Siddhartha Singh, MD, MS

METHODS

RESULTS

DISCUSSION

METHODS

RESULTS

DISCUSSION

METHODS

RESULTS

DISCUSSION

Readmissions of Medicare beneficiaries within 30 days of discharge are frequent and costly.[1] Concern about readmissions has prompted the Centers for Medicare & Medicaid Services (CMS) to reduce payments to hospitals with excess readmissions.[2] Research has identified a number of patient clinical and socio‐demographic factors associated with readmissions.[3] However, interventions designed to reduce readmissions have met with limited success. In a systematic review, no single intervention was regularly effective in reducing readmissions, despite the fact that interventions have targeted both predischarge, transition of care, and postdischarge processes of care.[4]

The different trajectories of care experienced by patients after hospital discharge, and their effect on risk of readmission, have been incompletely studied. Although early outpatient follow‐up after discharge is associated with lower readmission rates,[5, 6] a factor that has been minimally studied is the role of the emergency department (ED) and the ED provider in readmissions. The ED and ED providers feature prominently in the care received by patients shortly after discharge from a hospital. About a quarter of all hospitalized Medicare patients are evaluated in an ED within 30 days of discharge,[7, 8] and a majority of readmissions within 30 days of discharge are precipitated by an ED visit.[9] Hence, we asked whether when a recently discharged patient is seen in an ED, does the rate of readmission vary by ED provider and by ED facility?

We used Texas Medicare claims data to examine patients visiting the ED within 30 days of discharge from an initial hospitalization to determine if their risk of readmission varies by the ED provider caring for them and by the ED facility they visit.

METHODS

RESULTS

We identified 174,209 patients who visited an ED within 30 days of discharge from an initial hospitalization. Table 1 describes the characteristics of these patients as well as the readmission rates associated with these characteristics. The rate of readmission of our cohort of 1,627,705 discharges with or without a following ED visit was 16.2%, whereas the rate of readmission following an ED visit in our final cohort of 174,209 patients was 52.67%. This readmission rate increased with age, from 49.31% for patients between 66 and 70 years of age to 55.33% for patients older than 85 years. There were minor variations by gender and ethnicity. Patients residing in metropolitan areas or in zip codes with low education levels had higher readmission rates, as did those whose original admission was classified as emergency or those who were not discharged home.

Table 1 also presents the odds of readmission adjusted for all other factors in the table and also adjusted for clustering within ED providers in a 2‐level model. Increasing age, white race, metropolitan residence, nonhome discharge, higher severity of illness, more hospitalizations in the prior year, fewer physician visits in the prior year, and an emergency initial admission were each associated with a higher readmission rate.

We next generated estimates of readmission rates for each ED provider from the adjusted 2‐level models. Figure 1 shows the adjusted cumulative readmission rates for the 1922 ED providers. This figure shows the mean value and 95% confidence intervals of the readmission rates for each provider. Dark vertical lines indicate providers whose readmission rate differed significantly from the mean adjusted readmission rate of 52.1% for all providers. Of the ED providers, 14.2% had significantly higher readmission rates. The mean readmission rate for these 272 providers was 67.2%. Of the ED providers, 14.7% had significantly lower readmission rates. The mean readmission rate for these 283 providers was 36.8%.

Figure 1

To determine the contribution of the ED facility to the variation in readmission rates, we restricted our analysis to 48,883 patients (28.06% of our cohort) seen by 525 ED providers who were associated with only 1 facility (total of 143 facilities). Table 2 describes the unadjusted readmission rates stratified by specific characteristics of those facilities. The unadjusted readmission rate increased with the size of the associated hospital, from 47.61% for hospitals with less than 100 beds to 57.06% for hospitals with more than 400 beds. The readmission rate for nonprofit facilities was 53.81% and for for‐profit facilities was 57.39%. Facilities with no medical school affiliation had a readmission rate of 54.51%, whereas those with a major affiliation had a readmission rate of 58.72%.

With this smaller cohort, we performed 2 types of 2‐level models, where patients clustered within ED facilities and ER providers, respectively, and a 3‐level model accounting for clustering of patients within providers and of providers within facilities. From the facility‐patient 2‐level model, the variance of the ED facility was 0.2718 (95% confidence interval [CI]: 0.2083‐0.3696). From the provider‐patient 2‐level model, the variance of ED provider was 0.2532 (95% CI: 0.2166‐0.3002). However, when the 3‐level model was performed, the variance of ED provider decreased to 0.0893 (95% CI: 0.0723‐0.1132) and the variance of ED facility dropped to 0.2316 (95% CI: 0.1704‐0.3331) . This indicates 65% of the variation among ED providers was explained by the ED facility, and in contrast, 15% of the variation among ED facilities was explained by ED providers.

Table 2 also shows the adjusted odds of readmission generated from the 3‐level model. Patients receiving care in ED facilities in hospitals with more beds and in for‐profit hospitals were at higher risk for readmission. It is possible that patients seen at the ED associated with the discharging hospital had a lower risk of readmission. This finding was close to being statistically significant (P=0.051).

We repeated all the above analyses using an outcome of readmission anytime between the ED visit and 30 days after discharge from the initial hospitalization (rather than readmission on the day of or after the ED visit). All analyses produced results similar to the results presented above. For example, Figure 2 shows the adjusted cumulative readmission rates for the 1922 ED providers using this outcome. Of the ED providers, 12.8% had higher and 12.5% had lower readmission rates as compared to the mean readmission rate for all ED providers. The Spearman correlation coefficient between the rank of ED providers in immediate readmission rate (Figure 1) and readmission rate within 30 days of hospital discharge (Figure 2) was 0.94 (P<0.001).

Figure 2

DISCUSSION

This study found substantial variation in readmission rates by ED provider, despite controlling for patient clinical and sociodemographic factors. In 3‐level models, the ED facility explained a substantial part of the variation by ED provider, with patients seen at larger facilities and for‐profit facilities having higher readmission rates.

Variation among ED facilities and ED providers in readmission rates has not previously been studied. There is literature on the variation in ED facility and ED provider admission rates. As readmissions are a subset of all admissions, this literature provides context to our findings. Abualenain et al. examined admission rates for 89 ED physicians for adult patients presenting with an acute medical or surgical complaint at 3 EDs in a health system.[12] After adjusting for patient and clinical characteristics, admission rates varied from 21% to 49% among physicians and from 27% to 41% among 3 facilities. Two other studies from single hospitals have found similar variation among providers.[13, 14] The reasons for the variation among ED providers presumably relate to subjective aspects of clinical assessment and the reluctance of providers to rely solely on objective scales, even when they are available.[14, 15] Variation in admission rates among different facilities may relate to clustering of providers with similar practice styles within facilities, lack of clinical guidelines for certain conditions, as well as differences among facilities in the socioeconomic status and access to primary care of their clientele.[12, 16, 17] For example, Pines et al. have shown that ED facility admission rates are higher in communities with fewer primary care physicians per capita and are influenced by the prevailing county level admission rates.[16] Capp et al. showed persistent variation in admission rates across hospitals, despite adjusting for clinical criteria such as vital signs, chief complaints, and severity of illness.[18]

Structural differences in ED facilities may also influence the decision to admit. We found that patients visiting ED facilities in hospitals with more beds had a higher readmission rate. ED facility systems of care such as observation units or protocols are associated with lower admission rates.[19, 20] Finally, certain hospitals may actively influence the admission practice patterns of their ED providers. We noted that patients seen at for‐profit ED facilities had a greater risk of readmission. A similar finding has been described by Pines et al., who noted higher admission rates at for‐profit facilities.[16] In an extreme example, a recent Justice Department lawsuit alleged that a for‐profit hospital chain used software systems and financial incentives to ED providers to increase admissions.[21]

It is possible that the providers with low readmission rates may have inappropriately released patients who truly should have been admitted. A signal that this occurred would be if these patients were readmitted in the days after the ED visits. We examined this possibility by additionally examining readmissions occurring anytime between the ED visit until 30 days after discharge from the initial hospitalization. The results were similar to when we only included readmissions that occurred immediately following the ED visit, with a very high correlation (r=0.94) between the ranking of the ED providers by readmission rates in both circumstances. This suggests that the decisions of the ED providers with low readmission rates to admit or release from the ED were likely appropriate.

Our research has limitations. We studied patients with fee‐for‐service Medicare in a single large state in the United States over a 4‐year period. Our findings may not be generalizable to younger patient populations, other regions with different sociodemographic patterns and healthcare systems, or other time periods. We could not control for many factors that may impact the risk of readmission but are not measured in Medicare databases (eg, clinical data such as vital signs, measures of quality of transition from discharging hospital, ED provider workload). To attribute care to a single ED provider, we excluded patients who were taken care of by multiple ED providers. These patients may have different needs from our study population (eg, more complex issues and longer stays in the ED) and may bias our results.

This study provides a new direction for research and quality improvement targeting readmissions. Research should extend beyond the discharge transition and examine the entire trajectory of posthospitalization care to better understand readmissions. Based directly on this study, research could investigate the practice patterns of ED providers and systems of care at ED facilities that affect readmissions rates. Such investigation could inform quality improvement efforts to standardize care for patients in the ED.

CMS policies hold hospitals accountable for readmissions of the patients they discharge, but do not address the admission process in the ED that leads to readmissions of recently discharged patients. Given the present study, and the fact that the proportion of all hospital admissions that occur through the ED has grown to 44%,[22] consideration of the role of the ED in public policy efforts to discourage unnecessary inpatient care may be appropriate.

In summary, this study shows that a recently discharged patient's chances of being readmitted depends partly on the ED provider who evaluates them and on the ED facility at which they seek care. ED provider practice patterns and ED facility systems of care may be a target for interventions aimed at decreasing readmission rates.

Readmissions of Medicare beneficiaries within 30 days of discharge are frequent and costly.[1] Concern about readmissions has prompted the Centers for Medicare & Medicaid Services (CMS) to reduce payments to hospitals with excess readmissions.[2] Research has identified a number of patient clinical and socio‐demographic factors associated with readmissions.[3] However, interventions designed to reduce readmissions have met with limited success. In a systematic review, no single intervention was regularly effective in reducing readmissions, despite the fact that interventions have targeted both predischarge, transition of care, and postdischarge processes of care.[4]

The different trajectories of care experienced by patients after hospital discharge, and their effect on risk of readmission, have been incompletely studied. Although early outpatient follow‐up after discharge is associated with lower readmission rates,[5, 6] a factor that has been minimally studied is the role of the emergency department (ED) and the ED provider in readmissions. The ED and ED providers feature prominently in the care received by patients shortly after discharge from a hospital. About a quarter of all hospitalized Medicare patients are evaluated in an ED within 30 days of discharge,[7, 8] and a majority of readmissions within 30 days of discharge are precipitated by an ED visit.[9] Hence, we asked whether when a recently discharged patient is seen in an ED, does the rate of readmission vary by ED provider and by ED facility?

We used Texas Medicare claims data to examine patients visiting the ED within 30 days of discharge from an initial hospitalization to determine if their risk of readmission varies by the ED provider caring for them and by the ED facility they visit.

METHODS

RESULTS

We identified 174,209 patients who visited an ED within 30 days of discharge from an initial hospitalization. Table 1 describes the characteristics of these patients as well as the readmission rates associated with these characteristics. The rate of readmission of our cohort of 1,627,705 discharges with or without a following ED visit was 16.2%, whereas the rate of readmission following an ED visit in our final cohort of 174,209 patients was 52.67%. This readmission rate increased with age, from 49.31% for patients between 66 and 70 years of age to 55.33% for patients older than 85 years. There were minor variations by gender and ethnicity. Patients residing in metropolitan areas or in zip codes with low education levels had higher readmission rates, as did those whose original admission was classified as emergency or those who were not discharged home.

Table 1 also presents the odds of readmission adjusted for all other factors in the table and also adjusted for clustering within ED providers in a 2‐level model. Increasing age, white race, metropolitan residence, nonhome discharge, higher severity of illness, more hospitalizations in the prior year, fewer physician visits in the prior year, and an emergency initial admission were each associated with a higher readmission rate.

We next generated estimates of readmission rates for each ED provider from the adjusted 2‐level models. Figure 1 shows the adjusted cumulative readmission rates for the 1922 ED providers. This figure shows the mean value and 95% confidence intervals of the readmission rates for each provider. Dark vertical lines indicate providers whose readmission rate differed significantly from the mean adjusted readmission rate of 52.1% for all providers. Of the ED providers, 14.2% had significantly higher readmission rates. The mean readmission rate for these 272 providers was 67.2%. Of the ED providers, 14.7% had significantly lower readmission rates. The mean readmission rate for these 283 providers was 36.8%.

Figure 1

To determine the contribution of the ED facility to the variation in readmission rates, we restricted our analysis to 48,883 patients (28.06% of our cohort) seen by 525 ED providers who were associated with only 1 facility (total of 143 facilities). Table 2 describes the unadjusted readmission rates stratified by specific characteristics of those facilities. The unadjusted readmission rate increased with the size of the associated hospital, from 47.61% for hospitals with less than 100 beds to 57.06% for hospitals with more than 400 beds. The readmission rate for nonprofit facilities was 53.81% and for for‐profit facilities was 57.39%. Facilities with no medical school affiliation had a readmission rate of 54.51%, whereas those with a major affiliation had a readmission rate of 58.72%.

With this smaller cohort, we performed 2 types of 2‐level models, where patients clustered within ED facilities and ER providers, respectively, and a 3‐level model accounting for clustering of patients within providers and of providers within facilities. From the facility‐patient 2‐level model, the variance of the ED facility was 0.2718 (95% confidence interval [CI]: 0.2083‐0.3696). From the provider‐patient 2‐level model, the variance of ED provider was 0.2532 (95% CI: 0.2166‐0.3002). However, when the 3‐level model was performed, the variance of ED provider decreased to 0.0893 (95% CI: 0.0723‐0.1132) and the variance of ED facility dropped to 0.2316 (95% CI: 0.1704‐0.3331) . This indicates 65% of the variation among ED providers was explained by the ED facility, and in contrast, 15% of the variation among ED facilities was explained by ED providers.

Table 2 also shows the adjusted odds of readmission generated from the 3‐level model. Patients receiving care in ED facilities in hospitals with more beds and in for‐profit hospitals were at higher risk for readmission. It is possible that patients seen at the ED associated with the discharging hospital had a lower risk of readmission. This finding was close to being statistically significant (P=0.051).

We repeated all the above analyses using an outcome of readmission anytime between the ED visit and 30 days after discharge from the initial hospitalization (rather than readmission on the day of or after the ED visit). All analyses produced results similar to the results presented above. For example, Figure 2 shows the adjusted cumulative readmission rates for the 1922 ED providers using this outcome. Of the ED providers, 12.8% had higher and 12.5% had lower readmission rates as compared to the mean readmission rate for all ED providers. The Spearman correlation coefficient between the rank of ED providers in immediate readmission rate (Figure 1) and readmission rate within 30 days of hospital discharge (Figure 2) was 0.94 (P<0.001).

Figure 2

DISCUSSION

This study found substantial variation in readmission rates by ED provider, despite controlling for patient clinical and sociodemographic factors. In 3‐level models, the ED facility explained a substantial part of the variation by ED provider, with patients seen at larger facilities and for‐profit facilities having higher readmission rates.

Variation among ED facilities and ED providers in readmission rates has not previously been studied. There is literature on the variation in ED facility and ED provider admission rates. As readmissions are a subset of all admissions, this literature provides context to our findings. Abualenain et al. examined admission rates for 89 ED physicians for adult patients presenting with an acute medical or surgical complaint at 3 EDs in a health system.[12] After adjusting for patient and clinical characteristics, admission rates varied from 21% to 49% among physicians and from 27% to 41% among 3 facilities. Two other studies from single hospitals have found similar variation among providers.[13, 14] The reasons for the variation among ED providers presumably relate to subjective aspects of clinical assessment and the reluctance of providers to rely solely on objective scales, even when they are available.[14, 15] Variation in admission rates among different facilities may relate to clustering of providers with similar practice styles within facilities, lack of clinical guidelines for certain conditions, as well as differences among facilities in the socioeconomic status and access to primary care of their clientele.[12, 16, 17] For example, Pines et al. have shown that ED facility admission rates are higher in communities with fewer primary care physicians per capita and are influenced by the prevailing county level admission rates.[16] Capp et al. showed persistent variation in admission rates across hospitals, despite adjusting for clinical criteria such as vital signs, chief complaints, and severity of illness.[18]

Structural differences in ED facilities may also influence the decision to admit. We found that patients visiting ED facilities in hospitals with more beds had a higher readmission rate. ED facility systems of care such as observation units or protocols are associated with lower admission rates.[19, 20] Finally, certain hospitals may actively influence the admission practice patterns of their ED providers. We noted that patients seen at for‐profit ED facilities had a greater risk of readmission. A similar finding has been described by Pines et al., who noted higher admission rates at for‐profit facilities.[16] In an extreme example, a recent Justice Department lawsuit alleged that a for‐profit hospital chain used software systems and financial incentives to ED providers to increase admissions.[21]

It is possible that the providers with low readmission rates may have inappropriately released patients who truly should have been admitted. A signal that this occurred would be if these patients were readmitted in the days after the ED visits. We examined this possibility by additionally examining readmissions occurring anytime between the ED visit until 30 days after discharge from the initial hospitalization. The results were similar to when we only included readmissions that occurred immediately following the ED visit, with a very high correlation (r=0.94) between the ranking of the ED providers by readmission rates in both circumstances. This suggests that the decisions of the ED providers with low readmission rates to admit or release from the ED were likely appropriate.

Our research has limitations. We studied patients with fee‐for‐service Medicare in a single large state in the United States over a 4‐year period. Our findings may not be generalizable to younger patient populations, other regions with different sociodemographic patterns and healthcare systems, or other time periods. We could not control for many factors that may impact the risk of readmission but are not measured in Medicare databases (eg, clinical data such as vital signs, measures of quality of transition from discharging hospital, ED provider workload). To attribute care to a single ED provider, we excluded patients who were taken care of by multiple ED providers. These patients may have different needs from our study population (eg, more complex issues and longer stays in the ED) and may bias our results.

This study provides a new direction for research and quality improvement targeting readmissions. Research should extend beyond the discharge transition and examine the entire trajectory of posthospitalization care to better understand readmissions. Based directly on this study, research could investigate the practice patterns of ED providers and systems of care at ED facilities that affect readmissions rates. Such investigation could inform quality improvement efforts to standardize care for patients in the ED.

CMS policies hold hospitals accountable for readmissions of the patients they discharge, but do not address the admission process in the ED that leads to readmissions of recently discharged patients. Given the present study, and the fact that the proportion of all hospital admissions that occur through the ED has grown to 44%,[22] consideration of the role of the ED in public policy efforts to discourage unnecessary inpatient care may be appropriate.

In summary, this study shows that a recently discharged patient's chances of being readmitted depends partly on the ED provider who evaluates them and on the ED facility at which they seek care. ED provider practice patterns and ED facility systems of care may be a target for interventions aimed at decreasing readmission rates.

Readmissions of Medicare beneficiaries within 30 days of discharge are frequent and costly.[1] Concern about readmissions has prompted the Centers for Medicare & Medicaid Services (CMS) to reduce payments to hospitals with excess readmissions.[2] Research has identified a number of patient clinical and socio‐demographic factors associated with readmissions.[3] However, interventions designed to reduce readmissions have met with limited success. In a systematic review, no single intervention was regularly effective in reducing readmissions, despite the fact that interventions have targeted both predischarge, transition of care, and postdischarge processes of care.[4]

The different trajectories of care experienced by patients after hospital discharge, and their effect on risk of readmission, have been incompletely studied. Although early outpatient follow‐up after discharge is associated with lower readmission rates,[5, 6] a factor that has been minimally studied is the role of the emergency department (ED) and the ED provider in readmissions. The ED and ED providers feature prominently in the care received by patients shortly after discharge from a hospital. About a quarter of all hospitalized Medicare patients are evaluated in an ED within 30 days of discharge,[7, 8] and a majority of readmissions within 30 days of discharge are precipitated by an ED visit.[9] Hence, we asked whether when a recently discharged patient is seen in an ED, does the rate of readmission vary by ED provider and by ED facility?

We used Texas Medicare claims data to examine patients visiting the ED within 30 days of discharge from an initial hospitalization to determine if their risk of readmission varies by the ED provider caring for them and by the ED facility they visit.

METHODS

RESULTS

We identified 174,209 patients who visited an ED within 30 days of discharge from an initial hospitalization. Table 1 describes the characteristics of these patients as well as the readmission rates associated with these characteristics. The rate of readmission of our cohort of 1,627,705 discharges with or without a following ED visit was 16.2%, whereas the rate of readmission following an ED visit in our final cohort of 174,209 patients was 52.67%. This readmission rate increased with age, from 49.31% for patients between 66 and 70 years of age to 55.33% for patients older than 85 years. There were minor variations by gender and ethnicity. Patients residing in metropolitan areas or in zip codes with low education levels had higher readmission rates, as did those whose original admission was classified as emergency or those who were not discharged home.

Table 1 also presents the odds of readmission adjusted for all other factors in the table and also adjusted for clustering within ED providers in a 2‐level model. Increasing age, white race, metropolitan residence, nonhome discharge, higher severity of illness, more hospitalizations in the prior year, fewer physician visits in the prior year, and an emergency initial admission were each associated with a higher readmission rate.

We next generated estimates of readmission rates for each ED provider from the adjusted 2‐level models. Figure 1 shows the adjusted cumulative readmission rates for the 1922 ED providers. This figure shows the mean value and 95% confidence intervals of the readmission rates for each provider. Dark vertical lines indicate providers whose readmission rate differed significantly from the mean adjusted readmission rate of 52.1% for all providers. Of the ED providers, 14.2% had significantly higher readmission rates. The mean readmission rate for these 272 providers was 67.2%. Of the ED providers, 14.7% had significantly lower readmission rates. The mean readmission rate for these 283 providers was 36.8%.

Figure 1

To determine the contribution of the ED facility to the variation in readmission rates, we restricted our analysis to 48,883 patients (28.06% of our cohort) seen by 525 ED providers who were associated with only 1 facility (total of 143 facilities). Table 2 describes the unadjusted readmission rates stratified by specific characteristics of those facilities. The unadjusted readmission rate increased with the size of the associated hospital, from 47.61% for hospitals with less than 100 beds to 57.06% for hospitals with more than 400 beds. The readmission rate for nonprofit facilities was 53.81% and for for‐profit facilities was 57.39%. Facilities with no medical school affiliation had a readmission rate of 54.51%, whereas those with a major affiliation had a readmission rate of 58.72%.

With this smaller cohort, we performed 2 types of 2‐level models, where patients clustered within ED facilities and ER providers, respectively, and a 3‐level model accounting for clustering of patients within providers and of providers within facilities. From the facility‐patient 2‐level model, the variance of the ED facility was 0.2718 (95% confidence interval [CI]: 0.2083‐0.3696). From the provider‐patient 2‐level model, the variance of ED provider was 0.2532 (95% CI: 0.2166‐0.3002). However, when the 3‐level model was performed, the variance of ED provider decreased to 0.0893 (95% CI: 0.0723‐0.1132) and the variance of ED facility dropped to 0.2316 (95% CI: 0.1704‐0.3331) . This indicates 65% of the variation among ED providers was explained by the ED facility, and in contrast, 15% of the variation among ED facilities was explained by ED providers.

Table 2 also shows the adjusted odds of readmission generated from the 3‐level model. Patients receiving care in ED facilities in hospitals with more beds and in for‐profit hospitals were at higher risk for readmission. It is possible that patients seen at the ED associated with the discharging hospital had a lower risk of readmission. This finding was close to being statistically significant (P=0.051).

We repeated all the above analyses using an outcome of readmission anytime between the ED visit and 30 days after discharge from the initial hospitalization (rather than readmission on the day of or after the ED visit). All analyses produced results similar to the results presented above. For example, Figure 2 shows the adjusted cumulative readmission rates for the 1922 ED providers using this outcome. Of the ED providers, 12.8% had higher and 12.5% had lower readmission rates as compared to the mean readmission rate for all ED providers. The Spearman correlation coefficient between the rank of ED providers in immediate readmission rate (Figure 1) and readmission rate within 30 days of hospital discharge (Figure 2) was 0.94 (P<0.001).

Figure 2

DISCUSSION

This study found substantial variation in readmission rates by ED provider, despite controlling for patient clinical and sociodemographic factors. In 3‐level models, the ED facility explained a substantial part of the variation by ED provider, with patients seen at larger facilities and for‐profit facilities having higher readmission rates.

Variation among ED facilities and ED providers in readmission rates has not previously been studied. There is literature on the variation in ED facility and ED provider admission rates. As readmissions are a subset of all admissions, this literature provides context to our findings. Abualenain et al. examined admission rates for 89 ED physicians for adult patients presenting with an acute medical or surgical complaint at 3 EDs in a health system.[12] After adjusting for patient and clinical characteristics, admission rates varied from 21% to 49% among physicians and from 27% to 41% among 3 facilities. Two other studies from single hospitals have found similar variation among providers.[13, 14] The reasons for the variation among ED providers presumably relate to subjective aspects of clinical assessment and the reluctance of providers to rely solely on objective scales, even when they are available.[14, 15] Variation in admission rates among different facilities may relate to clustering of providers with similar practice styles within facilities, lack of clinical guidelines for certain conditions, as well as differences among facilities in the socioeconomic status and access to primary care of their clientele.[12, 16, 17] For example, Pines et al. have shown that ED facility admission rates are higher in communities with fewer primary care physicians per capita and are influenced by the prevailing county level admission rates.[16] Capp et al. showed persistent variation in admission rates across hospitals, despite adjusting for clinical criteria such as vital signs, chief complaints, and severity of illness.[18]

Structural differences in ED facilities may also influence the decision to admit. We found that patients visiting ED facilities in hospitals with more beds had a higher readmission rate. ED facility systems of care such as observation units or protocols are associated with lower admission rates.[19, 20] Finally, certain hospitals may actively influence the admission practice patterns of their ED providers. We noted that patients seen at for‐profit ED facilities had a greater risk of readmission. A similar finding has been described by Pines et al., who noted higher admission rates at for‐profit facilities.[16] In an extreme example, a recent Justice Department lawsuit alleged that a for‐profit hospital chain used software systems and financial incentives to ED providers to increase admissions.[21]

It is possible that the providers with low readmission rates may have inappropriately released patients who truly should have been admitted. A signal that this occurred would be if these patients were readmitted in the days after the ED visits. We examined this possibility by additionally examining readmissions occurring anytime between the ED visit until 30 days after discharge from the initial hospitalization. The results were similar to when we only included readmissions that occurred immediately following the ED visit, with a very high correlation (r=0.94) between the ranking of the ED providers by readmission rates in both circumstances. This suggests that the decisions of the ED providers with low readmission rates to admit or release from the ED were likely appropriate.

Our research has limitations. We studied patients with fee‐for‐service Medicare in a single large state in the United States over a 4‐year period. Our findings may not be generalizable to younger patient populations, other regions with different sociodemographic patterns and healthcare systems, or other time periods. We could not control for many factors that may impact the risk of readmission but are not measured in Medicare databases (eg, clinical data such as vital signs, measures of quality of transition from discharging hospital, ED provider workload). To attribute care to a single ED provider, we excluded patients who were taken care of by multiple ED providers. These patients may have different needs from our study population (eg, more complex issues and longer stays in the ED) and may bias our results.

This study provides a new direction for research and quality improvement targeting readmissions. Research should extend beyond the discharge transition and examine the entire trajectory of posthospitalization care to better understand readmissions. Based directly on this study, research could investigate the practice patterns of ED providers and systems of care at ED facilities that affect readmissions rates. Such investigation could inform quality improvement efforts to standardize care for patients in the ED.

CMS policies hold hospitals accountable for readmissions of the patients they discharge, but do not address the admission process in the ED that leads to readmissions of recently discharged patients. Given the present study, and the fact that the proportion of all hospital admissions that occur through the ED has grown to 44%,[22] consideration of the role of the ED in public policy efforts to discourage unnecessary inpatient care may be appropriate.

In summary, this study shows that a recently discharged patient's chances of being readmitted depends partly on the ED provider who evaluates them and on the ED facility at which they seek care. ED provider practice patterns and ED facility systems of care may be a target for interventions aimed at decreasing readmission rates.

Localizing inpatient general medical teams to nursing units has high intuitive validity for improving physician productivity, hospital efficiency, and patient outcomes. Motion or the moving of personnel between tasksso prominent if teams are not localizedis 1 of the 7 wastes in lean thinking.1 In a timemotion study, where hospitalists cared for patients on up to 5 different wards, O'Leary et al2 have reported large parts of hospitalists' workdays spent in indirect patient care (69%), paging (13%), and travel (3%). Localization could increase the amount of time available for direct patient care, decrease time spent for (and interruptions due to) paging, and decrease travel time, all leading to greater productivity.

O'Leary et al3 have also reported the beneficial effects of localization of medical inpatients on communication between nurses and physicians, who could identify each other more often, and reported greater communication (specifically face‐to‐face communication) with each other following localization. This improvement in communication and effective multidisciplinary rounds could lead to safer care4 and better outcomes.

Further investigations about the effect of localization are limited. Roy et al5 have compared the outcomes of patients localized to 2 inpatient pods medically staffed by hospitalists and physician assistants (PAs) to geographically dispersed, but structurally different, house staff teams. They noticed significantly lower costs, slight but nonsignificant increase in length of stay, and no difference in mortality or readmissions, but it is impossible to tease out the affect of localization versus the affect of team composition. In a before‐and‐after study, Findlay et al6 have reported a decrease in mortality and complication rates in clinically homogenous surgical patients (proximal hip fractures) when cared for by junior trainee physicians localized to a unit, but their experience cannot be extrapolated to the much more diverse general medical population.

In our hospital, each general medical team could admit patients dispersed over 14 different units. An internal group, commissioned to evaluate our hospitalist practice, recommended reducing this dispersal to improve physician productivity, hospital efficiency, and outcomes of care. We therefore conducted a project to evaluate the impact of localizing general medical inpatient teams to a single nursing unit.

METHODS

RESULTS

DISCUSSION

We found that general medical patients admitted to H‐PA teams and localized to a single nursing unit had similar risk of 30‐day readmission and charges, but may have had a higher length of stay compared to historical and concurrent controls. The localized teams received far fewer pages, had more patient encounters, generated more RVUs, and walked less during their work day. Taken together, these findings imply that in our study, localization led to greater team productivity and a possible decrease in hospital efficiency, with no significant impact on readmissions or charges incurred.

The higher productivity was likely mediated by the preferential assignments of more patients to the localized teams, and improvements in workflow (such as fewer pages and fewer steps walked), which allowed them to provide more care with the same resources as the control teams. Kocher and Sahni7 recently pointed out that the healthcare sector has experienced no gains in labor productivity in the past 20 years. Our intervention fits their prescription for redesigning healthcare delivery models to achieve higher productivity.

The possibility of a higher LOS associated with localization was a counterintuitive finding, and similar to that reported by Roy et al.5 We propose 3 hypotheses to explain this:

• 1

  Selection bias: Higher workload of the localized teams led to compromised efficiency and a higher length of stay (eg, localized teams had fewer observation admissions, more hospitalizations with an ICU stay, and the AMO was asked to preferentially admit patients to localized teams).

• 2

  Localization provided teams the opportunity to spend more time with their patients (by decreasing nonvalue‐added tasks) and to consequently address more issues before transitioning to outpatient care, or to provide higher quality of care.

• 3

  Gaming: By having a hard cap on total number of occupied beds, we provided a perverse incentive to the localized teams to retain patients longer to keep assigned beds occupied, thereby delaying new admissions to avoid higher workload.

Our study cannot tell us which of these hypotheses represents the dominant phenomenon that led to this surprising finding. Hypothesis 3 is most worrying, and we suggest that others looking to localize their medical teams consider the possibility of unintended perverse incentives.

Differences were more pronounced between the historical control group and the intervention group, as opposed to the intervention group and concurrent controls. This may have occurred if we contaminated the concurrent control by decreasing the number of units they had to go to, by sequestering 1 unit for the intervention team.

Our report has limitations. It is a nonrandomized, quasi‐experimental investigation using a single institution's administrative databases. Our intervention was small in scale (localizing 2 out of 10 general medical teams on 1 out of 14 nursing units). What impact a wider implementation of localization may have on emergency department throughput and hospital occupancy remains to be studied. Nevertheless, our research is the first report, to our knowledge, investigating a wide variety of outcomes of localizing inpatient medical teams, and adds significantly to the limited research on this topic. It also provides significant operational details for other institutions to use when localizing medical teams.

We conclude that our intervention of localization of medical teams to a single nursing unit led to higher productivity and better workflow, but did not impact readmissions or charges incurred. We caution others designing similar localization interventions to protect against possible perverse incentives for inefficient care.

Localizing inpatient general medical teams to nursing units has high intuitive validity for improving physician productivity, hospital efficiency, and patient outcomes. Motion or the moving of personnel between tasksso prominent if teams are not localizedis 1 of the 7 wastes in lean thinking.1 In a timemotion study, where hospitalists cared for patients on up to 5 different wards, O'Leary et al2 have reported large parts of hospitalists' workdays spent in indirect patient care (69%), paging (13%), and travel (3%). Localization could increase the amount of time available for direct patient care, decrease time spent for (and interruptions due to) paging, and decrease travel time, all leading to greater productivity.

O'Leary et al3 have also reported the beneficial effects of localization of medical inpatients on communication between nurses and physicians, who could identify each other more often, and reported greater communication (specifically face‐to‐face communication) with each other following localization. This improvement in communication and effective multidisciplinary rounds could lead to safer care4 and better outcomes.

Further investigations about the effect of localization are limited. Roy et al5 have compared the outcomes of patients localized to 2 inpatient pods medically staffed by hospitalists and physician assistants (PAs) to geographically dispersed, but structurally different, house staff teams. They noticed significantly lower costs, slight but nonsignificant increase in length of stay, and no difference in mortality or readmissions, but it is impossible to tease out the affect of localization versus the affect of team composition. In a before‐and‐after study, Findlay et al6 have reported a decrease in mortality and complication rates in clinically homogenous surgical patients (proximal hip fractures) when cared for by junior trainee physicians localized to a unit, but their experience cannot be extrapolated to the much more diverse general medical population.

In our hospital, each general medical team could admit patients dispersed over 14 different units. An internal group, commissioned to evaluate our hospitalist practice, recommended reducing this dispersal to improve physician productivity, hospital efficiency, and outcomes of care. We therefore conducted a project to evaluate the impact of localizing general medical inpatient teams to a single nursing unit.

METHODS

RESULTS

DISCUSSION

We found that general medical patients admitted to H‐PA teams and localized to a single nursing unit had similar risk of 30‐day readmission and charges, but may have had a higher length of stay compared to historical and concurrent controls. The localized teams received far fewer pages, had more patient encounters, generated more RVUs, and walked less during their work day. Taken together, these findings imply that in our study, localization led to greater team productivity and a possible decrease in hospital efficiency, with no significant impact on readmissions or charges incurred.

The higher productivity was likely mediated by the preferential assignments of more patients to the localized teams, and improvements in workflow (such as fewer pages and fewer steps walked), which allowed them to provide more care with the same resources as the control teams. Kocher and Sahni7 recently pointed out that the healthcare sector has experienced no gains in labor productivity in the past 20 years. Our intervention fits their prescription for redesigning healthcare delivery models to achieve higher productivity.

The possibility of a higher LOS associated with localization was a counterintuitive finding, and similar to that reported by Roy et al.5 We propose 3 hypotheses to explain this:

• 1

  Selection bias: Higher workload of the localized teams led to compromised efficiency and a higher length of stay (eg, localized teams had fewer observation admissions, more hospitalizations with an ICU stay, and the AMO was asked to preferentially admit patients to localized teams).

• 2

  Localization provided teams the opportunity to spend more time with their patients (by decreasing nonvalue‐added tasks) and to consequently address more issues before transitioning to outpatient care, or to provide higher quality of care.

• 3

  Gaming: By having a hard cap on total number of occupied beds, we provided a perverse incentive to the localized teams to retain patients longer to keep assigned beds occupied, thereby delaying new admissions to avoid higher workload.

Our study cannot tell us which of these hypotheses represents the dominant phenomenon that led to this surprising finding. Hypothesis 3 is most worrying, and we suggest that others looking to localize their medical teams consider the possibility of unintended perverse incentives.

Differences were more pronounced between the historical control group and the intervention group, as opposed to the intervention group and concurrent controls. This may have occurred if we contaminated the concurrent control by decreasing the number of units they had to go to, by sequestering 1 unit for the intervention team.

Our report has limitations. It is a nonrandomized, quasi‐experimental investigation using a single institution's administrative databases. Our intervention was small in scale (localizing 2 out of 10 general medical teams on 1 out of 14 nursing units). What impact a wider implementation of localization may have on emergency department throughput and hospital occupancy remains to be studied. Nevertheless, our research is the first report, to our knowledge, investigating a wide variety of outcomes of localizing inpatient medical teams, and adds significantly to the limited research on this topic. It also provides significant operational details for other institutions to use when localizing medical teams.

We conclude that our intervention of localization of medical teams to a single nursing unit led to higher productivity and better workflow, but did not impact readmissions or charges incurred. We caution others designing similar localization interventions to protect against possible perverse incentives for inefficient care.

Localizing inpatient general medical teams to nursing units has high intuitive validity for improving physician productivity, hospital efficiency, and patient outcomes. Motion or the moving of personnel between tasksso prominent if teams are not localizedis 1 of the 7 wastes in lean thinking.1 In a timemotion study, where hospitalists cared for patients on up to 5 different wards, O'Leary et al2 have reported large parts of hospitalists' workdays spent in indirect patient care (69%), paging (13%), and travel (3%). Localization could increase the amount of time available for direct patient care, decrease time spent for (and interruptions due to) paging, and decrease travel time, all leading to greater productivity.

O'Leary et al3 have also reported the beneficial effects of localization of medical inpatients on communication between nurses and physicians, who could identify each other more often, and reported greater communication (specifically face‐to‐face communication) with each other following localization. This improvement in communication and effective multidisciplinary rounds could lead to safer care4 and better outcomes.

Further investigations about the effect of localization are limited. Roy et al5 have compared the outcomes of patients localized to 2 inpatient pods medically staffed by hospitalists and physician assistants (PAs) to geographically dispersed, but structurally different, house staff teams. They noticed significantly lower costs, slight but nonsignificant increase in length of stay, and no difference in mortality or readmissions, but it is impossible to tease out the affect of localization versus the affect of team composition. In a before‐and‐after study, Findlay et al6 have reported a decrease in mortality and complication rates in clinically homogenous surgical patients (proximal hip fractures) when cared for by junior trainee physicians localized to a unit, but their experience cannot be extrapolated to the much more diverse general medical population.

In our hospital, each general medical team could admit patients dispersed over 14 different units. An internal group, commissioned to evaluate our hospitalist practice, recommended reducing this dispersal to improve physician productivity, hospital efficiency, and outcomes of care. We therefore conducted a project to evaluate the impact of localizing general medical inpatient teams to a single nursing unit.

METHODS

RESULTS

DISCUSSION

We found that general medical patients admitted to H‐PA teams and localized to a single nursing unit had similar risk of 30‐day readmission and charges, but may have had a higher length of stay compared to historical and concurrent controls. The localized teams received far fewer pages, had more patient encounters, generated more RVUs, and walked less during their work day. Taken together, these findings imply that in our study, localization led to greater team productivity and a possible decrease in hospital efficiency, with no significant impact on readmissions or charges incurred.

The higher productivity was likely mediated by the preferential assignments of more patients to the localized teams, and improvements in workflow (such as fewer pages and fewer steps walked), which allowed them to provide more care with the same resources as the control teams. Kocher and Sahni7 recently pointed out that the healthcare sector has experienced no gains in labor productivity in the past 20 years. Our intervention fits their prescription for redesigning healthcare delivery models to achieve higher productivity.

The possibility of a higher LOS associated with localization was a counterintuitive finding, and similar to that reported by Roy et al.5 We propose 3 hypotheses to explain this:

• 1

  Selection bias: Higher workload of the localized teams led to compromised efficiency and a higher length of stay (eg, localized teams had fewer observation admissions, more hospitalizations with an ICU stay, and the AMO was asked to preferentially admit patients to localized teams).

• 2

  Localization provided teams the opportunity to spend more time with their patients (by decreasing nonvalue‐added tasks) and to consequently address more issues before transitioning to outpatient care, or to provide higher quality of care.

• 3

  Gaming: By having a hard cap on total number of occupied beds, we provided a perverse incentive to the localized teams to retain patients longer to keep assigned beds occupied, thereby delaying new admissions to avoid higher workload.

Our study cannot tell us which of these hypotheses represents the dominant phenomenon that led to this surprising finding. Hypothesis 3 is most worrying, and we suggest that others looking to localize their medical teams consider the possibility of unintended perverse incentives.

Differences were more pronounced between the historical control group and the intervention group, as opposed to the intervention group and concurrent controls. This may have occurred if we contaminated the concurrent control by decreasing the number of units they had to go to, by sequestering 1 unit for the intervention team.

Our report has limitations. It is a nonrandomized, quasi‐experimental investigation using a single institution's administrative databases. Our intervention was small in scale (localizing 2 out of 10 general medical teams on 1 out of 14 nursing units). What impact a wider implementation of localization may have on emergency department throughput and hospital occupancy remains to be studied. Nevertheless, our research is the first report, to our knowledge, investigating a wide variety of outcomes of localizing inpatient medical teams, and adds significantly to the limited research on this topic. It also provides significant operational details for other institutions to use when localizing medical teams.

We conclude that our intervention of localization of medical teams to a single nursing unit led to higher productivity and better workflow, but did not impact readmissions or charges incurred. We caution others designing similar localization interventions to protect against possible perverse incentives for inefficient care.

In internal medicine residency training, the most commonly used metric for measuring workload of physicians is the number of patients being followed or the number being admitted. There are data to support the importance of these census numbers. One study conducted at an academic medical center demonstrated that for patients admitted to medical services, the number of patients admitted on a call night was positively associated with mortality, even after adjustment in multivariable models.1

The problem with a census is that it is only a rough indicator of the amount of work that a given intern or resident will have. In a focus group study that our group conducted with internal medicine residents, several contributors to patient care errors were identified. Workload was identified as a major factor contributing to patient care mistakes.2 In describing workload, residents noted not only census but the complexity of the patient as contributing factors to workload.

A more comprehensive method than relying on census data has been used in anesthesia.3, 4 In 2 studies, anesthesiologists were asked to rate the effort or intensity associated with the tasks that they performed in the operating room.4, 5 In subsequent studies, this group used a trained observer to record the tasks anesthesiologists performed during a case.6, 7 Work density was calculated by multiplying the duration of each task by the previously developed task intensity score. In this way, work per unit of time can be calculated as can a cumulative workload score for a certain duration of time.

These methods provide the background for the work that we conducted in this study. The purpose of this study was to assign a task effort score to the tasks performed during periods that include admitting patients to the hospital.

METHODS

RESULTS

The study participation rate was 69% (59/85). The sample consisted of 31 (52%) women and 40 (68%) house staff (see Table 2). The mean age was 34 years. This participation rate represents approximately 1/3 of the internal medicine house staff and a smaller percentage of the faculty that would have been eligible.

DISCUSSION

In this study, we used a comprehensive list of tasks performed by internal medicine doctors while admitting patients and produced a numeric assessment of the effort associated with each. The list was generated by an expert panel and comprised 6 categories and 99 items. Residents and attending physicians then rated each task based on level of difficulty, specifically looking at the mental, psychological, and physical effort required by each.

Indirect patient care was the task category in our study that had the most tasks associated with it (41 out of 99). Direct patient care included 32 items, but 10 of these were procedures (eg, lumbar puncture), some of which are uncommonly performed. Several time‐motion studies have been performed to document the work done by residents815 and hospitalists.16, 17 Although our study did not assess the time spent on each task, the distribution of tasks across categories is consistent with these time‐motion studies, which show that the amount of time spent in direct patient care is a small fraction of the amount of time spent in the hospital,12 and that work such as interprofessional communication10 and documentation16 consume the majority of time.

This project allowed us to consider the effort required for inpatient internal medicine work on a more granular level than has been described previously. Although the difficulty of tasks associated with anesthesia and surgical work has been described,3, 4, 7, 1820 our study is a unique contribution to the internal medicine literature. Understanding the difficulty of tasks performed by inpatient physicians is an important step toward better management of workload. With concerns about burnout in hospitalists21, 22 and residents,2325 it seems wise to take the difficulty of the work they do into consideration in a more proactive manner. In addition, understanding workload may have patient safety applications. In one study of mistakes made by house staff, 51% of the survey respondents identified workload as a contributing factor.26

We assessed effort for inpatient work by generating a task list and then measuring 3 domains of each task: physical, mental, and psychological. As a result, we were able to further quantify the difficulty of work completed by physicians. Recent work from outside of medicine suggests that individuals have a finite capacity for mental workload, and when this is breached, decision‐making quality is impaired.27 This suggests that it is important to take work intensity into account when assigning work to individuals. For example, a detailed assessment of workload at the task level combined with the amount of time spent on each task would allow us to know how much effort is typically involved with admitting a new patient. This information would allow for more equal distribution of workload across admitting teams. In addition, these methods could be expanded to understand how much effort is involved in the discharge process. This could be taken into account at the beginning of a day when allocating work such as admissions and discharges between members of a team.

This methodology has the potential to be used in other ways to help quantify the effort required for the work that physicians do. Many departments are struggling to develop a system for giving credit to faculty for the time they spend on nonpatient care activities. Perhaps these methods could be used to develop effort scores associated with administrative tasks, and administrative relative value units could be calculated accordingly. Similar techniques have been used with educational relative value units.28

We know from the nursing literature that workload is related to both burnout and patient safety. Burnout is a process related to the emotional work of providing care to people.29 Our methods clearly incorporate the psychological stress of work into the workload assessment. Evaluating the amount of time spent on tasks with high psychological scores may be helpful in identifying work patterns that are more likely to produce burnout in physicians and nurses.

With respect to patient safety, higher patient‐to‐nurse ratios are associated with failure to rescue30 and nosocomial infections.31 Furthermore, researchers have demonstrated that systems issues can add substantially to nursing workload.32 Methods such as those described in our study take into account both patient‐related and systems‐related tasks, and therefore could result in more detailed workload assessments. With more detailed information about contributors to workload, better predictions about optimal staffing could be made, which would ultimately lead to fewer adverse patient events.

Our study has limitations. First, the initial task list was based on the compilation efforts from only 10 physicians. However, this group of physicians represented 3 hospitals and included both resident and attending physicians. Second, the survey data were gathered from a single institution. Although we included trainees and faculty, more participants would be needed to answer questions about how experience and setting/environmental factors affect these assessments. However, participants were instructed to reflect on their whole experience with each task, which presumably includes multiple institutions and training levels. Third, the sample size is fairly small, with more house staff than faculty (hospitalists and nonhospitalists) represented. Regardless, this study is the first attempt to define and quantify workload for internal medicine physicians using these methods. In future studies, we will expand the number of institutions and levels of experience to validate our current data. Finally, the difficulty of the tasks is clearly a subjective assessment. Although this methodology has face validity, further work needs to be done to validate these findings against other measurements of workload, such as census, or more general subjective workload assessments, such as the NASA task load index.33

In conclusion, we have described the tasks performed by inpatient physicians and the difficulty associated with them. Moreover, we have described a methodology that could be replicated at other centers for the purpose of validating our findings or quantifying workload of other types of tasks. We believe that this is the first step toward a more comprehensive understanding of the workload encountered by inpatient physicians. Because workload has implications for physician burnout and patient safety, it is essential that we fully understand the contributors to workload, including the innate difficulty of the tasks that comprise it.

In internal medicine residency training, the most commonly used metric for measuring workload of physicians is the number of patients being followed or the number being admitted. There are data to support the importance of these census numbers. One study conducted at an academic medical center demonstrated that for patients admitted to medical services, the number of patients admitted on a call night was positively associated with mortality, even after adjustment in multivariable models.1

The problem with a census is that it is only a rough indicator of the amount of work that a given intern or resident will have. In a focus group study that our group conducted with internal medicine residents, several contributors to patient care errors were identified. Workload was identified as a major factor contributing to patient care mistakes.2 In describing workload, residents noted not only census but the complexity of the patient as contributing factors to workload.

A more comprehensive method than relying on census data has been used in anesthesia.3, 4 In 2 studies, anesthesiologists were asked to rate the effort or intensity associated with the tasks that they performed in the operating room.4, 5 In subsequent studies, this group used a trained observer to record the tasks anesthesiologists performed during a case.6, 7 Work density was calculated by multiplying the duration of each task by the previously developed task intensity score. In this way, work per unit of time can be calculated as can a cumulative workload score for a certain duration of time.

These methods provide the background for the work that we conducted in this study. The purpose of this study was to assign a task effort score to the tasks performed during periods that include admitting patients to the hospital.

METHODS

RESULTS

The study participation rate was 69% (59/85). The sample consisted of 31 (52%) women and 40 (68%) house staff (see Table 2). The mean age was 34 years. This participation rate represents approximately 1/3 of the internal medicine house staff and a smaller percentage of the faculty that would have been eligible.

DISCUSSION

In this study, we used a comprehensive list of tasks performed by internal medicine doctors while admitting patients and produced a numeric assessment of the effort associated with each. The list was generated by an expert panel and comprised 6 categories and 99 items. Residents and attending physicians then rated each task based on level of difficulty, specifically looking at the mental, psychological, and physical effort required by each.

Indirect patient care was the task category in our study that had the most tasks associated with it (41 out of 99). Direct patient care included 32 items, but 10 of these were procedures (eg, lumbar puncture), some of which are uncommonly performed. Several time‐motion studies have been performed to document the work done by residents815 and hospitalists.16, 17 Although our study did not assess the time spent on each task, the distribution of tasks across categories is consistent with these time‐motion studies, which show that the amount of time spent in direct patient care is a small fraction of the amount of time spent in the hospital,12 and that work such as interprofessional communication10 and documentation16 consume the majority of time.

This project allowed us to consider the effort required for inpatient internal medicine work on a more granular level than has been described previously. Although the difficulty of tasks associated with anesthesia and surgical work has been described,3, 4, 7, 1820 our study is a unique contribution to the internal medicine literature. Understanding the difficulty of tasks performed by inpatient physicians is an important step toward better management of workload. With concerns about burnout in hospitalists21, 22 and residents,2325 it seems wise to take the difficulty of the work they do into consideration in a more proactive manner. In addition, understanding workload may have patient safety applications. In one study of mistakes made by house staff, 51% of the survey respondents identified workload as a contributing factor.26

We assessed effort for inpatient work by generating a task list and then measuring 3 domains of each task: physical, mental, and psychological. As a result, we were able to further quantify the difficulty of work completed by physicians. Recent work from outside of medicine suggests that individuals have a finite capacity for mental workload, and when this is breached, decision‐making quality is impaired.27 This suggests that it is important to take work intensity into account when assigning work to individuals. For example, a detailed assessment of workload at the task level combined with the amount of time spent on each task would allow us to know how much effort is typically involved with admitting a new patient. This information would allow for more equal distribution of workload across admitting teams. In addition, these methods could be expanded to understand how much effort is involved in the discharge process. This could be taken into account at the beginning of a day when allocating work such as admissions and discharges between members of a team.

This methodology has the potential to be used in other ways to help quantify the effort required for the work that physicians do. Many departments are struggling to develop a system for giving credit to faculty for the time they spend on nonpatient care activities. Perhaps these methods could be used to develop effort scores associated with administrative tasks, and administrative relative value units could be calculated accordingly. Similar techniques have been used with educational relative value units.28

We know from the nursing literature that workload is related to both burnout and patient safety. Burnout is a process related to the emotional work of providing care to people.29 Our methods clearly incorporate the psychological stress of work into the workload assessment. Evaluating the amount of time spent on tasks with high psychological scores may be helpful in identifying work patterns that are more likely to produce burnout in physicians and nurses.

With respect to patient safety, higher patient‐to‐nurse ratios are associated with failure to rescue30 and nosocomial infections.31 Furthermore, researchers have demonstrated that systems issues can add substantially to nursing workload.32 Methods such as those described in our study take into account both patient‐related and systems‐related tasks, and therefore could result in more detailed workload assessments. With more detailed information about contributors to workload, better predictions about optimal staffing could be made, which would ultimately lead to fewer adverse patient events.

Our study has limitations. First, the initial task list was based on the compilation efforts from only 10 physicians. However, this group of physicians represented 3 hospitals and included both resident and attending physicians. Second, the survey data were gathered from a single institution. Although we included trainees and faculty, more participants would be needed to answer questions about how experience and setting/environmental factors affect these assessments. However, participants were instructed to reflect on their whole experience with each task, which presumably includes multiple institutions and training levels. Third, the sample size is fairly small, with more house staff than faculty (hospitalists and nonhospitalists) represented. Regardless, this study is the first attempt to define and quantify workload for internal medicine physicians using these methods. In future studies, we will expand the number of institutions and levels of experience to validate our current data. Finally, the difficulty of the tasks is clearly a subjective assessment. Although this methodology has face validity, further work needs to be done to validate these findings against other measurements of workload, such as census, or more general subjective workload assessments, such as the NASA task load index.33

In conclusion, we have described the tasks performed by inpatient physicians and the difficulty associated with them. Moreover, we have described a methodology that could be replicated at other centers for the purpose of validating our findings or quantifying workload of other types of tasks. We believe that this is the first step toward a more comprehensive understanding of the workload encountered by inpatient physicians. Because workload has implications for physician burnout and patient safety, it is essential that we fully understand the contributors to workload, including the innate difficulty of the tasks that comprise it.

In internal medicine residency training, the most commonly used metric for measuring workload of physicians is the number of patients being followed or the number being admitted. There are data to support the importance of these census numbers. One study conducted at an academic medical center demonstrated that for patients admitted to medical services, the number of patients admitted on a call night was positively associated with mortality, even after adjustment in multivariable models.1

The problem with a census is that it is only a rough indicator of the amount of work that a given intern or resident will have. In a focus group study that our group conducted with internal medicine residents, several contributors to patient care errors were identified. Workload was identified as a major factor contributing to patient care mistakes.2 In describing workload, residents noted not only census but the complexity of the patient as contributing factors to workload.

A more comprehensive method than relying on census data has been used in anesthesia.3, 4 In 2 studies, anesthesiologists were asked to rate the effort or intensity associated with the tasks that they performed in the operating room.4, 5 In subsequent studies, this group used a trained observer to record the tasks anesthesiologists performed during a case.6, 7 Work density was calculated by multiplying the duration of each task by the previously developed task intensity score. In this way, work per unit of time can be calculated as can a cumulative workload score for a certain duration of time.

These methods provide the background for the work that we conducted in this study. The purpose of this study was to assign a task effort score to the tasks performed during periods that include admitting patients to the hospital.

METHODS

RESULTS

The study participation rate was 69% (59/85). The sample consisted of 31 (52%) women and 40 (68%) house staff (see Table 2). The mean age was 34 years. This participation rate represents approximately 1/3 of the internal medicine house staff and a smaller percentage of the faculty that would have been eligible.

DISCUSSION

In this study, we used a comprehensive list of tasks performed by internal medicine doctors while admitting patients and produced a numeric assessment of the effort associated with each. The list was generated by an expert panel and comprised 6 categories and 99 items. Residents and attending physicians then rated each task based on level of difficulty, specifically looking at the mental, psychological, and physical effort required by each.

Indirect patient care was the task category in our study that had the most tasks associated with it (41 out of 99). Direct patient care included 32 items, but 10 of these were procedures (eg, lumbar puncture), some of which are uncommonly performed. Several time‐motion studies have been performed to document the work done by residents815 and hospitalists.16, 17 Although our study did not assess the time spent on each task, the distribution of tasks across categories is consistent with these time‐motion studies, which show that the amount of time spent in direct patient care is a small fraction of the amount of time spent in the hospital,12 and that work such as interprofessional communication10 and documentation16 consume the majority of time.

This project allowed us to consider the effort required for inpatient internal medicine work on a more granular level than has been described previously. Although the difficulty of tasks associated with anesthesia and surgical work has been described,3, 4, 7, 1820 our study is a unique contribution to the internal medicine literature. Understanding the difficulty of tasks performed by inpatient physicians is an important step toward better management of workload. With concerns about burnout in hospitalists21, 22 and residents,2325 it seems wise to take the difficulty of the work they do into consideration in a more proactive manner. In addition, understanding workload may have patient safety applications. In one study of mistakes made by house staff, 51% of the survey respondents identified workload as a contributing factor.26

We assessed effort for inpatient work by generating a task list and then measuring 3 domains of each task: physical, mental, and psychological. As a result, we were able to further quantify the difficulty of work completed by physicians. Recent work from outside of medicine suggests that individuals have a finite capacity for mental workload, and when this is breached, decision‐making quality is impaired.27 This suggests that it is important to take work intensity into account when assigning work to individuals. For example, a detailed assessment of workload at the task level combined with the amount of time spent on each task would allow us to know how much effort is typically involved with admitting a new patient. This information would allow for more equal distribution of workload across admitting teams. In addition, these methods could be expanded to understand how much effort is involved in the discharge process. This could be taken into account at the beginning of a day when allocating work such as admissions and discharges between members of a team.

This methodology has the potential to be used in other ways to help quantify the effort required for the work that physicians do. Many departments are struggling to develop a system for giving credit to faculty for the time they spend on nonpatient care activities. Perhaps these methods could be used to develop effort scores associated with administrative tasks, and administrative relative value units could be calculated accordingly. Similar techniques have been used with educational relative value units.28

We know from the nursing literature that workload is related to both burnout and patient safety. Burnout is a process related to the emotional work of providing care to people.29 Our methods clearly incorporate the psychological stress of work into the workload assessment. Evaluating the amount of time spent on tasks with high psychological scores may be helpful in identifying work patterns that are more likely to produce burnout in physicians and nurses.

With respect to patient safety, higher patient‐to‐nurse ratios are associated with failure to rescue30 and nosocomial infections.31 Furthermore, researchers have demonstrated that systems issues can add substantially to nursing workload.32 Methods such as those described in our study take into account both patient‐related and systems‐related tasks, and therefore could result in more detailed workload assessments. With more detailed information about contributors to workload, better predictions about optimal staffing could be made, which would ultimately lead to fewer adverse patient events.

Our study has limitations. First, the initial task list was based on the compilation efforts from only 10 physicians. However, this group of physicians represented 3 hospitals and included both resident and attending physicians. Second, the survey data were gathered from a single institution. Although we included trainees and faculty, more participants would be needed to answer questions about how experience and setting/environmental factors affect these assessments. However, participants were instructed to reflect on their whole experience with each task, which presumably includes multiple institutions and training levels. Third, the sample size is fairly small, with more house staff than faculty (hospitalists and nonhospitalists) represented. Regardless, this study is the first attempt to define and quantify workload for internal medicine physicians using these methods. In future studies, we will expand the number of institutions and levels of experience to validate our current data. Finally, the difficulty of the tasks is clearly a subjective assessment. Although this methodology has face validity, further work needs to be done to validate these findings against other measurements of workload, such as census, or more general subjective workload assessments, such as the NASA task load index.33

In conclusion, we have described the tasks performed by inpatient physicians and the difficulty associated with them. Moreover, we have described a methodology that could be replicated at other centers for the purpose of validating our findings or quantifying workload of other types of tasks. We believe that this is the first step toward a more comprehensive understanding of the workload encountered by inpatient physicians. Because workload has implications for physician burnout and patient safety, it is essential that we fully understand the contributors to workload, including the innate difficulty of the tasks that comprise it.