. Without a HIPAA release to access the EMR, baseline comparison of the study groups and adjustment for comorbidity could not be completed for these patients. At 6 months posthospital discharge, 53 subjects refused the interview when contacted by telephone.

Figure 1

As might be expected in a quasiexperimental design, the study groups were not equivalent at baseline (Table 1). The intervention and comparison groups differed with regard to age, length of stay (LOS), proportion of acute admissions, and the Elixhauser comorbidity index. These differences suggest that the intervention group was older, had a longer LOS, higher acuity at the time of admission, and more comorbidities. In addition, as a result of the intervention, the intervention group was more likely to receive NRT or bupropion in hospital and a Fax‐to‐Quit referral to the NYS Smokers' Quitline. In the intervention group, most patients received 1 visit from the SCS, only 2% received 2 visits. Family members were included in smoking cessation counseling for 58% of the intervention group.

As shown in Table 2, there was a significant amount of diagnostic heterogeneity in the discharge diagnoses codes of patients included in the study. However, there were significantly more patients in the intervention group with a first discharge diagnosis of cardiovascular disease (25%) compared to the comparison group (12%; P = 0.00).

Readmission outcomes based on EMR and administrative data were available for 607 inpatients with signed HIPAA releases. The readmission rate was higher for the intervention (41%) than the comparison group (20%). Despite a higher readmission rate, the crude mortality within the 6 months posthospital discharge was lower for the intervention group, ie, 0.02 (6/276), than the comparison group, which had a crude mortality of 0.04 (16/384) during this period.

A multivariate survival model, controlling for age, sex, Elixhauser comorbidity index, LOS, and cardiovascular diagnosis, showed a significantly reduced mortality in the intervention group (hazard ratio [HR] = 0.37; P = 0.04). Although cardiac status (P = 0.09) and LOS (P = 0.15) were not significant in this model, they were retained because both of these variables showed significantly higher levels (along with the Elixhauser Index) at baseline in the intervention group, implying that the intervention group was sicker than the comparison group. The Elixhauser comorbidity index (HR = 1.42; P < 0.00) and age (HR = 1.07; P < 0.00) were the only other significant predictors of mortality in this model.

Among those responding to the interview at 6 months post‐hospital discharge (n = 326), there were no significant differences between the study groups with regard to age first started smoking, gender, educational level, employment status, ethnicity, or physical health status (data not shown). Table 3 summarizes the outcomes by the intervention and comparison groups at 6 months post‐hospital discharge. The point prevalence for abstinence was 27% in the intervention group compared to 19% in the comparison group (P = 0.09). Using the intent to treat analysis, the point prevalence for abstinence was 16% in the intervention group compared to 9.8% in the comparison group (P = 0.02). Self‐reported quit status was 63% in the intervention group vs. 48% in the comparison group (P = 0.00). Using the intent to treat analysis, quit status was 44% in the intervention group vs. 30% in the comparison group (P = 0.00). Exclusion of the 52 patients without signed HIPAA releases (Figure 1) did not significantly alter these outcomes.

Patients who received the inpatient smoking cessation counseling were more likely to be called by or use the NYS Smokers' Quitline; however, these differences were not statistically significant. There was no difference between the study groups in awareness of the Quitline but the intervention group was more aware that free NRT was offered by the NYS Quitline. In terms of quit methods used during the 6‐month period (Table 4), NRT or bupropion use was higher in the intervention group. There were no other significant differences between the study groups, except for the use of acupuncture.

Multivariate analysis predicting quit status at 6 months post‐hospital discharge included covariates controlling for age, sex, LOS, study group, and comorbidity. This analysis showed that patients with a cardiovascular discharge diagnosis were more likely to quit than patients who had other discharge diagnoses (odds ratio [OR], 3.02; 95% confidence interval [CI], 1.65.7; P = 0.00). Another statistically significant covariate in this model included sex (men were more likely to quit: OR, 0.61; 95% CI, 0.390.97; P = 0.04). Participating in the inpatient intervention group was marginally significant when controlling for these other variables (OR, 1.54; 95% CI, 0.982.45; P = 0.06). Hospital LOS, age, receipt of NRT in hospital, and the Elixhauser comorbidity index were not predictive of quit status at 6 months.

Discussion

This study demonstrates how effective an inpatient smoking cessation program can be for increasing the success of quitting smoking after hospital discharge. At 6 months posthospital discharge, the intervention group had significantly higher intent to treat outcomes for point prevalence abstinence and quit status as well as lower crude and adjusted mortality than the comparison group.

Although at baseline the intervention group was older, had a longer LOS, more cardiovascular diagnoses, and higher comorbidity index, crude post‐hospital discharge mortality was significantly less in the intervention group (0.02) than in the comparison group (0.04). This finding is more significant in light of the higher comorbidity and acuity of the intervention group at baseline. Our multivariate survival model that controlled for these imbalances at baseline demonstrated that the intervention group had significantly less mortality than the comparison group (HR = 0.37; P = 0.04). Reduction in mortality, as soon as 30 days after inpatient smoking cessation counseling, has been demonstrated postmyocardial infarction.17, 18 Intensive smoking cessation quit services were also linked with lower all cause mortality among cardiovascular disease patients 2 years posthospitalization.19 Our study, despite its relatively small sample size, demonstrates that the intervention retains its impact on mortality in real‐world settings.

Following participation in an inpatient smoking cessation program, self‐reported quit status at 6 months post‐hospital discharge in the intervention group was significantly higher in the intervention group (63%) than the comparison group (48%; P = 0.00). Using the intent to treat method, the differences between the study groups was still significant (44% in the intervention group, 30% in the comparison group; P = 0.00). Given the limitations of self‐report and responder bias, the actual outcomes fall somewhere between these 2 estimates. In an effectiveness study of inpatient smoking cessation involving 6 hospitals in California, self‐reported quit rates of 26% at 6 months were reported; however, different methods were used so the results are not strictly comparable.20

Our multivariate analysis suggests that patients with cardiovascular discharge diagnosis were more likely to quit than patients who had other discharge diagnoses (OR, 3.02; 95% CI, 1.65.7; P = 0.0007). This study extends findings of other studies that show that the success of smoking cessation may vary by diagnosis, particularly for smokers admitted for cardiovascular disease.21, 22 Other studies have shown that smoking cessation rates among patients post‐myocardial infarction were higher in admitting facilities that had hospital‐based smoking cessation programs and for those patients referred to cardiac rehabilitation.23 Thus, the availability of a hospital‐based smoking cessation program may be considered a structural measure of health care quality as suggested by Dawood et al.23

The use of NRT greatly increased in our hospital, coincident with the start of the inpatient cessation program7 and, in this study, NRT use appears to continue after hospital discharge. Some studies show an additive effect of NRT combined with cessation counseling.24, 25 Although a Cochrane review did not find a statistically significant difference, there was a trend toward higher quit rates with the addition of NRT.21, 22

Because hospitalized smokers may be more motivated to stop smoking, the updated 2008 DHHS clinical practice guidelines for Treating Tobacco Use and Dependence now recommend that all inpatients who currently smoke be given medications, advised, counseled, and receive follow‐up after discharge.26 Although our inpatient cessation program was started before these clinical practice guidelines were updated, we have had the opportunity to evaluate the recommended practice of inpatient tobacco cessation counseling. Compared to effects shown in efficacy studies, clinical interventions often lose effect size in daily practice and real‐world settings.27, 28 It is reassuring that, in this effectiveness study, the impact of this intervention is still demonstrable.

Provision of inpatient smoking cessation has been shown to be an effective smoking cessation intervention if combined with outpatient follow‐up.29 Reviews by Rigotti et al.21, 22 recommend that inpatient high‐intensity behavioral interventions should be followed by at least 1 month of supportive contact after discharge to promote smoking cessation among hospitalized patients. In our study, specific cessation‐related outpatient follow‐up was not provided by our program. Although letters were sent to primary care providers describing the cessation service provided during the inpatient stay, our study could not ascertain what specific cessation service was offered by either primary‐care or specialty‐care providers during posthospitalization follow‐up visits. An efficient alternative to outpatient visits may be follow‐up delivered via a quitline. Follow‐up in our study included referrals to the NYS Smokers' Quitline; however, only about 10% of inpatient reported using this service. While feasible, the effectiveness of quitline follow‐up is as yet unknown.30

Conclusions

This quasiexperimental effectiveness study showed that inpatient smoking cessation intervention improved smoking cessation outcomes, use of NRT, and was associated with a decreased mortality 6 months post‐hospital discharge. The effectiveness of this inpatient intervention is maintained in real world settings but may be improved with posthospital discharge follow‐up.

In 1992, the Joint Commission on Accreditation of Healthcare Organizations (Joint Commission) introduced standards to make hospital buildings smoke‐free, resulting in the nation's first industry‐wide ban on smoking in the workplace. This hospital smoking ban has led to increased smoking cessation among employees.1 Since 2003, core measures from the Joint Commission and quality indicators from the Centers for Medicare and Medicaid Services have included inpatient smoking cessation counseling for acute myocardial infarction, pneumonia, and heart failure, as national guidelines strongly recommend smoking cessation counseling for patients with these diseases who smoke.25

The Department of Health and Human Services (DHHS) 2008 update on Clinical Practice Guidelines for Treating Tobacco Use and Dependence6 recommends that clinicians use hospitalization as an opportunity to promote smoking cessation and to prescribe medications to alleviate withdrawal symptoms. Hospitalization is an opportune time for smoking cessation because patients are restricted to a smoke‐free environment in the hospital and, increasingly, on hospital campuses.6 The illness leading to hospitalization may be attributable, at least in part, to tobacco use, thereby increasing the patient's receptivity to cessation counseling. Last, medications used in‐hospital to treat nicotine withdrawal symptoms may lead to continued or future use of these medications that, in turn, may ultimately lead to a successful quit attempt.

We report on the outcomes of our hospital's attempt to do this in the context of implementation of a smoke‐free medical campus.7 This study was designed to measure whether an inpatient smoking cessation intervention increases the likelihood of smoking cessation 6 months post‐hospital discharge. Because effectiveness studies are the next step to improving translation of research into health promotion practice,8 we set out to measure what the impact of this intervention would be in routine clinical practice as opposed to a carefully structured efficacy trial.

Methods

Results

From January 1, 2007 to May 30, 2008, 660 inpatients who were current smokers were recruited into the study. Figure 1 summarizes patient flow through the study and explains the final sample size of 607. Exclusions include 52 inpatients from the study who completed the 6‐month interview but who did not return a written HIPAA release. Without a HIPAA release to access the EMR, baseline comparison of the study groups and adjustment for comorbidity could not be completed for these patients. At 6 months posthospital discharge, 53 subjects refused the interview when contacted by telephone.

Figure 1

As might be expected in a quasiexperimental design, the study groups were not equivalent at baseline (Table 1). The intervention and comparison groups differed with regard to age, length of stay (LOS), proportion of acute admissions, and the Elixhauser comorbidity index. These differences suggest that the intervention group was older, had a longer LOS, higher acuity at the time of admission, and more comorbidities. In addition, as a result of the intervention, the intervention group was more likely to receive NRT or bupropion in hospital and a Fax‐to‐Quit referral to the NYS Smokers' Quitline. In the intervention group, most patients received 1 visit from the SCS, only 2% received 2 visits. Family members were included in smoking cessation counseling for 58% of the intervention group.

As shown in Table 2, there was a significant amount of diagnostic heterogeneity in the discharge diagnoses codes of patients included in the study. However, there were significantly more patients in the intervention group with a first discharge diagnosis of cardiovascular disease (25%) compared to the comparison group (12%; P = 0.00).

Readmission outcomes based on EMR and administrative data were available for 607 inpatients with signed HIPAA releases. The readmission rate was higher for the intervention (41%) than the comparison group (20%). Despite a higher readmission rate, the crude mortality within the 6 months posthospital discharge was lower for the intervention group, ie, 0.02 (6/276), than the comparison group, which had a crude mortality of 0.04 (16/384) during this period.

A multivariate survival model, controlling for age, sex, Elixhauser comorbidity index, LOS, and cardiovascular diagnosis, showed a significantly reduced mortality in the intervention group (hazard ratio [HR] = 0.37; P = 0.04). Although cardiac status (P = 0.09) and LOS (P = 0.15) were not significant in this model, they were retained because both of these variables showed significantly higher levels (along with the Elixhauser Index) at baseline in the intervention group, implying that the intervention group was sicker than the comparison group. The Elixhauser comorbidity index (HR = 1.42; P < 0.00) and age (HR = 1.07; P < 0.00) were the only other significant predictors of mortality in this model.

Among those responding to the interview at 6 months post‐hospital discharge (n = 326), there were no significant differences between the study groups with regard to age first started smoking, gender, educational level, employment status, ethnicity, or physical health status (data not shown). Table 3 summarizes the outcomes by the intervention and comparison groups at 6 months post‐hospital discharge. The point prevalence for abstinence was 27% in the intervention group compared to 19% in the comparison group (P = 0.09). Using the intent to treat analysis, the point prevalence for abstinence was 16% in the intervention group compared to 9.8% in the comparison group (P = 0.02). Self‐reported quit status was 63% in the intervention group vs. 48% in the comparison group (P = 0.00). Using the intent to treat analysis, quit status was 44% in the intervention group vs. 30% in the comparison group (P = 0.00). Exclusion of the 52 patients without signed HIPAA releases (Figure 1) did not significantly alter these outcomes.

Patients who received the inpatient smoking cessation counseling were more likely to be called by or use the NYS Smokers' Quitline; however, these differences were not statistically significant. There was no difference between the study groups in awareness of the Quitline but the intervention group was more aware that free NRT was offered by the NYS Quitline. In terms of quit methods used during the 6‐month period (Table 4), NRT or bupropion use was higher in the intervention group. There were no other significant differences between the study groups, except for the use of acupuncture.

Multivariate analysis predicting quit status at 6 months post‐hospital discharge included covariates controlling for age, sex, LOS, study group, and comorbidity. This analysis showed that patients with a cardiovascular discharge diagnosis were more likely to quit than patients who had other discharge diagnoses (odds ratio [OR], 3.02; 95% confidence interval [CI], 1.65.7; P = 0.00). Another statistically significant covariate in this model included sex (men were more likely to quit: OR, 0.61; 95% CI, 0.390.97; P = 0.04). Participating in the inpatient intervention group was marginally significant when controlling for these other variables (OR, 1.54; 95% CI, 0.982.45; P = 0.06). Hospital LOS, age, receipt of NRT in hospital, and the Elixhauser comorbidity index were not predictive of quit status at 6 months.

Discussion

This study demonstrates how effective an inpatient smoking cessation program can be for increasing the success of quitting smoking after hospital discharge. At 6 months posthospital discharge, the intervention group had significantly higher intent to treat outcomes for point prevalence abstinence and quit status as well as lower crude and adjusted mortality than the comparison group.

Although at baseline the intervention group was older, had a longer LOS, more cardiovascular diagnoses, and higher comorbidity index, crude post‐hospital discharge mortality was significantly less in the intervention group (0.02) than in the comparison group (0.04). This finding is more significant in light of the higher comorbidity and acuity of the intervention group at baseline. Our multivariate survival model that controlled for these imbalances at baseline demonstrated that the intervention group had significantly less mortality than the comparison group (HR = 0.37; P = 0.04). Reduction in mortality, as soon as 30 days after inpatient smoking cessation counseling, has been demonstrated postmyocardial infarction.17, 18 Intensive smoking cessation quit services were also linked with lower all cause mortality among cardiovascular disease patients 2 years posthospitalization.19 Our study, despite its relatively small sample size, demonstrates that the intervention retains its impact on mortality in real‐world settings.

Following participation in an inpatient smoking cessation program, self‐reported quit status at 6 months post‐hospital discharge in the intervention group was significantly higher in the intervention group (63%) than the comparison group (48%; P = 0.00). Using the intent to treat method, the differences between the study groups was still significant (44% in the intervention group, 30% in the comparison group; P = 0.00). Given the limitations of self‐report and responder bias, the actual outcomes fall somewhere between these 2 estimates. In an effectiveness study of inpatient smoking cessation involving 6 hospitals in California, self‐reported quit rates of 26% at 6 months were reported; however, different methods were used so the results are not strictly comparable.20

Our multivariate analysis suggests that patients with cardiovascular discharge diagnosis were more likely to quit than patients who had other discharge diagnoses (OR, 3.02; 95% CI, 1.65.7; P = 0.0007). This study extends findings of other studies that show that the success of smoking cessation may vary by diagnosis, particularly for smokers admitted for cardiovascular disease.21, 22 Other studies have shown that smoking cessation rates among patients post‐myocardial infarction were higher in admitting facilities that had hospital‐based smoking cessation programs and for those patients referred to cardiac rehabilitation.23 Thus, the availability of a hospital‐based smoking cessation program may be considered a structural measure of health care quality as suggested by Dawood et al.23

The use of NRT greatly increased in our hospital, coincident with the start of the inpatient cessation program7 and, in this study, NRT use appears to continue after hospital discharge. Some studies show an additive effect of NRT combined with cessation counseling.24, 25 Although a Cochrane review did not find a statistically significant difference, there was a trend toward higher quit rates with the addition of NRT.21, 22

Because hospitalized smokers may be more motivated to stop smoking, the updated 2008 DHHS clinical practice guidelines for Treating Tobacco Use and Dependence now recommend that all inpatients who currently smoke be given medications, advised, counseled, and receive follow‐up after discharge.26 Although our inpatient cessation program was started before these clinical practice guidelines were updated, we have had the opportunity to evaluate the recommended practice of inpatient tobacco cessation counseling. Compared to effects shown in efficacy studies, clinical interventions often lose effect size in daily practice and real‐world settings.27, 28 It is reassuring that, in this effectiveness study, the impact of this intervention is still demonstrable.

Provision of inpatient smoking cessation has been shown to be an effective smoking cessation intervention if combined with outpatient follow‐up.29 Reviews by Rigotti et al.21, 22 recommend that inpatient high‐intensity behavioral interventions should be followed by at least 1 month of supportive contact after discharge to promote smoking cessation among hospitalized patients. In our study, specific cessation‐related outpatient follow‐up was not provided by our program. Although letters were sent to primary care providers describing the cessation service provided during the inpatient stay, our study could not ascertain what specific cessation service was offered by either primary‐care or specialty‐care providers during posthospitalization follow‐up visits. An efficient alternative to outpatient visits may be follow‐up delivered via a quitline. Follow‐up in our study included referrals to the NYS Smokers' Quitline; however, only about 10% of inpatient reported using this service. While feasible, the effectiveness of quitline follow‐up is as yet unknown.30

Conclusions

This quasiexperimental effectiveness study showed that inpatient smoking cessation intervention improved smoking cessation outcomes, use of NRT, and was associated with a decreased mortality 6 months post‐hospital discharge. The effectiveness of this inpatient intervention is maintained in real world settings but may be improved with posthospital discharge follow‐up.

In 1992, the Joint Commission on Accreditation of Healthcare Organizations (Joint Commission) introduced standards to make hospital buildings smoke‐free, resulting in the nation's first industry‐wide ban on smoking in the workplace. This hospital smoking ban has led to increased smoking cessation among employees.1 Since 2003, core measures from the Joint Commission and quality indicators from the Centers for Medicare and Medicaid Services have included inpatient smoking cessation counseling for acute myocardial infarction, pneumonia, and heart failure, as national guidelines strongly recommend smoking cessation counseling for patients with these diseases who smoke.25

The Department of Health and Human Services (DHHS) 2008 update on Clinical Practice Guidelines for Treating Tobacco Use and Dependence6 recommends that clinicians use hospitalization as an opportunity to promote smoking cessation and to prescribe medications to alleviate withdrawal symptoms. Hospitalization is an opportune time for smoking cessation because patients are restricted to a smoke‐free environment in the hospital and, increasingly, on hospital campuses.6 The illness leading to hospitalization may be attributable, at least in part, to tobacco use, thereby increasing the patient's receptivity to cessation counseling. Last, medications used in‐hospital to treat nicotine withdrawal symptoms may lead to continued or future use of these medications that, in turn, may ultimately lead to a successful quit attempt.

We report on the outcomes of our hospital's attempt to do this in the context of implementation of a smoke‐free medical campus.7 This study was designed to measure whether an inpatient smoking cessation intervention increases the likelihood of smoking cessation 6 months post‐hospital discharge. Because effectiveness studies are the next step to improving translation of research into health promotion practice,8 we set out to measure what the impact of this intervention would be in routine clinical practice as opposed to a carefully structured efficacy trial.

Methods

Results

From January 1, 2007 to May 30, 2008, 660 inpatients who were current smokers were recruited into the study. Figure 1 summarizes patient flow through the study and explains the final sample size of 607. Exclusions include 52 inpatients from the study who completed the 6‐month interview but who did not return a written HIPAA release. Without a HIPAA release to access the EMR, baseline comparison of the study groups and adjustment for comorbidity could not be completed for these patients. At 6 months posthospital discharge, 53 subjects refused the interview when contacted by telephone.

Figure 1

As might be expected in a quasiexperimental design, the study groups were not equivalent at baseline (Table 1). The intervention and comparison groups differed with regard to age, length of stay (LOS), proportion of acute admissions, and the Elixhauser comorbidity index. These differences suggest that the intervention group was older, had a longer LOS, higher acuity at the time of admission, and more comorbidities. In addition, as a result of the intervention, the intervention group was more likely to receive NRT or bupropion in hospital and a Fax‐to‐Quit referral to the NYS Smokers' Quitline. In the intervention group, most patients received 1 visit from the SCS, only 2% received 2 visits. Family members were included in smoking cessation counseling for 58% of the intervention group.

As shown in Table 2, there was a significant amount of diagnostic heterogeneity in the discharge diagnoses codes of patients included in the study. However, there were significantly more patients in the intervention group with a first discharge diagnosis of cardiovascular disease (25%) compared to the comparison group (12%; P = 0.00).

Readmission outcomes based on EMR and administrative data were available for 607 inpatients with signed HIPAA releases. The readmission rate was higher for the intervention (41%) than the comparison group (20%). Despite a higher readmission rate, the crude mortality within the 6 months posthospital discharge was lower for the intervention group, ie, 0.02 (6/276), than the comparison group, which had a crude mortality of 0.04 (16/384) during this period.

A multivariate survival model, controlling for age, sex, Elixhauser comorbidity index, LOS, and cardiovascular diagnosis, showed a significantly reduced mortality in the intervention group (hazard ratio [HR] = 0.37; P = 0.04). Although cardiac status (P = 0.09) and LOS (P = 0.15) were not significant in this model, they were retained because both of these variables showed significantly higher levels (along with the Elixhauser Index) at baseline in the intervention group, implying that the intervention group was sicker than the comparison group. The Elixhauser comorbidity index (HR = 1.42; P < 0.00) and age (HR = 1.07; P < 0.00) were the only other significant predictors of mortality in this model.

Among those responding to the interview at 6 months post‐hospital discharge (n = 326), there were no significant differences between the study groups with regard to age first started smoking, gender, educational level, employment status, ethnicity, or physical health status (data not shown). Table 3 summarizes the outcomes by the intervention and comparison groups at 6 months post‐hospital discharge. The point prevalence for abstinence was 27% in the intervention group compared to 19% in the comparison group (P = 0.09). Using the intent to treat analysis, the point prevalence for abstinence was 16% in the intervention group compared to 9.8% in the comparison group (P = 0.02). Self‐reported quit status was 63% in the intervention group vs. 48% in the comparison group (P = 0.00). Using the intent to treat analysis, quit status was 44% in the intervention group vs. 30% in the comparison group (P = 0.00). Exclusion of the 52 patients without signed HIPAA releases (Figure 1) did not significantly alter these outcomes.

Patients who received the inpatient smoking cessation counseling were more likely to be called by or use the NYS Smokers' Quitline; however, these differences were not statistically significant. There was no difference between the study groups in awareness of the Quitline but the intervention group was more aware that free NRT was offered by the NYS Quitline. In terms of quit methods used during the 6‐month period (Table 4), NRT or bupropion use was higher in the intervention group. There were no other significant differences between the study groups, except for the use of acupuncture.

Multivariate analysis predicting quit status at 6 months post‐hospital discharge included covariates controlling for age, sex, LOS, study group, and comorbidity. This analysis showed that patients with a cardiovascular discharge diagnosis were more likely to quit than patients who had other discharge diagnoses (odds ratio [OR], 3.02; 95% confidence interval [CI], 1.65.7; P = 0.00). Another statistically significant covariate in this model included sex (men were more likely to quit: OR, 0.61; 95% CI, 0.390.97; P = 0.04). Participating in the inpatient intervention group was marginally significant when controlling for these other variables (OR, 1.54; 95% CI, 0.982.45; P = 0.06). Hospital LOS, age, receipt of NRT in hospital, and the Elixhauser comorbidity index were not predictive of quit status at 6 months.

Discussion

This study demonstrates how effective an inpatient smoking cessation program can be for increasing the success of quitting smoking after hospital discharge. At 6 months posthospital discharge, the intervention group had significantly higher intent to treat outcomes for point prevalence abstinence and quit status as well as lower crude and adjusted mortality than the comparison group.

Although at baseline the intervention group was older, had a longer LOS, more cardiovascular diagnoses, and higher comorbidity index, crude post‐hospital discharge mortality was significantly less in the intervention group (0.02) than in the comparison group (0.04). This finding is more significant in light of the higher comorbidity and acuity of the intervention group at baseline. Our multivariate survival model that controlled for these imbalances at baseline demonstrated that the intervention group had significantly less mortality than the comparison group (HR = 0.37; P = 0.04). Reduction in mortality, as soon as 30 days after inpatient smoking cessation counseling, has been demonstrated postmyocardial infarction.17, 18 Intensive smoking cessation quit services were also linked with lower all cause mortality among cardiovascular disease patients 2 years posthospitalization.19 Our study, despite its relatively small sample size, demonstrates that the intervention retains its impact on mortality in real‐world settings.

Following participation in an inpatient smoking cessation program, self‐reported quit status at 6 months post‐hospital discharge in the intervention group was significantly higher in the intervention group (63%) than the comparison group (48%; P = 0.00). Using the intent to treat method, the differences between the study groups was still significant (44% in the intervention group, 30% in the comparison group; P = 0.00). Given the limitations of self‐report and responder bias, the actual outcomes fall somewhere between these 2 estimates. In an effectiveness study of inpatient smoking cessation involving 6 hospitals in California, self‐reported quit rates of 26% at 6 months were reported; however, different methods were used so the results are not strictly comparable.20

Our multivariate analysis suggests that patients with cardiovascular discharge diagnosis were more likely to quit than patients who had other discharge diagnoses (OR, 3.02; 95% CI, 1.65.7; P = 0.0007). This study extends findings of other studies that show that the success of smoking cessation may vary by diagnosis, particularly for smokers admitted for cardiovascular disease.21, 22 Other studies have shown that smoking cessation rates among patients post‐myocardial infarction were higher in admitting facilities that had hospital‐based smoking cessation programs and for those patients referred to cardiac rehabilitation.23 Thus, the availability of a hospital‐based smoking cessation program may be considered a structural measure of health care quality as suggested by Dawood et al.23

The use of NRT greatly increased in our hospital, coincident with the start of the inpatient cessation program7 and, in this study, NRT use appears to continue after hospital discharge. Some studies show an additive effect of NRT combined with cessation counseling.24, 25 Although a Cochrane review did not find a statistically significant difference, there was a trend toward higher quit rates with the addition of NRT.21, 22

Because hospitalized smokers may be more motivated to stop smoking, the updated 2008 DHHS clinical practice guidelines for Treating Tobacco Use and Dependence now recommend that all inpatients who currently smoke be given medications, advised, counseled, and receive follow‐up after discharge.26 Although our inpatient cessation program was started before these clinical practice guidelines were updated, we have had the opportunity to evaluate the recommended practice of inpatient tobacco cessation counseling. Compared to effects shown in efficacy studies, clinical interventions often lose effect size in daily practice and real‐world settings.27, 28 It is reassuring that, in this effectiveness study, the impact of this intervention is still demonstrable.

Provision of inpatient smoking cessation has been shown to be an effective smoking cessation intervention if combined with outpatient follow‐up.29 Reviews by Rigotti et al.21, 22 recommend that inpatient high‐intensity behavioral interventions should be followed by at least 1 month of supportive contact after discharge to promote smoking cessation among hospitalized patients. In our study, specific cessation‐related outpatient follow‐up was not provided by our program. Although letters were sent to primary care providers describing the cessation service provided during the inpatient stay, our study could not ascertain what specific cessation service was offered by either primary‐care or specialty‐care providers during posthospitalization follow‐up visits. An efficient alternative to outpatient visits may be follow‐up delivered via a quitline. Follow‐up in our study included referrals to the NYS Smokers' Quitline; however, only about 10% of inpatient reported using this service. While feasible, the effectiveness of quitline follow‐up is as yet unknown.30

Conclusions

This quasiexperimental effectiveness study showed that inpatient smoking cessation intervention improved smoking cessation outcomes, use of NRT, and was associated with a decreased mortality 6 months post‐hospital discharge. The effectiveness of this inpatient intervention is maintained in real world settings but may be improved with posthospital discharge follow‐up.

Hospitals are challenged to improve quality while reducing costs, yet traditional methods of cost containment have had limited success in aligning the goals of hospitals and physicians. Physicians directly control more than 80% of total medical costs.1 The current fee‐for‐service system encourages procedures and the use of hospital resources. Without the proper incentives to gain active participation and collaboration of the medical staff in improving the efficiency of care, the ability to manage medical costs and improve hospital operational and financial performance is hampered. A further challenge is to encourage physicians to improve the quality of care and maintain safe medical practice. While several examples of pay‐for‐performance (P4P) have previously been attempted to increase efficiency, gainsharing offers real opportunities to achieve these outcomes.

Previous reports regarding the results of gainsharing programs describe its use in outpatient settings and its limited ability to reduce costs for inpatient care for surgical implants such as coronary stents2 or orthopedic prostheses.3 The present study represents the largest series to date using a gainsharing model in a comprehensive program of inpatient care at a tertiary care medical center.

Patients and Methods

Beth Israel Medical Center is a 1000‐bed tertiary care university‐affiliated teaching hospital, located in New York City. The hospital serves a large and ethnically diverse community predominantly located in the lower east side of Manhattan and discharged about 50,000 patients per year during the study period of July 2006 through June 2009.

Applied Medical Software, Inc. (AMS, Collingswood, NJ) analyzed hospital data for case mix and severity. To establish best practice norms (BPNs), AMS used inpatient discharge data (UB‐92) to determine costs by APR‐DRG's4 during calendar year 2005, prior to the inception of the program to establish BPNs. Costs were allocated into specific areas listed in Table 1. A minimum of 10 cases was necessary in each DRG. Cost outliers (as defined by the mean cost of the APR DRG plus 3 standard deviations) were excluded. These data were used to establish a baseline for each physician and a BPN, which was set at the top 25th percentile for each specific APR DRG. BPNs were determined after exclusions using the following criteria:

• 1

  Each eligible physician had to have at least 10 admissions within their specialty;

• 2

  Each eligible DRG had to have at least 5 qualifying physicians within a medical specialty;

• 3

  Each eligible APR DRG had to have at least 3 qualifying admissions;

• 4

  If the above criteria are met, the BPN was set at the mean of the top 25th percentile of physicians (25% of the physicians with the lowest costs).

Once BPNs were determined, patients were grouped by physician and compared to the BPN for a particular APR DRG. All patients of participating physicians with qualifying APR DRGs were included in the analysis reports summarizing these results, computed quarterly and distributed to each physician. Obstetrical and psychiatric admissions were excluded in the program. APR DRG data for each physician was compared from year to year to determine whether an individual physician demonstrated measurable improvement in performance.

The gainsharing program was implemented in 2006. Physician participation was voluntary. Payments were made to physicians without any risk or penalties from participation. Incentives were based on individual performance. Incentives for nonsurgical admissions were intended to offset the loss of physician income related to more efficient medical management and a reduced hospital length of stay (LOS). Income for surgical admissions was intended to reward physicians for efficient preoperative and postoperative care.

The methodology provides financial incentives for physicians for each hospital discharge in 2 ways:

• 1

  Improvement in costs per case against their own historical performance;

• 2

  Cost per case performance compared to BPN.

In the first year of the gainsharing program, two thirds of the total allowable incentive payments were allocated to physicians' improvement, with one third based on a performance metric. Payments for improvement were phased out over the first 3 years of the gainsharing program, with payments focused fully on performance in Year 3. Cases were adjusted for case‐mix and severity of illness (four levels of APR DRG). Physicians were not penalized for any cases in which costs greatly exceeded BPN. A floor was placed at the BPN and no additional financial incentives were paid for surpassing it. Baselines and BPNs were recalculated yearly.

A key aspect of the gainsharing program was the establishment of specific quality parameters (Table 2) that need to be met before any incentive payments were made. A committee regularly reviewed the quality performance data of each physician to determine eligibility for payments. Physicians were considered to be ineligible for incentive compensation until the next measurement period if there was evidence of failure to adequately meet these measures. At least 80% compliance with core measures (minimum 5 discharges in each domain) was expected. Infectious complication rates were to remain not more than 1 standard deviation above National Healthcare Safety Network rates during the same time period. In addition, payments were withheld from physicians if it was found that the standard of care was not met for any morbidity or mortality that was peer reviewed or if there were any significant patient complaints. Readmission rates were expected to remain at or below the baseline established during the previous 12 months by DRG.

Employed and private practice community physicians were both eligible for the gainsharing program. Physician participation in the program was voluntary. All patients admitted to the Medical Center received notification on admission about the program. The aggregate costs by DRG were calculated quarterly. Savings over the previous yearif anywere calculated. A total of 20% of the savings was used to administer the program and for incentive payments to physicians.

From July 1, 2006 through September 2008, only commercial managed care cases were eligible for this program. As a result of the approval of the gainsharing program as a demonstration project by the Centers for Medicare and Medicaid Services (CMS), Medicare cases were added to the program starting October 1, 2008.

Physician Payment Calculation Methodology

Results

Over the 3‐year period, 184 physicians enrolled, representing 54% of those eligible. The remainder of physicians either decided not to enroll or were not eligible due to inadequate number of index DRG cases or excluded diagnoses. Payer mix was 27% Medicare and 48% of the discharges were commercial and managed care. The remaining cases were a combination of Medicaid and self‐pay. A total of 29,535 commercial and managed care discharges were evaluated from participating physicians (58%) and 20,360 similar discharges from non‐participating physicians. This number of admissions accounted for 29% of all hospital discharges during this time period. Surgical admissions accounted for 43% and nonsurgical admissions for 57%. The distribution of patients by service is shown in Table 3. Pulmonary and cardiology diagnoses were the most frequent reasons for medical admissions. General and head and neck surgery were the most frequent surgical admissions. During the time period of the gainsharing program, the medical center saved $25.1 million for costs attributed to these cases. Participating physicians saved $6.9 million more than non‐participating physicians (P = 0.02, Figure 1), but all discharges demonstrated cost savings during the study period. Cost savings (Figure 2) resulted from savings in medical/surgical supplies and implants (35%), daily hospital costs, (28%), intensive care unit costs (16%) and coronary care unit costs (15%), and operating room costs (8%). Reduction in cost from reduced magnetic resonance imaging (MRI) use was not statistically significant. There were minimal increases in costs due to computed tomography (CT) scan use, cardiopulmonary care, laboratory use, pharmacy and blood bank, but none of these reached statistical significance.

Figure 1

Figure 2

Hospital LOS decreased 9.8% from baseline among participating doctors, while LOS decreased 9.0% among non‐participating physicians; this difference was not statistically significant (P = 0.6). Participating physicians reduced costs by an average of $7,871 per quarter, compared to a reduction in costs by $3,018 for admissions by non‐participating physicians (P < 0.0001). The average savings per admission for the participating physicians were $1,835, and for non‐participating physicians were $1,107, a difference of $728 per admission. Overall, cost savings during the three year period averaged $105,000 per physician who participated in the program and $67,000 per physician who did not (P < 0.05). There was not a statistical difference in savings between medical and surgical admissions (P = 0.24).

Deviations from quality thresholds were identified during this time period. Some or all of the gainsharing income was withheld from 8% of participating physicians due to quality issues, incomplete medical records, or administrative reasons. Payouts to participating physicians averaged $1,866 quarterly (range $0‐$27,631). Overall, 9.4% of the hospital savings was directly paid to the participating physicians. Compliance with core measures improved in the following domains from year 2006 to 2009; acute myocardial infarction 94% to 98%, congestive heart failure 76% to 93%, pneumonia 88% to 97%, and surgical care improvement project 90% to 97%, (P = 0.17). There was no measurable increase in 30‐day mortality or readmission by APR‐DRG. The number of incomplete medical records decreased from an average of 43% of the total number of records in the second quarter of 2006 to 30% in the second quarter of 2009 (P < 0.0001). Other quality indicators remained statistically unchanged.

Discussion

The promise of gainsharing may motivate physicians to decrease hospital costs while maintaining quality medical care, since it aligns physician and hospital incentives. Providing a reward to physicians creates positive reinforcement, which is more effective than warnings against poor performing physicians (carrot vs. stick).5, 6 This study is the first and largest of its kind to show the results of a gainsharing program for inpatient medical and surgical admissions and demonstrates that significant cost savings may be achieved. This is similar to previous studies that have shown positive outcomes for pay‐for‐performance programs.7

Participating physicians in the present study accumulated almost $7 million more in savings than non‐participating physicians. Over time this difference has increased, possibly due to a learning curve in educating participating physicians and the way in which information about their performance is given back to them. A significant portion of the hospital's cost savings was through improvements in documentation and completion of medical records. While there was an actual reduction in average length of stay (ALOS), better documentation may also have contributed to adjusting the severity level within each DRG.

Using financial incentives to positively impact on physician behavior is not new. One program in a community‐based hospitalist group reported similar improvements in medical record documentation, as well as improvements in physician meeting attendance and quality goals.8 Another study found that such hospital programs noted improved physician engagement and commitment to best practices and to improving the quality of care.9

There is significant experience in the outpatient setting using pay‐for‐performance programs to enhance quality. Millett et al.10 demonstrated a reduction in smoking among patients with diabetes in a program in the United Kingdom. Another study in Rochester, New York that used pay‐for‐performance incentives demonstrated better diabetes management.11 Mandel and Kotagal12 demonstrated improved asthma care utilizing a quality incentive program.

The use of financial motivation for physicians, as part of a hospital pay‐for‐performance program, has been shown to lead to improvements in quality performance scores when compared to non pay‐for‐performance hospitals.13 Berthiaume demonstrated decreased costs and improvements in risk‐adjusted complications and risk‐adjusted LOS in patents admitted for acute coronary intervention in a pay‐for‐performance program.14 Quality initiatives were integral for the gainsharing program, since measures such as surgical site infections may increase LOS and hospital costs. Core measures related to the care of patients with acute myocardial infarction, heart failure, pneumonia, and surgical prophylaxis steadily improved since the initiation of the gainsharing program. Gainsharing programs also enhance physician compliance with administrative responsibilities such as the completion of medical records.

One unexpected finding of our study was that there was a cost savings per admission even in the patients of physicians who did not participate in the gainsharing program. While the participating physicians showed statistically significant improvements in cost savings, savings were found in both groups. This raises the question as to whether these cost reductions could have been impacted by other factors such as new labor or vendor contracts, better documentation, improved operating room utilization and improved and timely documentation in the medical record. Another possibility is the Hawthorne effect on physicians, who altered their behavior with knowledge that process and outcome measurement were being measured. Physicians who voluntarily sign up for a gainsharing program would be expected to be more committed to the success of this program than physicians who decide to opt out. While this might appear to be a selection bias it does illustrate the point that motivated physicians are more likely to positively change their practice behaviors. However, one might suggest that financial savings directly attributed to the gainsharing program was not the $25.1 million saved during the 3 years overall, but the difference between participating and non‐participating physicians, or $6.9 million.

While the motivation to complete medical records was significant (gainsharing dollars were withheld from doctors with more than 5 incomplete charts for more than 30 days) it was not the only reason why the number of delinquent chart percentage decreased during the study period. While the improvement was significant, there are still more opportunities to reduce the number of incomplete charts. Hospital regulatory inspections and periodic physician education were also likely to have reduced the number of incomplete inpatient charts during this time period and may do so in the future.15

The program focused on the physician activities that have the greatest impact on hospital costs. While optimizing laboratory, blood bank, and pharmacy management decreased hospital costs; we found that improvements in patient LOS, days in an intensive care unit, and management of surgical implants had the greatest impact on costs. Orthopedic surgeons began to use different implants, and all surgeons refrained from opening disposable surgical supplies until needed. Patients in intensive care unit beds stable for transfer were moved to regular medical/surgical rooms earlier. Since the program helped physicians understand the importance of LOS, many physicians increased their rounding on weekends and considered LOS implications before ordering diagnostic procedures that could be performed as an outpatient. Nurses, physician extenders such as physician assistants, and social workers have played an important role in streamlining patient care and hospital discharge; however, they were not directly rewarded under this program.

There are challenges to aligning the incentives of internists compared to procedure‐based specialists. This may be that the result of surgeons receiving payment for bundled care and thus the incentives are already aligned. The incentive of the program for internists, who get paid for each per daily visit, was intended to overcome the lost income resulting from an earlier discharge. Moreover, in the present study, only the discharging physician received incentive payments for each case. Patient care is undoubtedly a team effort and many physicians (radiologists, anesthesiologists, pathologists, emergency medicine physicians, consultant specialty physicians, etc.) are clearly left out in the present gainsharing program. Aligning the incentives of these physicians might be necessary. Furthermore, the actions of other members of the medical team and consultants, by their behaviors, could limit the incentive payments for the discharging physician. The discharging physician is often unable to control the transfer of a patient from a high‐cost or severity unit, or improve the timeliness of consulting physicians. Previous authors have raised the issue as to whether a physician should be prevented from payment because of the actions of another member of the medical team.16

Ensuring a fair and transparent system is important in any pay‐for‐performance program. The present gainsharing program required sophisticated data analysis, which added to the costs of the program. To implement such a program, data must be clear and understandable, segregated by DRG and severity adjusted. But should the highest reward payments go to those who perform the best or improve the most? In the present study, some physicians were consistently unable to meet quality benchmarks. This may be related to several factors, 1 of which might be a particular physician's case mix. Some authors have raised concerns that pay‐for‐performance programs may unfairly impact physicians who care for more challenging or patients from disadvantaged socioeconomic circumstances.17 Other authors have questioned whether widespread implementation of such a program could potentially increase healthcare disparities in the community.18 It has been suggested by Greene and Nash that for a program to be successful, physicians who feel they provide good care yet but are not rewarded should be given an independent review.16 Such a process is important to prevent resentment among physicians who are unable to meet benchmarks for payment, despite hard work.19 Conversely, other studies have found that many physicians who receive payments in a pay‐for‐performance system do not necessarily consciously make improvement to enhance financial performance.20 Only 54% of eligible physicians participated in the present gainsharing program. This is likely due to lack of understanding about the program, misperceptions about the ethics of such programs, perceived possible negative patient outcome, conflict of interest and mistrust.21, 22 This underscores the importance of providing understandable performance results, education, and a physician champion to help facilitate communication and enhanced outcomes. What is clear is that the perception by participating physicians is that this program is worthwhile as the number of participating physicians has steadily increased and it has become an incentive for new providers to choose this medical center over others.

In conclusion, the results of the present study show that physicians can help hospitals reduce inpatients costs while maintaining or improving hospital quality. Improvements in patient LOS, implant costs, overall costs per admission, and medical record completion were noted. Further work is needed to improve physician education and better understand the impact of uneven physician case mix. Further efforts are necessary to allow other members of the health care team to participate and benefit from gainsharing.

Hospitals are challenged to improve quality while reducing costs, yet traditional methods of cost containment have had limited success in aligning the goals of hospitals and physicians. Physicians directly control more than 80% of total medical costs.1 The current fee‐for‐service system encourages procedures and the use of hospital resources. Without the proper incentives to gain active participation and collaboration of the medical staff in improving the efficiency of care, the ability to manage medical costs and improve hospital operational and financial performance is hampered. A further challenge is to encourage physicians to improve the quality of care and maintain safe medical practice. While several examples of pay‐for‐performance (P4P) have previously been attempted to increase efficiency, gainsharing offers real opportunities to achieve these outcomes.

Previous reports regarding the results of gainsharing programs describe its use in outpatient settings and its limited ability to reduce costs for inpatient care for surgical implants such as coronary stents2 or orthopedic prostheses.3 The present study represents the largest series to date using a gainsharing model in a comprehensive program of inpatient care at a tertiary care medical center.

Patients and Methods

Beth Israel Medical Center is a 1000‐bed tertiary care university‐affiliated teaching hospital, located in New York City. The hospital serves a large and ethnically diverse community predominantly located in the lower east side of Manhattan and discharged about 50,000 patients per year during the study period of July 2006 through June 2009.

Applied Medical Software, Inc. (AMS, Collingswood, NJ) analyzed hospital data for case mix and severity. To establish best practice norms (BPNs), AMS used inpatient discharge data (UB‐92) to determine costs by APR‐DRG's4 during calendar year 2005, prior to the inception of the program to establish BPNs. Costs were allocated into specific areas listed in Table 1. A minimum of 10 cases was necessary in each DRG. Cost outliers (as defined by the mean cost of the APR DRG plus 3 standard deviations) were excluded. These data were used to establish a baseline for each physician and a BPN, which was set at the top 25th percentile for each specific APR DRG. BPNs were determined after exclusions using the following criteria:

• 1

  Each eligible physician had to have at least 10 admissions within their specialty;

• 2

  Each eligible DRG had to have at least 5 qualifying physicians within a medical specialty;

• 3

  Each eligible APR DRG had to have at least 3 qualifying admissions;

• 4

  If the above criteria are met, the BPN was set at the mean of the top 25th percentile of physicians (25% of the physicians with the lowest costs).

Once BPNs were determined, patients were grouped by physician and compared to the BPN for a particular APR DRG. All patients of participating physicians with qualifying APR DRGs were included in the analysis reports summarizing these results, computed quarterly and distributed to each physician. Obstetrical and psychiatric admissions were excluded in the program. APR DRG data for each physician was compared from year to year to determine whether an individual physician demonstrated measurable improvement in performance.

The gainsharing program was implemented in 2006. Physician participation was voluntary. Payments were made to physicians without any risk or penalties from participation. Incentives were based on individual performance. Incentives for nonsurgical admissions were intended to offset the loss of physician income related to more efficient medical management and a reduced hospital length of stay (LOS). Income for surgical admissions was intended to reward physicians for efficient preoperative and postoperative care.

The methodology provides financial incentives for physicians for each hospital discharge in 2 ways:

• 1

  Improvement in costs per case against their own historical performance;

• 2

  Cost per case performance compared to BPN.

In the first year of the gainsharing program, two thirds of the total allowable incentive payments were allocated to physicians' improvement, with one third based on a performance metric. Payments for improvement were phased out over the first 3 years of the gainsharing program, with payments focused fully on performance in Year 3. Cases were adjusted for case‐mix and severity of illness (four levels of APR DRG). Physicians were not penalized for any cases in which costs greatly exceeded BPN. A floor was placed at the BPN and no additional financial incentives were paid for surpassing it. Baselines and BPNs were recalculated yearly.

A key aspect of the gainsharing program was the establishment of specific quality parameters (Table 2) that need to be met before any incentive payments were made. A committee regularly reviewed the quality performance data of each physician to determine eligibility for payments. Physicians were considered to be ineligible for incentive compensation until the next measurement period if there was evidence of failure to adequately meet these measures. At least 80% compliance with core measures (minimum 5 discharges in each domain) was expected. Infectious complication rates were to remain not more than 1 standard deviation above National Healthcare Safety Network rates during the same time period. In addition, payments were withheld from physicians if it was found that the standard of care was not met for any morbidity or mortality that was peer reviewed or if there were any significant patient complaints. Readmission rates were expected to remain at or below the baseline established during the previous 12 months by DRG.

Employed and private practice community physicians were both eligible for the gainsharing program. Physician participation in the program was voluntary. All patients admitted to the Medical Center received notification on admission about the program. The aggregate costs by DRG were calculated quarterly. Savings over the previous yearif anywere calculated. A total of 20% of the savings was used to administer the program and for incentive payments to physicians.

From July 1, 2006 through September 2008, only commercial managed care cases were eligible for this program. As a result of the approval of the gainsharing program as a demonstration project by the Centers for Medicare and Medicaid Services (CMS), Medicare cases were added to the program starting October 1, 2008.

Physician Payment Calculation Methodology

Results

Over the 3‐year period, 184 physicians enrolled, representing 54% of those eligible. The remainder of physicians either decided not to enroll or were not eligible due to inadequate number of index DRG cases or excluded diagnoses. Payer mix was 27% Medicare and 48% of the discharges were commercial and managed care. The remaining cases were a combination of Medicaid and self‐pay. A total of 29,535 commercial and managed care discharges were evaluated from participating physicians (58%) and 20,360 similar discharges from non‐participating physicians. This number of admissions accounted for 29% of all hospital discharges during this time period. Surgical admissions accounted for 43% and nonsurgical admissions for 57%. The distribution of patients by service is shown in Table 3. Pulmonary and cardiology diagnoses were the most frequent reasons for medical admissions. General and head and neck surgery were the most frequent surgical admissions. During the time period of the gainsharing program, the medical center saved $25.1 million for costs attributed to these cases. Participating physicians saved $6.9 million more than non‐participating physicians (P = 0.02, Figure 1), but all discharges demonstrated cost savings during the study period. Cost savings (Figure 2) resulted from savings in medical/surgical supplies and implants (35%), daily hospital costs, (28%), intensive care unit costs (16%) and coronary care unit costs (15%), and operating room costs (8%). Reduction in cost from reduced magnetic resonance imaging (MRI) use was not statistically significant. There were minimal increases in costs due to computed tomography (CT) scan use, cardiopulmonary care, laboratory use, pharmacy and blood bank, but none of these reached statistical significance.

Figure 1

Figure 2

Hospital LOS decreased 9.8% from baseline among participating doctors, while LOS decreased 9.0% among non‐participating physicians; this difference was not statistically significant (P = 0.6). Participating physicians reduced costs by an average of $7,871 per quarter, compared to a reduction in costs by $3,018 for admissions by non‐participating physicians (P < 0.0001). The average savings per admission for the participating physicians were $1,835, and for non‐participating physicians were $1,107, a difference of $728 per admission. Overall, cost savings during the three year period averaged $105,000 per physician who participated in the program and $67,000 per physician who did not (P < 0.05). There was not a statistical difference in savings between medical and surgical admissions (P = 0.24).

Deviations from quality thresholds were identified during this time period. Some or all of the gainsharing income was withheld from 8% of participating physicians due to quality issues, incomplete medical records, or administrative reasons. Payouts to participating physicians averaged $1,866 quarterly (range $0‐$27,631). Overall, 9.4% of the hospital savings was directly paid to the participating physicians. Compliance with core measures improved in the following domains from year 2006 to 2009; acute myocardial infarction 94% to 98%, congestive heart failure 76% to 93%, pneumonia 88% to 97%, and surgical care improvement project 90% to 97%, (P = 0.17). There was no measurable increase in 30‐day mortality or readmission by APR‐DRG. The number of incomplete medical records decreased from an average of 43% of the total number of records in the second quarter of 2006 to 30% in the second quarter of 2009 (P < 0.0001). Other quality indicators remained statistically unchanged.

Discussion

The promise of gainsharing may motivate physicians to decrease hospital costs while maintaining quality medical care, since it aligns physician and hospital incentives. Providing a reward to physicians creates positive reinforcement, which is more effective than warnings against poor performing physicians (carrot vs. stick).5, 6 This study is the first and largest of its kind to show the results of a gainsharing program for inpatient medical and surgical admissions and demonstrates that significant cost savings may be achieved. This is similar to previous studies that have shown positive outcomes for pay‐for‐performance programs.7

Participating physicians in the present study accumulated almost $7 million more in savings than non‐participating physicians. Over time this difference has increased, possibly due to a learning curve in educating participating physicians and the way in which information about their performance is given back to them. A significant portion of the hospital's cost savings was through improvements in documentation and completion of medical records. While there was an actual reduction in average length of stay (ALOS), better documentation may also have contributed to adjusting the severity level within each DRG.

Using financial incentives to positively impact on physician behavior is not new. One program in a community‐based hospitalist group reported similar improvements in medical record documentation, as well as improvements in physician meeting attendance and quality goals.8 Another study found that such hospital programs noted improved physician engagement and commitment to best practices and to improving the quality of care.9

There is significant experience in the outpatient setting using pay‐for‐performance programs to enhance quality. Millett et al.10 demonstrated a reduction in smoking among patients with diabetes in a program in the United Kingdom. Another study in Rochester, New York that used pay‐for‐performance incentives demonstrated better diabetes management.11 Mandel and Kotagal12 demonstrated improved asthma care utilizing a quality incentive program.

The use of financial motivation for physicians, as part of a hospital pay‐for‐performance program, has been shown to lead to improvements in quality performance scores when compared to non pay‐for‐performance hospitals.13 Berthiaume demonstrated decreased costs and improvements in risk‐adjusted complications and risk‐adjusted LOS in patents admitted for acute coronary intervention in a pay‐for‐performance program.14 Quality initiatives were integral for the gainsharing program, since measures such as surgical site infections may increase LOS and hospital costs. Core measures related to the care of patients with acute myocardial infarction, heart failure, pneumonia, and surgical prophylaxis steadily improved since the initiation of the gainsharing program. Gainsharing programs also enhance physician compliance with administrative responsibilities such as the completion of medical records.

One unexpected finding of our study was that there was a cost savings per admission even in the patients of physicians who did not participate in the gainsharing program. While the participating physicians showed statistically significant improvements in cost savings, savings were found in both groups. This raises the question as to whether these cost reductions could have been impacted by other factors such as new labor or vendor contracts, better documentation, improved operating room utilization and improved and timely documentation in the medical record. Another possibility is the Hawthorne effect on physicians, who altered their behavior with knowledge that process and outcome measurement were being measured. Physicians who voluntarily sign up for a gainsharing program would be expected to be more committed to the success of this program than physicians who decide to opt out. While this might appear to be a selection bias it does illustrate the point that motivated physicians are more likely to positively change their practice behaviors. However, one might suggest that financial savings directly attributed to the gainsharing program was not the $25.1 million saved during the 3 years overall, but the difference between participating and non‐participating physicians, or $6.9 million.

While the motivation to complete medical records was significant (gainsharing dollars were withheld from doctors with more than 5 incomplete charts for more than 30 days) it was not the only reason why the number of delinquent chart percentage decreased during the study period. While the improvement was significant, there are still more opportunities to reduce the number of incomplete charts. Hospital regulatory inspections and periodic physician education were also likely to have reduced the number of incomplete inpatient charts during this time period and may do so in the future.15

The program focused on the physician activities that have the greatest impact on hospital costs. While optimizing laboratory, blood bank, and pharmacy management decreased hospital costs; we found that improvements in patient LOS, days in an intensive care unit, and management of surgical implants had the greatest impact on costs. Orthopedic surgeons began to use different implants, and all surgeons refrained from opening disposable surgical supplies until needed. Patients in intensive care unit beds stable for transfer were moved to regular medical/surgical rooms earlier. Since the program helped physicians understand the importance of LOS, many physicians increased their rounding on weekends and considered LOS implications before ordering diagnostic procedures that could be performed as an outpatient. Nurses, physician extenders such as physician assistants, and social workers have played an important role in streamlining patient care and hospital discharge; however, they were not directly rewarded under this program.

There are challenges to aligning the incentives of internists compared to procedure‐based specialists. This may be that the result of surgeons receiving payment for bundled care and thus the incentives are already aligned. The incentive of the program for internists, who get paid for each per daily visit, was intended to overcome the lost income resulting from an earlier discharge. Moreover, in the present study, only the discharging physician received incentive payments for each case. Patient care is undoubtedly a team effort and many physicians (radiologists, anesthesiologists, pathologists, emergency medicine physicians, consultant specialty physicians, etc.) are clearly left out in the present gainsharing program. Aligning the incentives of these physicians might be necessary. Furthermore, the actions of other members of the medical team and consultants, by their behaviors, could limit the incentive payments for the discharging physician. The discharging physician is often unable to control the transfer of a patient from a high‐cost or severity unit, or improve the timeliness of consulting physicians. Previous authors have raised the issue as to whether a physician should be prevented from payment because of the actions of another member of the medical team.16

Ensuring a fair and transparent system is important in any pay‐for‐performance program. The present gainsharing program required sophisticated data analysis, which added to the costs of the program. To implement such a program, data must be clear and understandable, segregated by DRG and severity adjusted. But should the highest reward payments go to those who perform the best or improve the most? In the present study, some physicians were consistently unable to meet quality benchmarks. This may be related to several factors, 1 of which might be a particular physician's case mix. Some authors have raised concerns that pay‐for‐performance programs may unfairly impact physicians who care for more challenging or patients from disadvantaged socioeconomic circumstances.17 Other authors have questioned whether widespread implementation of such a program could potentially increase healthcare disparities in the community.18 It has been suggested by Greene and Nash that for a program to be successful, physicians who feel they provide good care yet but are not rewarded should be given an independent review.16 Such a process is important to prevent resentment among physicians who are unable to meet benchmarks for payment, despite hard work.19 Conversely, other studies have found that many physicians who receive payments in a pay‐for‐performance system do not necessarily consciously make improvement to enhance financial performance.20 Only 54% of eligible physicians participated in the present gainsharing program. This is likely due to lack of understanding about the program, misperceptions about the ethics of such programs, perceived possible negative patient outcome, conflict of interest and mistrust.21, 22 This underscores the importance of providing understandable performance results, education, and a physician champion to help facilitate communication and enhanced outcomes. What is clear is that the perception by participating physicians is that this program is worthwhile as the number of participating physicians has steadily increased and it has become an incentive for new providers to choose this medical center over others.

In conclusion, the results of the present study show that physicians can help hospitals reduce inpatients costs while maintaining or improving hospital quality. Improvements in patient LOS, implant costs, overall costs per admission, and medical record completion were noted. Further work is needed to improve physician education and better understand the impact of uneven physician case mix. Further efforts are necessary to allow other members of the health care team to participate and benefit from gainsharing.

Hospitals are challenged to improve quality while reducing costs, yet traditional methods of cost containment have had limited success in aligning the goals of hospitals and physicians. Physicians directly control more than 80% of total medical costs.1 The current fee‐for‐service system encourages procedures and the use of hospital resources. Without the proper incentives to gain active participation and collaboration of the medical staff in improving the efficiency of care, the ability to manage medical costs and improve hospital operational and financial performance is hampered. A further challenge is to encourage physicians to improve the quality of care and maintain safe medical practice. While several examples of pay‐for‐performance (P4P) have previously been attempted to increase efficiency, gainsharing offers real opportunities to achieve these outcomes.

Previous reports regarding the results of gainsharing programs describe its use in outpatient settings and its limited ability to reduce costs for inpatient care for surgical implants such as coronary stents2 or orthopedic prostheses.3 The present study represents the largest series to date using a gainsharing model in a comprehensive program of inpatient care at a tertiary care medical center.

Patients and Methods

Beth Israel Medical Center is a 1000‐bed tertiary care university‐affiliated teaching hospital, located in New York City. The hospital serves a large and ethnically diverse community predominantly located in the lower east side of Manhattan and discharged about 50,000 patients per year during the study period of July 2006 through June 2009.

Applied Medical Software, Inc. (AMS, Collingswood, NJ) analyzed hospital data for case mix and severity. To establish best practice norms (BPNs), AMS used inpatient discharge data (UB‐92) to determine costs by APR‐DRG's4 during calendar year 2005, prior to the inception of the program to establish BPNs. Costs were allocated into specific areas listed in Table 1. A minimum of 10 cases was necessary in each DRG. Cost outliers (as defined by the mean cost of the APR DRG plus 3 standard deviations) were excluded. These data were used to establish a baseline for each physician and a BPN, which was set at the top 25th percentile for each specific APR DRG. BPNs were determined after exclusions using the following criteria:

• 1

  Each eligible physician had to have at least 10 admissions within their specialty;

• 2

  Each eligible DRG had to have at least 5 qualifying physicians within a medical specialty;

• 3

  Each eligible APR DRG had to have at least 3 qualifying admissions;

• 4

  If the above criteria are met, the BPN was set at the mean of the top 25th percentile of physicians (25% of the physicians with the lowest costs).

Once BPNs were determined, patients were grouped by physician and compared to the BPN for a particular APR DRG. All patients of participating physicians with qualifying APR DRGs were included in the analysis reports summarizing these results, computed quarterly and distributed to each physician. Obstetrical and psychiatric admissions were excluded in the program. APR DRG data for each physician was compared from year to year to determine whether an individual physician demonstrated measurable improvement in performance.

The gainsharing program was implemented in 2006. Physician participation was voluntary. Payments were made to physicians without any risk or penalties from participation. Incentives were based on individual performance. Incentives for nonsurgical admissions were intended to offset the loss of physician income related to more efficient medical management and a reduced hospital length of stay (LOS). Income for surgical admissions was intended to reward physicians for efficient preoperative and postoperative care.

The methodology provides financial incentives for physicians for each hospital discharge in 2 ways:

• 1

  Improvement in costs per case against their own historical performance;

• 2

  Cost per case performance compared to BPN.

In the first year of the gainsharing program, two thirds of the total allowable incentive payments were allocated to physicians' improvement, with one third based on a performance metric. Payments for improvement were phased out over the first 3 years of the gainsharing program, with payments focused fully on performance in Year 3. Cases were adjusted for case‐mix and severity of illness (four levels of APR DRG). Physicians were not penalized for any cases in which costs greatly exceeded BPN. A floor was placed at the BPN and no additional financial incentives were paid for surpassing it. Baselines and BPNs were recalculated yearly.

A key aspect of the gainsharing program was the establishment of specific quality parameters (Table 2) that need to be met before any incentive payments were made. A committee regularly reviewed the quality performance data of each physician to determine eligibility for payments. Physicians were considered to be ineligible for incentive compensation until the next measurement period if there was evidence of failure to adequately meet these measures. At least 80% compliance with core measures (minimum 5 discharges in each domain) was expected. Infectious complication rates were to remain not more than 1 standard deviation above National Healthcare Safety Network rates during the same time period. In addition, payments were withheld from physicians if it was found that the standard of care was not met for any morbidity or mortality that was peer reviewed or if there were any significant patient complaints. Readmission rates were expected to remain at or below the baseline established during the previous 12 months by DRG.

Employed and private practice community physicians were both eligible for the gainsharing program. Physician participation in the program was voluntary. All patients admitted to the Medical Center received notification on admission about the program. The aggregate costs by DRG were calculated quarterly. Savings over the previous yearif anywere calculated. A total of 20% of the savings was used to administer the program and for incentive payments to physicians.

From July 1, 2006 through September 2008, only commercial managed care cases were eligible for this program. As a result of the approval of the gainsharing program as a demonstration project by the Centers for Medicare and Medicaid Services (CMS), Medicare cases were added to the program starting October 1, 2008.

Physician Payment Calculation Methodology

Results

Over the 3‐year period, 184 physicians enrolled, representing 54% of those eligible. The remainder of physicians either decided not to enroll or were not eligible due to inadequate number of index DRG cases or excluded diagnoses. Payer mix was 27% Medicare and 48% of the discharges were commercial and managed care. The remaining cases were a combination of Medicaid and self‐pay. A total of 29,535 commercial and managed care discharges were evaluated from participating physicians (58%) and 20,360 similar discharges from non‐participating physicians. This number of admissions accounted for 29% of all hospital discharges during this time period. Surgical admissions accounted for 43% and nonsurgical admissions for 57%. The distribution of patients by service is shown in Table 3. Pulmonary and cardiology diagnoses were the most frequent reasons for medical admissions. General and head and neck surgery were the most frequent surgical admissions. During the time period of the gainsharing program, the medical center saved $25.1 million for costs attributed to these cases. Participating physicians saved $6.9 million more than non‐participating physicians (P = 0.02, Figure 1), but all discharges demonstrated cost savings during the study period. Cost savings (Figure 2) resulted from savings in medical/surgical supplies and implants (35%), daily hospital costs, (28%), intensive care unit costs (16%) and coronary care unit costs (15%), and operating room costs (8%). Reduction in cost from reduced magnetic resonance imaging (MRI) use was not statistically significant. There were minimal increases in costs due to computed tomography (CT) scan use, cardiopulmonary care, laboratory use, pharmacy and blood bank, but none of these reached statistical significance.

Figure 1

Figure 2

Hospital LOS decreased 9.8% from baseline among participating doctors, while LOS decreased 9.0% among non‐participating physicians; this difference was not statistically significant (P = 0.6). Participating physicians reduced costs by an average of $7,871 per quarter, compared to a reduction in costs by $3,018 for admissions by non‐participating physicians (P < 0.0001). The average savings per admission for the participating physicians were $1,835, and for non‐participating physicians were $1,107, a difference of $728 per admission. Overall, cost savings during the three year period averaged $105,000 per physician who participated in the program and $67,000 per physician who did not (P < 0.05). There was not a statistical difference in savings between medical and surgical admissions (P = 0.24).

Deviations from quality thresholds were identified during this time period. Some or all of the gainsharing income was withheld from 8% of participating physicians due to quality issues, incomplete medical records, or administrative reasons. Payouts to participating physicians averaged $1,866 quarterly (range $0‐$27,631). Overall, 9.4% of the hospital savings was directly paid to the participating physicians. Compliance with core measures improved in the following domains from year 2006 to 2009; acute myocardial infarction 94% to 98%, congestive heart failure 76% to 93%, pneumonia 88% to 97%, and surgical care improvement project 90% to 97%, (P = 0.17). There was no measurable increase in 30‐day mortality or readmission by APR‐DRG. The number of incomplete medical records decreased from an average of 43% of the total number of records in the second quarter of 2006 to 30% in the second quarter of 2009 (P < 0.0001). Other quality indicators remained statistically unchanged.

Discussion

The promise of gainsharing may motivate physicians to decrease hospital costs while maintaining quality medical care, since it aligns physician and hospital incentives. Providing a reward to physicians creates positive reinforcement, which is more effective than warnings against poor performing physicians (carrot vs. stick).5, 6 This study is the first and largest of its kind to show the results of a gainsharing program for inpatient medical and surgical admissions and demonstrates that significant cost savings may be achieved. This is similar to previous studies that have shown positive outcomes for pay‐for‐performance programs.7

Participating physicians in the present study accumulated almost $7 million more in savings than non‐participating physicians. Over time this difference has increased, possibly due to a learning curve in educating participating physicians and the way in which information about their performance is given back to them. A significant portion of the hospital's cost savings was through improvements in documentation and completion of medical records. While there was an actual reduction in average length of stay (ALOS), better documentation may also have contributed to adjusting the severity level within each DRG.

Using financial incentives to positively impact on physician behavior is not new. One program in a community‐based hospitalist group reported similar improvements in medical record documentation, as well as improvements in physician meeting attendance and quality goals.8 Another study found that such hospital programs noted improved physician engagement and commitment to best practices and to improving the quality of care.9

There is significant experience in the outpatient setting using pay‐for‐performance programs to enhance quality. Millett et al.10 demonstrated a reduction in smoking among patients with diabetes in a program in the United Kingdom. Another study in Rochester, New York that used pay‐for‐performance incentives demonstrated better diabetes management.11 Mandel and Kotagal12 demonstrated improved asthma care utilizing a quality incentive program.

The use of financial motivation for physicians, as part of a hospital pay‐for‐performance program, has been shown to lead to improvements in quality performance scores when compared to non pay‐for‐performance hospitals.13 Berthiaume demonstrated decreased costs and improvements in risk‐adjusted complications and risk‐adjusted LOS in patents admitted for acute coronary intervention in a pay‐for‐performance program.14 Quality initiatives were integral for the gainsharing program, since measures such as surgical site infections may increase LOS and hospital costs. Core measures related to the care of patients with acute myocardial infarction, heart failure, pneumonia, and surgical prophylaxis steadily improved since the initiation of the gainsharing program. Gainsharing programs also enhance physician compliance with administrative responsibilities such as the completion of medical records.

One unexpected finding of our study was that there was a cost savings per admission even in the patients of physicians who did not participate in the gainsharing program. While the participating physicians showed statistically significant improvements in cost savings, savings were found in both groups. This raises the question as to whether these cost reductions could have been impacted by other factors such as new labor or vendor contracts, better documentation, improved operating room utilization and improved and timely documentation in the medical record. Another possibility is the Hawthorne effect on physicians, who altered their behavior with knowledge that process and outcome measurement were being measured. Physicians who voluntarily sign up for a gainsharing program would be expected to be more committed to the success of this program than physicians who decide to opt out. While this might appear to be a selection bias it does illustrate the point that motivated physicians are more likely to positively change their practice behaviors. However, one might suggest that financial savings directly attributed to the gainsharing program was not the $25.1 million saved during the 3 years overall, but the difference between participating and non‐participating physicians, or $6.9 million.

While the motivation to complete medical records was significant (gainsharing dollars were withheld from doctors with more than 5 incomplete charts for more than 30 days) it was not the only reason why the number of delinquent chart percentage decreased during the study period. While the improvement was significant, there are still more opportunities to reduce the number of incomplete charts. Hospital regulatory inspections and periodic physician education were also likely to have reduced the number of incomplete inpatient charts during this time period and may do so in the future.15

The program focused on the physician activities that have the greatest impact on hospital costs. While optimizing laboratory, blood bank, and pharmacy management decreased hospital costs; we found that improvements in patient LOS, days in an intensive care unit, and management of surgical implants had the greatest impact on costs. Orthopedic surgeons began to use different implants, and all surgeons refrained from opening disposable surgical supplies until needed. Patients in intensive care unit beds stable for transfer were moved to regular medical/surgical rooms earlier. Since the program helped physicians understand the importance of LOS, many physicians increased their rounding on weekends and considered LOS implications before ordering diagnostic procedures that could be performed as an outpatient. Nurses, physician extenders such as physician assistants, and social workers have played an important role in streamlining patient care and hospital discharge; however, they were not directly rewarded under this program.

There are challenges to aligning the incentives of internists compared to procedure‐based specialists. This may be that the result of surgeons receiving payment for bundled care and thus the incentives are already aligned. The incentive of the program for internists, who get paid for each per daily visit, was intended to overcome the lost income resulting from an earlier discharge. Moreover, in the present study, only the discharging physician received incentive payments for each case. Patient care is undoubtedly a team effort and many physicians (radiologists, anesthesiologists, pathologists, emergency medicine physicians, consultant specialty physicians, etc.) are clearly left out in the present gainsharing program. Aligning the incentives of these physicians might be necessary. Furthermore, the actions of other members of the medical team and consultants, by their behaviors, could limit the incentive payments for the discharging physician. The discharging physician is often unable to control the transfer of a patient from a high‐cost or severity unit, or improve the timeliness of consulting physicians. Previous authors have raised the issue as to whether a physician should be prevented from payment because of the actions of another member of the medical team.16

Ensuring a fair and transparent system is important in any pay‐for‐performance program. The present gainsharing program required sophisticated data analysis, which added to the costs of the program. To implement such a program, data must be clear and understandable, segregated by DRG and severity adjusted. But should the highest reward payments go to those who perform the best or improve the most? In the present study, some physicians were consistently unable to meet quality benchmarks. This may be related to several factors, 1 of which might be a particular physician's case mix. Some authors have raised concerns that pay‐for‐performance programs may unfairly impact physicians who care for more challenging or patients from disadvantaged socioeconomic circumstances.17 Other authors have questioned whether widespread implementation of such a program could potentially increase healthcare disparities in the community.18 It has been suggested by Greene and Nash that for a program to be successful, physicians who feel they provide good care yet but are not rewarded should be given an independent review.16 Such a process is important to prevent resentment among physicians who are unable to meet benchmarks for payment, despite hard work.19 Conversely, other studies have found that many physicians who receive payments in a pay‐for‐performance system do not necessarily consciously make improvement to enhance financial performance.20 Only 54% of eligible physicians participated in the present gainsharing program. This is likely due to lack of understanding about the program, misperceptions about the ethics of such programs, perceived possible negative patient outcome, conflict of interest and mistrust.21, 22 This underscores the importance of providing understandable performance results, education, and a physician champion to help facilitate communication and enhanced outcomes. What is clear is that the perception by participating physicians is that this program is worthwhile as the number of participating physicians has steadily increased and it has become an incentive for new providers to choose this medical center over others.

In conclusion, the results of the present study show that physicians can help hospitals reduce inpatients costs while maintaining or improving hospital quality. Improvements in patient LOS, implant costs, overall costs per admission, and medical record completion were noted. Further work is needed to improve physician education and better understand the impact of uneven physician case mix. Further efforts are necessary to allow other members of the health care team to participate and benefit from gainsharing.

Transfer of responsibility for patients, or handoff,1 occurs frequently in hospitalist services, requiring excellent and timely communication to ensure patient safety. Communication failure is a major contributor to medical errors.2, 3 Recognizing such findings, a growing body of literature addresses handoff techniques for learners.47

Vidyarthi described the handoff process as traditionally informal, unstructured, and idiosyncratic,4 and many believe efforts to formalize and structure this process are important for patient safety.8 Standardized handoff forms have improved accuracy of information.9 Web‐based sign‐out systems reportedly reduced the number of patients missed on rounds.10

Hospitalists also face challenges with effective communication during service change.11 The Society of Hospital Medicine identified the handoff skill as a core competency for hospitalists, and recommendations based on a systematic review of the literature were published.12 Inpatient medicine programs are increasingly using midlevel providers such as nurse practitioners (NPs) and physician assistants (PAs) along with hospitalists to accommodate workload while maintaining the scholarly enterprise in academic centers.13 To our knowledge there is no literature examining the hospitalist service handoffs involving NP/PAs.

We wished to study the effectiveness and timeliness of the morning handoff from the night coverage providers to the daytime teams consisting of one hospitalist and one NP/PA. Our objectives were to identify deficiencies and to evaluate the effectiveness of a restructured handoff process.

Methods

The Mayo Clinic Institutional Review Board reviewed and approved this study.

Intervention

The improvement team structured a new handoff process to address these deficiencies.

• 1

  Environment: Moved to a smaller room (lower ceiling, less ambient noise).

• 2

  Identification: table cards designating seats for participants (reduced queries regarding what service are you, today?).

• 3

  Start Times: Each service team assigned a consistent start time (labeled on the table card) within a 15‐minute period, and although earlier reportage could occur, any service team present at their designated time has priority for the attention of the night NP/PA, and the opportunity to ask questions.

• 4

  Quiet and Focus: HIM members were reminded to remain quiet in the handoff room, so the service receiving report has the floor and personal conversations must not impede the principals.

• 5

  Visual Cue: Green Good to go sign placed on team table cards when no verbal was required.

• 6

  Written e‐Material: The improvement team required elements of a brief written report in a specified column of our existing electronic service list (ESL). The ESL is a custom designed template importing laboratory, medication, and demographic data automatically but also capable of free text additions (Figure 1). All providers were instructed to update the ESL every 12 hours.

• 7

  Admission and Progress Notes: After manual electronic medical record search, the CAs printed any notes generated in the preceding 12 hours and placed them by the team table card.

Figure 1

The improvement team provided education for the new process at a division meeting and through e‐mail. The recommended report sequence was night NP/PA reporting and day service teams asking questions and seeking clarifications. We discouraged editorial comments and chit‐chat.

A member of the improvement team monitored the new handoff process for 15 days, and 3 months later for 10 days.

Survey

An anonymous survey (Figure 2) concerning staff satisfaction with handoff was conducted immediately before and 15 days after the intervention. In the e‐mail containing the postintervention survey, providers were asked to respond only if they had been on service the preceding 15 days (and thus eligible to participate in handoff). To help insure this, the first question read, Have you been on service during the past 15 days?

Figure 2

Results

Thirty‐six of the 44 providers (82%) answered the preintervention survey, including 18 of 22 hospitalists (82%), 17 of 20 NPs/PAs (85%), and 1 of 2 CAs (50%). During the intervention based on our staffing model, 21 providers had the opportunity to participate in handoff, and 18 (86%) answered the postintervention survey, including 5 of 6 hospitalists (83%), 9 of 14 NPs/PAs (64%), and 2 of 2 CAs (100%). All respondents to the postintervention survey reported being on service during the previous 15 days.

As summarized in Table 1, compared to 60.5% of survey participants (n = 38) who thought morning handoff was performed in a timely fashion preintervention, 100% (n = 15) felt it was performed in a timely fashion postintervention (P = 0.005). The average time spent in morning report before the intervention was 11 minutes, as compared to 5 minutes after the intervention (P 0.0028). Prior to the intervention, 6.5 minutes of the handoff were viewed to be wasteful, as compared to 0.5 minutes of the handoff in the postintervention survey (P 0.0001). Attendance and quality of information perceptions did not demonstrate statistically significant change.

During the 15‐day observation period, morning handoff started by 0745 on 14 of 15 (93%) of days and finished by 0800 on 15 of 15 (100%) of days. Table cards, ESL, and progress notes were on the table by 0745 on 15 of 15 (100%) of days following the intervention. Three months after the intervention, the following were observed: morning handoff started by 0745 on 10 of 10 (100%) of days; finished by 0800 on 10 of 10 (100%) of days; and table cards, ESL, and progress notes were on the table by 0745 on 10 of 10 (100%) of days.

Discussion

We describe an intervention that set the expectation for formal, structured written and verbal communication in a focused environment involving outgoing and incoming clinicians, resulting in improved satisfaction. Before the intervention, the improvement team identified by consensus 4 problems: unpredictable start time, inefficiency, environment, and report quality. Formal structuring of our handoff process resulted in statistically significant improvement in handoff timeliness and efficiency in the view of the HIM division members. Process improvement included precise team specific start times within a 12‐minute window to improve reliability and predictability and eliminating nonproductive waiting. Additionally, receiving teams were clearly identified with table cards so that no time was wasted locating the appropriate service for report, and minimizing role‐identification challenges. The good to go sign signaled teams that no events had occurred overnight requiring verbal report. Handoff timeliness persisted 3 months after the intervention, suggesting that the process is easily sustainable.

Postintervention survey comments noted the improved environment: a smaller, quieter room with the door closed. Before the intervention, all day team providers, CAs and night provider met in a large, loud room where multiple conversations were commonplace. Previous study of the handoff process supports creating an environment free of distraction.4

Postintervention survey responses to the open‐ended questions suggested improved provider satisfaction with the quality of the report. We believe this occurred for several reasons. First, having a precise start time for each team within a 12‐minute window led to a more focused report. Second, the ESL provided a column for providers to suggest plans of care for anticipated overnight events to improve preparedness and avoid significant omissions. Third, hospital notes generated overnight were made available which allowed daytime providers to review events before handoff, for a more informed update, or just after verbal report to reinforce the information just received, a technique used in other high‐reliability organizations.16 This measure also provided an at‐a‐glance view of each patient, decreasing the complexity of handoff.17

This study has important limitations. We address the handoff process of 1 hospitalist group at a single academic center. NP/PAs are the clinicians with first‐call responsibility for the night coverage of our patients, and the handoff process between the night NP/PA and daytime provider was studied. The handoff between physicians for patients admitted overnight was not assessed. Another limitation is that the time spent in handoff is reported as a participant estimate. There was no objective measurement of time, and respondents may have been biased. An additional limitation of our study concerns the preintervention and postintervention surveys. Both surveys were anonymous, which makes discerning the absolute impact of the intervention difficult due to the lack of paired responses. Lastly, our institution has an ESL. This option may not be available in other hospital systems.

Several deficiencies in the handoff process were addressed by providing key clinical data verbally and in written format, enhancing the physical environment, and defining each team's handoff start time. Our process improvements are consistent with the handoff recommendations endorsed by the Society of Hospital Medicine.12 Subsequent direct observation, subjective reports, and survey results demonstrated improvement in the handoff process.

Future studies might measure the effectiveness of morning handoff by end‐shift interviews of the daytime clinicians. Similarly, a study of evening handoff could measure the efficiency and effectiveness of report given by day teams to night‐coverage colleagues. Furthermore, if the handoff report skill set can be more rigorously defined and measured, a hospitalist clinical competency for hospitalists and NP/PAs could be developed in this core process‐of‐care.12

Transfer of responsibility for patients, or handoff,1 occurs frequently in hospitalist services, requiring excellent and timely communication to ensure patient safety. Communication failure is a major contributor to medical errors.2, 3 Recognizing such findings, a growing body of literature addresses handoff techniques for learners.47

Vidyarthi described the handoff process as traditionally informal, unstructured, and idiosyncratic,4 and many believe efforts to formalize and structure this process are important for patient safety.8 Standardized handoff forms have improved accuracy of information.9 Web‐based sign‐out systems reportedly reduced the number of patients missed on rounds.10

Hospitalists also face challenges with effective communication during service change.11 The Society of Hospital Medicine identified the handoff skill as a core competency for hospitalists, and recommendations based on a systematic review of the literature were published.12 Inpatient medicine programs are increasingly using midlevel providers such as nurse practitioners (NPs) and physician assistants (PAs) along with hospitalists to accommodate workload while maintaining the scholarly enterprise in academic centers.13 To our knowledge there is no literature examining the hospitalist service handoffs involving NP/PAs.

We wished to study the effectiveness and timeliness of the morning handoff from the night coverage providers to the daytime teams consisting of one hospitalist and one NP/PA. Our objectives were to identify deficiencies and to evaluate the effectiveness of a restructured handoff process.

Methods

The Mayo Clinic Institutional Review Board reviewed and approved this study.

Intervention

The improvement team structured a new handoff process to address these deficiencies.

• 1

  Environment: Moved to a smaller room (lower ceiling, less ambient noise).

• 2

  Identification: table cards designating seats for participants (reduced queries regarding what service are you, today?).

• 3

  Start Times: Each service team assigned a consistent start time (labeled on the table card) within a 15‐minute period, and although earlier reportage could occur, any service team present at their designated time has priority for the attention of the night NP/PA, and the opportunity to ask questions.

• 4

  Quiet and Focus: HIM members were reminded to remain quiet in the handoff room, so the service receiving report has the floor and personal conversations must not impede the principals.

• 5

  Visual Cue: Green Good to go sign placed on team table cards when no verbal was required.

• 6

  Written e‐Material: The improvement team required elements of a brief written report in a specified column of our existing electronic service list (ESL). The ESL is a custom designed template importing laboratory, medication, and demographic data automatically but also capable of free text additions (Figure 1). All providers were instructed to update the ESL every 12 hours.

• 7

  Admission and Progress Notes: After manual electronic medical record search, the CAs printed any notes generated in the preceding 12 hours and placed them by the team table card.

Figure 1

The improvement team provided education for the new process at a division meeting and through e‐mail. The recommended report sequence was night NP/PA reporting and day service teams asking questions and seeking clarifications. We discouraged editorial comments and chit‐chat.

A member of the improvement team monitored the new handoff process for 15 days, and 3 months later for 10 days.

Survey

An anonymous survey (Figure 2) concerning staff satisfaction with handoff was conducted immediately before and 15 days after the intervention. In the e‐mail containing the postintervention survey, providers were asked to respond only if they had been on service the preceding 15 days (and thus eligible to participate in handoff). To help insure this, the first question read, Have you been on service during the past 15 days?

Figure 2

Results

Thirty‐six of the 44 providers (82%) answered the preintervention survey, including 18 of 22 hospitalists (82%), 17 of 20 NPs/PAs (85%), and 1 of 2 CAs (50%). During the intervention based on our staffing model, 21 providers had the opportunity to participate in handoff, and 18 (86%) answered the postintervention survey, including 5 of 6 hospitalists (83%), 9 of 14 NPs/PAs (64%), and 2 of 2 CAs (100%). All respondents to the postintervention survey reported being on service during the previous 15 days.

As summarized in Table 1, compared to 60.5% of survey participants (n = 38) who thought morning handoff was performed in a timely fashion preintervention, 100% (n = 15) felt it was performed in a timely fashion postintervention (P = 0.005). The average time spent in morning report before the intervention was 11 minutes, as compared to 5 minutes after the intervention (P 0.0028). Prior to the intervention, 6.5 minutes of the handoff were viewed to be wasteful, as compared to 0.5 minutes of the handoff in the postintervention survey (P 0.0001). Attendance and quality of information perceptions did not demonstrate statistically significant change.

During the 15‐day observation period, morning handoff started by 0745 on 14 of 15 (93%) of days and finished by 0800 on 15 of 15 (100%) of days. Table cards, ESL, and progress notes were on the table by 0745 on 15 of 15 (100%) of days following the intervention. Three months after the intervention, the following were observed: morning handoff started by 0745 on 10 of 10 (100%) of days; finished by 0800 on 10 of 10 (100%) of days; and table cards, ESL, and progress notes were on the table by 0745 on 10 of 10 (100%) of days.

Discussion

We describe an intervention that set the expectation for formal, structured written and verbal communication in a focused environment involving outgoing and incoming clinicians, resulting in improved satisfaction. Before the intervention, the improvement team identified by consensus 4 problems: unpredictable start time, inefficiency, environment, and report quality. Formal structuring of our handoff process resulted in statistically significant improvement in handoff timeliness and efficiency in the view of the HIM division members. Process improvement included precise team specific start times within a 12‐minute window to improve reliability and predictability and eliminating nonproductive waiting. Additionally, receiving teams were clearly identified with table cards so that no time was wasted locating the appropriate service for report, and minimizing role‐identification challenges. The good to go sign signaled teams that no events had occurred overnight requiring verbal report. Handoff timeliness persisted 3 months after the intervention, suggesting that the process is easily sustainable.

Postintervention survey comments noted the improved environment: a smaller, quieter room with the door closed. Before the intervention, all day team providers, CAs and night provider met in a large, loud room where multiple conversations were commonplace. Previous study of the handoff process supports creating an environment free of distraction.4

Postintervention survey responses to the open‐ended questions suggested improved provider satisfaction with the quality of the report. We believe this occurred for several reasons. First, having a precise start time for each team within a 12‐minute window led to a more focused report. Second, the ESL provided a column for providers to suggest plans of care for anticipated overnight events to improve preparedness and avoid significant omissions. Third, hospital notes generated overnight were made available which allowed daytime providers to review events before handoff, for a more informed update, or just after verbal report to reinforce the information just received, a technique used in other high‐reliability organizations.16 This measure also provided an at‐a‐glance view of each patient, decreasing the complexity of handoff.17

This study has important limitations. We address the handoff process of 1 hospitalist group at a single academic center. NP/PAs are the clinicians with first‐call responsibility for the night coverage of our patients, and the handoff process between the night NP/PA and daytime provider was studied. The handoff between physicians for patients admitted overnight was not assessed. Another limitation is that the time spent in handoff is reported as a participant estimate. There was no objective measurement of time, and respondents may have been biased. An additional limitation of our study concerns the preintervention and postintervention surveys. Both surveys were anonymous, which makes discerning the absolute impact of the intervention difficult due to the lack of paired responses. Lastly, our institution has an ESL. This option may not be available in other hospital systems.

Several deficiencies in the handoff process were addressed by providing key clinical data verbally and in written format, enhancing the physical environment, and defining each team's handoff start time. Our process improvements are consistent with the handoff recommendations endorsed by the Society of Hospital Medicine.12 Subsequent direct observation, subjective reports, and survey results demonstrated improvement in the handoff process.

Future studies might measure the effectiveness of morning handoff by end‐shift interviews of the daytime clinicians. Similarly, a study of evening handoff could measure the efficiency and effectiveness of report given by day teams to night‐coverage colleagues. Furthermore, if the handoff report skill set can be more rigorously defined and measured, a hospitalist clinical competency for hospitalists and NP/PAs could be developed in this core process‐of‐care.12

Transfer of responsibility for patients, or handoff,1 occurs frequently in hospitalist services, requiring excellent and timely communication to ensure patient safety. Communication failure is a major contributor to medical errors.2, 3 Recognizing such findings, a growing body of literature addresses handoff techniques for learners.47

Vidyarthi described the handoff process as traditionally informal, unstructured, and idiosyncratic,4 and many believe efforts to formalize and structure this process are important for patient safety.8 Standardized handoff forms have improved accuracy of information.9 Web‐based sign‐out systems reportedly reduced the number of patients missed on rounds.10

Hospitalists also face challenges with effective communication during service change.11 The Society of Hospital Medicine identified the handoff skill as a core competency for hospitalists, and recommendations based on a systematic review of the literature were published.12 Inpatient medicine programs are increasingly using midlevel providers such as nurse practitioners (NPs) and physician assistants (PAs) along with hospitalists to accommodate workload while maintaining the scholarly enterprise in academic centers.13 To our knowledge there is no literature examining the hospitalist service handoffs involving NP/PAs.

We wished to study the effectiveness and timeliness of the morning handoff from the night coverage providers to the daytime teams consisting of one hospitalist and one NP/PA. Our objectives were to identify deficiencies and to evaluate the effectiveness of a restructured handoff process.

Methods

The Mayo Clinic Institutional Review Board reviewed and approved this study.

Intervention

The improvement team structured a new handoff process to address these deficiencies.

• 1

  Environment: Moved to a smaller room (lower ceiling, less ambient noise).

• 2

  Identification: table cards designating seats for participants (reduced queries regarding what service are you, today?).

• 3

  Start Times: Each service team assigned a consistent start time (labeled on the table card) within a 15‐minute period, and although earlier reportage could occur, any service team present at their designated time has priority for the attention of the night NP/PA, and the opportunity to ask questions.

• 4

  Quiet and Focus: HIM members were reminded to remain quiet in the handoff room, so the service receiving report has the floor and personal conversations must not impede the principals.

• 5

  Visual Cue: Green Good to go sign placed on team table cards when no verbal was required.

• 6

  Written e‐Material: The improvement team required elements of a brief written report in a specified column of our existing electronic service list (ESL). The ESL is a custom designed template importing laboratory, medication, and demographic data automatically but also capable of free text additions (Figure 1). All providers were instructed to update the ESL every 12 hours.

• 7

  Admission and Progress Notes: After manual electronic medical record search, the CAs printed any notes generated in the preceding 12 hours and placed them by the team table card.

Figure 1

The improvement team provided education for the new process at a division meeting and through e‐mail. The recommended report sequence was night NP/PA reporting and day service teams asking questions and seeking clarifications. We discouraged editorial comments and chit‐chat.

A member of the improvement team monitored the new handoff process for 15 days, and 3 months later for 10 days.

Survey

An anonymous survey (Figure 2) concerning staff satisfaction with handoff was conducted immediately before and 15 days after the intervention. In the e‐mail containing the postintervention survey, providers were asked to respond only if they had been on service the preceding 15 days (and thus eligible to participate in handoff). To help insure this, the first question read, Have you been on service during the past 15 days?

Figure 2

Results

Thirty‐six of the 44 providers (82%) answered the preintervention survey, including 18 of 22 hospitalists (82%), 17 of 20 NPs/PAs (85%), and 1 of 2 CAs (50%). During the intervention based on our staffing model, 21 providers had the opportunity to participate in handoff, and 18 (86%) answered the postintervention survey, including 5 of 6 hospitalists (83%), 9 of 14 NPs/PAs (64%), and 2 of 2 CAs (100%). All respondents to the postintervention survey reported being on service during the previous 15 days.

As summarized in Table 1, compared to 60.5% of survey participants (n = 38) who thought morning handoff was performed in a timely fashion preintervention, 100% (n = 15) felt it was performed in a timely fashion postintervention (P = 0.005). The average time spent in morning report before the intervention was 11 minutes, as compared to 5 minutes after the intervention (P 0.0028). Prior to the intervention, 6.5 minutes of the handoff were viewed to be wasteful, as compared to 0.5 minutes of the handoff in the postintervention survey (P 0.0001). Attendance and quality of information perceptions did not demonstrate statistically significant change.

During the 15‐day observation period, morning handoff started by 0745 on 14 of 15 (93%) of days and finished by 0800 on 15 of 15 (100%) of days. Table cards, ESL, and progress notes were on the table by 0745 on 15 of 15 (100%) of days following the intervention. Three months after the intervention, the following were observed: morning handoff started by 0745 on 10 of 10 (100%) of days; finished by 0800 on 10 of 10 (100%) of days; and table cards, ESL, and progress notes were on the table by 0745 on 10 of 10 (100%) of days.

Discussion

We describe an intervention that set the expectation for formal, structured written and verbal communication in a focused environment involving outgoing and incoming clinicians, resulting in improved satisfaction. Before the intervention, the improvement team identified by consensus 4 problems: unpredictable start time, inefficiency, environment, and report quality. Formal structuring of our handoff process resulted in statistically significant improvement in handoff timeliness and efficiency in the view of the HIM division members. Process improvement included precise team specific start times within a 12‐minute window to improve reliability and predictability and eliminating nonproductive waiting. Additionally, receiving teams were clearly identified with table cards so that no time was wasted locating the appropriate service for report, and minimizing role‐identification challenges. The good to go sign signaled teams that no events had occurred overnight requiring verbal report. Handoff timeliness persisted 3 months after the intervention, suggesting that the process is easily sustainable.

Postintervention survey comments noted the improved environment: a smaller, quieter room with the door closed. Before the intervention, all day team providers, CAs and night provider met in a large, loud room where multiple conversations were commonplace. Previous study of the handoff process supports creating an environment free of distraction.4

Postintervention survey responses to the open‐ended questions suggested improved provider satisfaction with the quality of the report. We believe this occurred for several reasons. First, having a precise start time for each team within a 12‐minute window led to a more focused report. Second, the ESL provided a column for providers to suggest plans of care for anticipated overnight events to improve preparedness and avoid significant omissions. Third, hospital notes generated overnight were made available which allowed daytime providers to review events before handoff, for a more informed update, or just after verbal report to reinforce the information just received, a technique used in other high‐reliability organizations.16 This measure also provided an at‐a‐glance view of each patient, decreasing the complexity of handoff.17

This study has important limitations. We address the handoff process of 1 hospitalist group at a single academic center. NP/PAs are the clinicians with first‐call responsibility for the night coverage of our patients, and the handoff process between the night NP/PA and daytime provider was studied. The handoff between physicians for patients admitted overnight was not assessed. Another limitation is that the time spent in handoff is reported as a participant estimate. There was no objective measurement of time, and respondents may have been biased. An additional limitation of our study concerns the preintervention and postintervention surveys. Both surveys were anonymous, which makes discerning the absolute impact of the intervention difficult due to the lack of paired responses. Lastly, our institution has an ESL. This option may not be available in other hospital systems.

Several deficiencies in the handoff process were addressed by providing key clinical data verbally and in written format, enhancing the physical environment, and defining each team's handoff start time. Our process improvements are consistent with the handoff recommendations endorsed by the Society of Hospital Medicine.12 Subsequent direct observation, subjective reports, and survey results demonstrated improvement in the handoff process.

Future studies might measure the effectiveness of morning handoff by end‐shift interviews of the daytime clinicians. Similarly, a study of evening handoff could measure the efficiency and effectiveness of report given by day teams to night‐coverage colleagues. Furthermore, if the handoff report skill set can be more rigorously defined and measured, a hospitalist clinical competency for hospitalists and NP/PAs could be developed in this core process‐of‐care.12

Hospitalists play key roles in many types of clinical services, including teaching, nonteaching, consultative, and comanagement services.14 While the impact of hospitalist programs on LOS for inpatient medicine services has been studied,58 less work has focused on the impact of hospitalists in other types of service delivery, such as in short‐stay or observation units.

While many hospitals now have short‐stay units to care for observation patients, most are adjuncts of the emergency department. A Canadian hospitalist‐run short‐stay unit that targeted patients with an expected LOS of less than 3 days has been described.9 The experience of a single, chest‐painspecific service has also been reported.10

In August 2005, we introduced a hospitalist‐run observation unit, the Clinical Decision Unit (CDU), at University Hospital, the primary teaching affiliate of the University of Texas Health Science Center at San Antonio (San Antonio, TX). The rationale was that observation‐level care in a dedicated short‐stay unit would be more efficient than in an inpatient general medicine service. Through the creation of this unit, we consolidated the care of all medical observation patients, including patients previously evaluated in a cardiology‐run chest pain unit.

In this brief report, we present a description of the unit as well as a preliminary analysis of the impact of the unit on LOS for the most common CDU diagnoses.

Methods

Results

Discussion and Conclusions

Implementation of a hospitalist‐run observation unit was associated with an overall decreased LOS for patients with the 5 most common CDU discharge diagnoses of chest pain, cellulitis, asthma, pyelonephritis, and syncope. The lack of statistically significantly differences in patient acuity in the preimplementation and postimplementation periods suggests this result is not due to acuity differences, but rather to unit implementation. We believe this reduction resulted from the greater efficiencies of care that occur from clustering observation patients in a geographically separate unit with dedicated nursing staff and efficient workflow. The reduction of 0.2 days over 2148 patients (total number of postimplementation discharges) led to an additional 429.6 days of capacity without adding additional beds. Thus, what might appear to be a modest LOS reduction has a larger impact when patient volume is considered.

For individual diagnoses, significant differences in LOS were seen for patients with cellulitis and asthma The lack of a difference for chest pain may be related to the fact that these patients were cared for in a chest pain unit prior to CDU creation, which likely fostered similar efficiencies. This finding may suggest that hospitalists are as efficient as cardiologists in assessing patients with chest pain. The lack of a difference in LOS for syncope may have reflected a bottleneck in obtaining echocardiogram tests. Finally, the lack of a difference for pyelonephritis may indicate that it is not a diagnosis for which observation is beneficial.

While our use of administrative data over the year‐long preimplementation and postimplementation periods allows for the inclusion of a large number of discharges, the retrospective study design limits the strength of our results. A prospective study would more definitively reduce the possibility of bias and ensure the validity of our finding of reduced LOS.

The creation of a hospitalist‐run observation unit may represent an alternative to emergency departmentrun units. It allows physicians with greater expertise in inpatient medicine to make admission and discharge decisions, allowing emergency department physicians to concentrate on the care of other patients. This can be particularly critical for high‐volume emergency departments. The CDU also offers an alternative to specialist‐run chest pain units. Because patients either stay for only the observation period or are admitted and typically moved off the unit, there is little need for provider continuity, and the discontinuous shift staffing model works well.

In addition to the geographic localization, several aspects of the CDU model may be critical to the successful implementation of similar hospitalist‐run observation units. Dedicated nursing staff with expertise in caring for high‐turnover patients with a more limited spectrum of diagnoses may be a factor. Another factor may be that the lack of less‐experienced trainees in a nonteaching service leads to more efficient care.

A potential area of further exploration includes understanding the differences between CDU patients who are discharged within 23 hours and those who are later admitted. This understanding may help us better differentiate patients appropriate for CDU admission, allowing the creation of more formal admission criteria.

Hospitalists play key roles in many types of clinical services, including teaching, nonteaching, consultative, and comanagement services.14 While the impact of hospitalist programs on LOS for inpatient medicine services has been studied,58 less work has focused on the impact of hospitalists in other types of service delivery, such as in short‐stay or observation units.

While many hospitals now have short‐stay units to care for observation patients, most are adjuncts of the emergency department. A Canadian hospitalist‐run short‐stay unit that targeted patients with an expected LOS of less than 3 days has been described.9 The experience of a single, chest‐painspecific service has also been reported.10

In August 2005, we introduced a hospitalist‐run observation unit, the Clinical Decision Unit (CDU), at University Hospital, the primary teaching affiliate of the University of Texas Health Science Center at San Antonio (San Antonio, TX). The rationale was that observation‐level care in a dedicated short‐stay unit would be more efficient than in an inpatient general medicine service. Through the creation of this unit, we consolidated the care of all medical observation patients, including patients previously evaluated in a cardiology‐run chest pain unit.

In this brief report, we present a description of the unit as well as a preliminary analysis of the impact of the unit on LOS for the most common CDU diagnoses.

Methods

Results

Discussion and Conclusions

Implementation of a hospitalist‐run observation unit was associated with an overall decreased LOS for patients with the 5 most common CDU discharge diagnoses of chest pain, cellulitis, asthma, pyelonephritis, and syncope. The lack of statistically significantly differences in patient acuity in the preimplementation and postimplementation periods suggests this result is not due to acuity differences, but rather to unit implementation. We believe this reduction resulted from the greater efficiencies of care that occur from clustering observation patients in a geographically separate unit with dedicated nursing staff and efficient workflow. The reduction of 0.2 days over 2148 patients (total number of postimplementation discharges) led to an additional 429.6 days of capacity without adding additional beds. Thus, what might appear to be a modest LOS reduction has a larger impact when patient volume is considered.

For individual diagnoses, significant differences in LOS were seen for patients with cellulitis and asthma The lack of a difference for chest pain may be related to the fact that these patients were cared for in a chest pain unit prior to CDU creation, which likely fostered similar efficiencies. This finding may suggest that hospitalists are as efficient as cardiologists in assessing patients with chest pain. The lack of a difference in LOS for syncope may have reflected a bottleneck in obtaining echocardiogram tests. Finally, the lack of a difference for pyelonephritis may indicate that it is not a diagnosis for which observation is beneficial.

While our use of administrative data over the year‐long preimplementation and postimplementation periods allows for the inclusion of a large number of discharges, the retrospective study design limits the strength of our results. A prospective study would more definitively reduce the possibility of bias and ensure the validity of our finding of reduced LOS.

The creation of a hospitalist‐run observation unit may represent an alternative to emergency departmentrun units. It allows physicians with greater expertise in inpatient medicine to make admission and discharge decisions, allowing emergency department physicians to concentrate on the care of other patients. This can be particularly critical for high‐volume emergency departments. The CDU also offers an alternative to specialist‐run chest pain units. Because patients either stay for only the observation period or are admitted and typically moved off the unit, there is little need for provider continuity, and the discontinuous shift staffing model works well.

In addition to the geographic localization, several aspects of the CDU model may be critical to the successful implementation of similar hospitalist‐run observation units. Dedicated nursing staff with expertise in caring for high‐turnover patients with a more limited spectrum of diagnoses may be a factor. Another factor may be that the lack of less‐experienced trainees in a nonteaching service leads to more efficient care.

A potential area of further exploration includes understanding the differences between CDU patients who are discharged within 23 hours and those who are later admitted. This understanding may help us better differentiate patients appropriate for CDU admission, allowing the creation of more formal admission criteria.

Hospitalists play key roles in many types of clinical services, including teaching, nonteaching, consultative, and comanagement services.14 While the impact of hospitalist programs on LOS for inpatient medicine services has been studied,58 less work has focused on the impact of hospitalists in other types of service delivery, such as in short‐stay or observation units.

While many hospitals now have short‐stay units to care for observation patients, most are adjuncts of the emergency department. A Canadian hospitalist‐run short‐stay unit that targeted patients with an expected LOS of less than 3 days has been described.9 The experience of a single, chest‐painspecific service has also been reported.10

In August 2005, we introduced a hospitalist‐run observation unit, the Clinical Decision Unit (CDU), at University Hospital, the primary teaching affiliate of the University of Texas Health Science Center at San Antonio (San Antonio, TX). The rationale was that observation‐level care in a dedicated short‐stay unit would be more efficient than in an inpatient general medicine service. Through the creation of this unit, we consolidated the care of all medical observation patients, including patients previously evaluated in a cardiology‐run chest pain unit.

In this brief report, we present a description of the unit as well as a preliminary analysis of the impact of the unit on LOS for the most common CDU diagnoses.

Methods

Results

Discussion and Conclusions

Implementation of a hospitalist‐run observation unit was associated with an overall decreased LOS for patients with the 5 most common CDU discharge diagnoses of chest pain, cellulitis, asthma, pyelonephritis, and syncope. The lack of statistically significantly differences in patient acuity in the preimplementation and postimplementation periods suggests this result is not due to acuity differences, but rather to unit implementation. We believe this reduction resulted from the greater efficiencies of care that occur from clustering observation patients in a geographically separate unit with dedicated nursing staff and efficient workflow. The reduction of 0.2 days over 2148 patients (total number of postimplementation discharges) led to an additional 429.6 days of capacity without adding additional beds. Thus, what might appear to be a modest LOS reduction has a larger impact when patient volume is considered.

For individual diagnoses, significant differences in LOS were seen for patients with cellulitis and asthma The lack of a difference for chest pain may be related to the fact that these patients were cared for in a chest pain unit prior to CDU creation, which likely fostered similar efficiencies. This finding may suggest that hospitalists are as efficient as cardiologists in assessing patients with chest pain. The lack of a difference in LOS for syncope may have reflected a bottleneck in obtaining echocardiogram tests. Finally, the lack of a difference for pyelonephritis may indicate that it is not a diagnosis for which observation is beneficial.

While our use of administrative data over the year‐long preimplementation and postimplementation periods allows for the inclusion of a large number of discharges, the retrospective study design limits the strength of our results. A prospective study would more definitively reduce the possibility of bias and ensure the validity of our finding of reduced LOS.

The creation of a hospitalist‐run observation unit may represent an alternative to emergency departmentrun units. It allows physicians with greater expertise in inpatient medicine to make admission and discharge decisions, allowing emergency department physicians to concentrate on the care of other patients. This can be particularly critical for high‐volume emergency departments. The CDU also offers an alternative to specialist‐run chest pain units. Because patients either stay for only the observation period or are admitted and typically moved off the unit, there is little need for provider continuity, and the discontinuous shift staffing model works well.

In addition to the geographic localization, several aspects of the CDU model may be critical to the successful implementation of similar hospitalist‐run observation units. Dedicated nursing staff with expertise in caring for high‐turnover patients with a more limited spectrum of diagnoses may be a factor. Another factor may be that the lack of less‐experienced trainees in a nonteaching service leads to more efficient care.

A potential area of further exploration includes understanding the differences between CDU patients who are discharged within 23 hours and those who are later admitted. This understanding may help us better differentiate patients appropriate for CDU admission, allowing the creation of more formal admission criteria.

Nearly half of the hospital beds in the United States are occupied by the elderly,1 whose numbers are increasing.2 The odds of a hospitalized Medicare patient being cared for by a hospitalist are increasing by nearly 30% per year.3 Hospitalists require competence in geriatrics to serve their patients and to teach trainees. Train‐the‐Trainer (TTT) programs both educate health care providers and provide educational materials, information, and skills for teaching others.4 This model has been successfully used in geriatrics to impact knowledge, attitudes, and self‐efficacy among health care workers.46

A prominent example of a geriatrics TTT program is the University of Chicago Curriculum for the Hospitalized Aging Medical Patient (CHAMP),7 which requires 48 hours of instruction over 12 sessions. To create a less time‐intensive learning format for busy hospitalists, the University of Chicago developed Mini‐CHAMP, a streamlined 2‐day workshop with web‐based components for hospitalist clinicians, but not necessarily hospitalist educators.7

We created The Donald W. Reynolds Program for Advancing Geriatrics Education (PAGE) at the University of California, San Francisco (UCSF), in light of the time intensity of CHAMP, to integrate geriatric TTT sessions within preexisting hospitalist faculty meetings. This model is consistent with current practices in faculty development.8 This paper describes the evaluation of the PAGE Model, which sought answers to 3 research questions: (1) Does PAGE increase faculty confidence in teaching geriatrics?, (2) Does PAGE increase the frequency of hospitalist teaching geriatrics topics?, and (3) Does PAGE increase residents' practice of geriatrics skills?

Methods

Results

The hospitalist group grew from 31 to 36 members in June of 2008. On average, 14 hospitalists (M = 14.40, standard deviation [SD] = 2.41, range 1119) attended each session, with all hospitalists (n = 36) attending 1 session (M = 3.83, SD = 2.35, range 19). At each session, an average of 72% completed a post‐session evaluation form. Overall, faculty were likely to use the PAGE teaching tools (M = 4.61, SD = 0.53) and would recommend PAGE to other hospitalists (M = 4.63, SD = 0.51).

Thirteen hospitalist trainees of 36 (36%) completed a post‐PAGE online questionnaire. Respondents taught on faculty for an average of 5 years (mean (M) = 5.08, SD = 3.52). Faculty perceived self‐efficacy at teaching residents about geriatrics improved significantly with a large effect size (pretest M = 3.05, SD = .60; posttest M = 3.96, SD = .36, d = 1.52; P < 0.001). Session attendance was positively correlated with the increase in geriatrics teaching self‐efficacy (r = .62, P < 0.05), while teaching experience was not (r = 0.05, P = 0.88). Hospitalist trainees found the PAGE model more useful after participating (M = 4.62, SD = 0.65), than they had expected (M = 3.92, SD = 0.76; P < 0.05).

All session leaders (n = 15) completed the questionnaire after PAGE (9 hospitalists, 5 geriatricians, 1 urologist). Two‐thirds had 5 years on faculty; eight had no prior experience as a faculty development trainer. Over 80% indicated that they found their coteaching experience, enjoyable, useful and collaborative. Only 1 participant did not commit to interdisciplinary teaching again. Most hospitalist session leaders reported that coteaching with a geriatrician enhanced their knowledge; they were more likely to consult a geriatrician regarding patients. All but 2 session leaders felt that the model fostered a collaborative environment between their 2 divisions.

Of the 56 residents, 41% (16 PGY1, 7 PGY2) completed a pretest; 43% (15 PGY1, 9 PGY2) completed a posttest. Residents reported receiving inpatient teaching on geriatrics skills significantly more frequently from hospitalists vs. nonhospitalist attendings both before PAGE (hospitalists M = 2.18, SD = 0.37; nonhospitalists M = 2.00, SD = 0.53, P < 0.05), and after (hospitalists M = 2.39, SD = 0.46; nonhospitalists M = 2.05, SD = 0.57, P < 0.05; see Fig. 1). Although hospitalists taught more frequently about geriatrics than nonhospitalists before PAGE, our findings suggest that they increased their teaching by a greater magnitude than nonhospitalists (P < 0.01, P > 0.05, respectively). Residents reported increased geriatric skill practice after PAGE with a medium effect size (pretest M = 2.92, SD = 0.55, posttest M = 3.28, SD = 0.66, P = 0.052, d = 0.66). There was greater mean reported practice for all skills with the exception of hospice care, which already was being performed between often and very often before PAGE. The largest increases in skill practice were (descending order, most increased first): assessing polypharmacy, performing skin exams, prognostication, performing functional assessments and examining Foley catheter use.

Figure 1

Discussion

Our aging population and a shortage of geriatricians necessitates new, feasible models for geriatric training. Similar to the CHAMP model,7 PAGE had a favorable impact on faculty perceived behavioral change; after the PAGE sessions, faculty reported significantly greater self‐efficacy of teaching geriatrics. However, this study also examined the impact of the PAGE Model on 2 groups not previously reported in the literature: faculty session leaders and medicine residents.

To our knowledge, this is the first study about a hospitalist TTT program codeveloped with nonhospitalists aimed at teaching geriatrics skills to residents, though smaller scale programs for medical students exist.24 We believe codevelopment was important in our model for many reasons. First, using hospitalist peers and local geriatricians likely increased trust in the educational curricula and allowed for strong communication channels between instructors.25, 26 Second, coteaching allowed for hospitalist mentorship. Hospitalists acknowledged their coleaders as mentors and several hospitalists subsequently engaged in new geriatric projects. Third, coteaching was felt to enhance patient care and increase geriatrician consultations. Coteaching may have applicability to other hospitalist faculty development such as intensive care and palliative care, and hospitalist programs may benefit from creating faculty development programs internally with their colleagues, rather than using online resources.

Another important finding of this study is that training hospitalists to teach about geriatrics seems to result in an increase in both the geriatric teaching that residents receive and residents' practice of geriatric skills. This outcome has not been previously demonstrated with geriatric TTT activities.27 This trickle‐down effect to residents likely results from both the increased teaching efficacy of hospitalists after the PAGE Model and the exportable nature of the teaching tools.

Several continuing medical education best practices were used which we believe contributed to the success of PAGE. First, we conducted a needs assessment, which improves knowledge outcomes.28, 29 Second, sessions included cases, lectures, and discussions. Use of multiple educational techniques yields greater knowledge and behavioral change as compared to a single method, such as lecture alone.24, 25, 30, 31 Finally, sessions were sequenced over a year, rather than clustered in short, intensive activity. Sequenced, or learn‐work‐learn opportunities allow education to be translated to practice and reinforced.8, 27, 30, 32

We believe that the PAGE Model is transportable to other hospitalist programs due to its cost and flexible nature. In economically‐lean times, hospitalist divisions can create a program similar to the PAGE Model essentially at no cost, except for donated faculty preparation time. In contrast, CHAMP was expensive, costing nearly $72,000 for 12 faculty to participate in the 48‐hour curriculum,7, 33 and volunteering physicians were compensated for their time. Though Mini‐CHAMP is a streamlined 2‐day workshop that offers free online lectures and slide sets, there may be some benefit to producing a faculty development program internally, as we stated above, and PAGE included additional topics (urinary catheters and decubitus ulcers/wound care) not covered in mini‐CHAMP.

There were several limitations to this study. First, some outcomes of the PAGE Model were assessed by retrospective self‐report, which may allow for recall bias. Although self‐report may or may not correlate with actual behavior,34 faculty and resident perspectives of their teaching and learning experiences are themselves important. Furthermore, a retrospective presurvey allows for content of an educational program or intervention to be explained prior to a survey, so that participants first assess their new level of understanding or skill on the post test, then reflectively assess the level of understanding or skill they had prior to the workshop. This avoids response shift bias and can improve internal validity.21, 35

Second, the small numbers of session leaders, hospitalist trainees, and residents restricted statistical power to detect small effects. The fact that we found significant improvements enhances the likelihood that the differences observed were not due to chance.

Third, the low response rates from the hospitalist trainee post‐intervention questionnaire and the residents' questionnaires may affect the validity of our results. For the resident survey, the subjects were not matched, and we cannot state that an individual's geriatric skill practice changed due to PAGE, though the results suggest the residency program as a whole improved the frequency of geriatric skill practice.

Finally, the residents were required to report the frequency of teaching on and practice of geriatric skills practice over the prior year and accuracy of recall may be an issue. However, frequencies were queried both pre and post intervention and favorable change was noted. Furthermore, because the high end of the 3‐point teaching scale was limited to more than once, the true amount of teaching may have been underestimated if more than once actually represented high frequencies.

Future studies are needed to replicate these findings at other institutions to confirm generalizability. It would be beneficial to measure patient outcomes to determine whether increased teaching and skill practice benefits patients using measures such as reduction in catheter related urinary tract infections, falls, and inadequate pain management. Further investigations of cotaught faculty development programs between hospitalists and other specialists help emphasize why internally created TTT programs are of greater value than online resources.

Conclusions

This time‐sensitive adaptation of a hospitalist geriatric TTT program was successfully implemented at an academic medical center and suggests improved hospitalist faculty self‐efficacy at teaching geriatric skills, increased frequency of inpatient geriatric teaching by hospitalists and increased resident geriatric skill practice. Confidence to care for geriatric patients and a strong skill set to assess risks and manage them appropriately will equip hospitalists and trainees to provide care that reduces geriatric patients' in‐hospital morbidity and costs of care. As hospitalists increasingly care for older adults, the need for time‐efficient methods of teaching geriatrics will continue to grow. The PAGE Model, and other new models of geriatric training for hospitalists, demonstrates that we are beginning to address this urgent need.