Design Overview

We used a prospective observational design to measure patient mortality and LOS within 2 medical ICU staffing paradigms. This was a collaborative study between the Division of Hospital Medicine and the Division of Pulmonary and Critical Care Medicine, with approval from Emory University's Institutional Review Board.

The hospitalist ICU model was staffed by a board certified internal medicine attending, with clinical responsibilities limited to the ICU. An intensivist‐led consult team (members distinct from the intensivist‐led ICU team) was staffed by a board certified pulmonary critical care attending and non‐physician providers. This consult team comanaged mechanically ventilated patients and was available for additional critical care consultation at the hospitalists' discretion. The intensivist‐led ICU model was staffed by a board certified pulmonary critical care attending, a pulmonary critical care fellow (postgraduate years 46) and 4 internal medicine residents (postgraduate years 23).

For their respective patients, the hospitalist and intensivist‐led teams participated in similar multidisciplinary ICU rounds with the charge nurse, respiratory therapist, and pharmacist. Both teams used the same evidence‐based ICU protocols and order sets. The hospitalists and intensivists were aware of the ongoing study.

Setting and Participants

Our study was conducted in an urban, community teaching hospital that is affiliated with a major regional academic university and has 400 medical‐surgical beds, including 56 ICU beds. All medical ICU patients receiving primary medical care from the hospitalist or intensivist‐led team were assessed for inclusion between October 2007 and September 2008. Predetermined exclusion criteria included surgery under general anesthesia, outside hospital transfers, pregnancy, and age under 18.

Selection of the admitting ICU team followed existing institutional referral patterns. For emergency department (ED) patients, the ED physicians made the decision to admit to the ICU and contacted an ICU team based on the respiratory support needs of the patient, not the admitting diagnosis. ED patients with respiratory failure who required invasive ventilatory support were admitted to the intensivist‐led ICU team. Those without invasive ventilatory support were admitted to the hospitalist ICU team, including ones with respiratory failure requiring noninvasive ventilation. Patients transferred from a hospital floor bed to the ICU by non‐hospitalist physicians were assigned to the intensivist‐led ICU team, while those transferred by hospitalist floor teams were assigned to the hospitalist ICU team, regardless of diagnosis or respiratory support needs. Patient assignments deviated from these patterns, however, based on ICU teams' census. The intensivist‐led ICU team had a strict limit of 20 patients, established by the residency program, and the hospitalist ICU team had a preferred limit of 12 patients.

Measurement of Outcomes and Follow‐Up

Study endpoints were in‐hospital and ICU mortality, as well as hospital and ICU LOS. Patient characteristics and outcome data were collected prospectively from medical records and hospital databases by 2 trained research nurses according to study protocol. For data collection training, 1 investigator (K.R.W.) reviewed sample data from 108 patients to ensure consistency and accuracy of data abstraction. Patients with several ICU admissions during 1 hospitalization had ICU data collected only from the first ICU entry, consistent with other trials' methodology.1, 4, 10, 1820 Additional ICU entries did not change ICU LOS derived only from the initial entry, but did contribute to hospital LOS. Data from patients with multiple ICU entries was analyzed with the original team assignment. All patients were followed until death or hospital discharge.

Statistical Analysis

Sample size was determined a priori using an expected inpatient mortality of 10% from historical data, power of 80%, and 2‐sided alpha of 0.05 to demonstrate no difference in outcomes, defined as a mortality difference of <5% between teams. This mortality difference used for the power calculation is consistent with other trial designs.2125 The required sample size was 1306 patients calculated using PASS software (version 2008, NCSS, Kaysville, UT), accounting for an expected 3:2 admission rate to the hospitalists. The statistician was blinded to team assignments.

Clinical characteristics of the groups were compared with the Student t test. The outcome and predictor variable distributions were examined with univariate analyses. Bivariate analyses were calculated for each predictor and endpoint. Multiple logistic and linear regression analyses were performed. Propensity scores were used and defined as the conditional probability of admission to the hospitalist versus intensivist‐led ICU team given a patient's covariates. It included all predictors in Table 1 and was calculated using logistic regression. Outcome measures were excluded from the regression.

A generalized linear model (GENMOD), using a binomial distribution and an identity link function,26 assessed the in‐hospital and ICU mortality rate differences between teams while controlling for major risk factors identified. GENMOD, however, does not accommodate several covariates, as it often fails for lack of convergence. Hence, logistic regression models with adjusted odds ratios (aOR) are reported as well.

The initial logistic regression model for in‐hospital and ICU mortality included all 20 independent variables from patient demographics, comorbidities, simplified acute physiology score (SAPS) II,27 respiratory support, central venous catheter (CVC) utilization, which included peripherally placed central catheters, and all terms for 2‐way interactions with team assignment. To determine the best model, a hierarchical backward elimination was executed while assessing for interactions, confounding, and estimate precision. Before removing a regression term, a likelihood ratio test was applied to each coefficient followed by Wald's chi square test.28 Collinearity diagnostic for nonlinear models was applied to look for multicollinearity. To exclude variables or regression terms, a condition index of 30 and variance decomposition proportion of 0.5 were used. The final model was evaluated for goodness‐of‐fit using the Hosmer and Lemeshow test.

LOS was analyzed with linear regression using the same covariates and backward elimination as the logistic model. Goodness‐of‐fit was evaluated using coefficient of determination (r²). A variance inflation factor of 10 was used to assess for collinearity. Two‐sided P values 0.05 were considered statistically significant. All analyses were performed with SAS software (version 9.1, SAS Institute, Cary, NC).

Patients

A total of 1747 patients received critical care from the hospitalist or intensivist‐led teams (Figure 1). Of the 1367 patients who met inclusion criteria, complete data was available for 1356 patients. The ED was the ICU admission source for 68.8% of hospitalist and 69.9% of intensivist patients. Baseline patient demographics were similar (Table 1). Among preexisting comorbidities, morbid obesity was more prevalent in hospitalist patients, whereas cancer and pulmonary and immunological diseases were more prevalent in intensivist patients (Table 1).

Figure 1

Hospitalist patients, compared to intensivist patients, had a lower mean SAPS II (37.4 vs 45.1, P < 0.001), less noninvasive (17.9% vs 25.8%, P < 0.001) and mechanical (11.0% vs 51.9%, P < 0.001) ventilation utilization, and fewer CVCs (29.1% vs 50.8%, P < 0.001) (Table 1). The intensivist‐led consult team comanaged 18.4% of the hospitalist patients. These 152 patients had a mean SAPS II of 41, and 19.7% required noninvasive ventilation while 44.1% required mechanical ventilation. For mechanically ventilated hospitalist patients, 80.2% were comanaged by the intensivist‐led consult team, 5.5% had palliative care for end‐of‐life management, 6.6% died imminently, and 7.7% received short‐term ventilation managed by the hospitalist. Hospitalist and intensivist‐led teams' ICU readmission rates were similar (7.0% vs 5.9%, P = 0.41).

Outcomes

Of the 1356 patients, there were 168 (12.4%) deaths and 135 (10.0%) occurred in the ICU. The overall mean ICU LOS was 4.0 days (SD 5.9), and mean hospital LOS was 9.1 days (SD 9.0). The mean hospital LOS for survivors was 9.0 days (SD 8.8).

Subgroup Analysis

Since each team's respiratory support utilization differed greatly and was a significant variable in the logistic and linear regression models, we performed subgroup analysis of mechanically ventilated patients (Table 4). Without mechanical ventilation, no significant outcome differences were detected between the intensivist and hospitalist groups when stratified by disease severity (Table 4).

Table 4.Subgroup Analysis: Stratified Mortality and Length of Stay of Patients With and Without Mechanical Ventilation

	Without Mechanical Ventilation			With Mechanical Ventilation
	Hospitalist % (No. Died)	Intensivist % (No. Died)	P Value	Hospitalist % (No. Died)	Intensivist % (No. Died)	P Value
	Hospitalist Days (Patients)	Intensivist Days (Patients)	P Value	Hospitalist Days (Patients)	Intensivist Days (Patients)	P Value
NOTE: All patients included in analyses (hospitalist ICU team patients: n = 828 with 91 requiring mechanical ventilation; intensivist‐led ICU team patients: n = 528 with 274 requiring mechanical ventilation). Abbreviations: LOS, length of stay; SAPS, simplified acute physiology score.
In‐hospital mortality
SAPS 33	1.4 (5)	2.2 (2)	0.63	0.0 (0)	8.9 (4)	0.22
SAPS 3451	5.2 (15)	4.3 (5)	0.72	27.5 (11)	17.4 (19)	0.18
SAPS 52	20.0 (20)	15.6 (7)	0.53	57.1 (20)	50.0 (60)	0.46
ICU mortality
SAPS 33	0.6 (2)	1.1 (1)	0.60	0.0 (0)	8.9 (4)	0.22
SAPS 3451	3.1 (9)	3.5 (4)	0.86	27.5 (11)	15.6 (17)	0.10
SAPS 52	11.0 (11)	11.1 (5)	0.98	54.3 (19)	43.3 (52)	0.26
Hospital LOS
SAPS 33	5.6 (347)	6.5 (93)	0.25	13.8 (16)	11.8 (45)	0.50
SAPS 3451	8.7 (290)	7.9 (116)	0.28	17.8 (40)	10.6 (109)	<0.001
SAPS 52	10.2 (100)	11.0 (45)	0.62	12.7 (35)	14.3 (120)	0.56
ICU LOS (days)
SAPS 33	1.9 (347)	2.2 (93)	0.30	8.0 (16)	7.0 (45)	0.67
SAPS 3451	2.6 (290)	2.8 (116)	0.52	10.6 (40)	7.2 (109)	0.02
SAPS 52	3.5 (100)	2.8 (45)	0.17	8.1 (35)	9.2 (120)	0.61

With mechanical ventilation, patients with intermediate illness severity had a significantly shorter hospital LOS (10.6 vs 17.8 days, P < 0.001) and ICU LOS (7.2 vs 10.6 days, P = 0.02) when managed by the intensivist‐led team (Table 4). When the calculations were repeated for only the patients who survived hospitalization, the shorter ICU LOS (6.5 vs 10.9 days, P = 0.01) remained significant but not the hospital LOS (10.3 vs 19.3 days, P = 0.10). The patients with intermediate acuity also showed a trend toward a decreased ICU mortality (15.6% vs 27.5%, P = 0.10) when managed by the intensivist‐led team (Table 4).

Adjusting for relevant risk factors, no statistically significant mortality rate difference was demonstrated between the hospitalist and intensivist‐led teams when evaluating all patients or patients without mechanical ventilation. The result, however, was inconclusive for patients with mechanical ventilation and did not allow refutation of the null hypothesis because the confidence interval for the mortality rate difference crossed the prespecified mortality difference threshold for clinical significance (Figure 2).

Figure 2

Study Limitations

Our study has several obvious limitations. It uses an observational design within a single hospital. However, this is seen in prior comparisons of intensivists to non‐intensivists.15, 810 Our study is unique with its prospective design and sample‐size calculation to demonstrate no difference in outcomes. Because our data is from a single center, it eliminates practice differences encountered when comparing multiple institutions, but it may also limit its generalizability.

Another major limitation in our comparison of an intensivist‐led ICU team to a hospitalist ICU team is their composition. Instead of 2 multidisciplinary teams, we compared a hospitalist's performance to that of a group of physicians at various levels of training. Similar comparisons have been seen in prior studies. For example, in the large study by Levy et al., half of the intensivists studied were in academic centers affiliated with teaching teams.18 Housestaff involvement, however, may have confounded the intensivist‐led team's patient outcomes. Tenner et al. demonstrated improved survival and decreased LOS in a pediatric ICU when hospitalists provided after‐hours coverage instead of residents.30 Furthermore, the patient census varied between the ICU teams, potentially impacting outcomes. While each service had only 1 attending, the hospitalist team had 1 clinician caring for patients whereas the intensivist‐led team had 5 to 6 clinicians. This study's implications may be more relevant to academic centers. A similar study of hospitalists and intensivists conducted in a nonteaching institution may yield different results.

Our 2 patient groups had substantial differences in illness severity and mechanical ventilation. Despite statistical techniques to address potential confounders in observational trials including stratification, multivariable adjustment, and propensity scores,29 residual confounders may still remain that influence the results and thus our conclusions. SAPS II is a validated method to objectively quantify disease severity and provide predictive mortality,27 however, it has known deficiencies. The use of propensity scores may not fully account for selection biases in team assignments introduced by the ED physicians. Biases may stem from the ICU teams' awareness of the ongoing study, and each team may have tried to maintain improved outcomes.

Additionally, the mortality outcomes represent in‐hospital mortality, not 30‐day mortality. This may be a less‐useful indicator of ICU performance because of post‐ICU transitions to extended care facilities and emphasis on end‐of‐life care. The majority of patients from both ICU models, however, did transfer to inpatient medical units under the care of non‐ICU hospitalist teams. Furthermore, this study did not capture important outcomes reported in other investigations, such as discharge disposition or quality of life after discharge.3132 Finally, the adjusted odds ratio for the intensivist‐led team's in‐hospital mortality (aOR 0.8, P = 0.23), referent to the hospitalist, does not eliminate the possibility that an intensivist‐led model may reduce mortality risk.

Our study suggests that intermediate‐ and high‐acuity, mechanically ventilated patients may benefit from care by intensivists rather than hospitalists. The results from this initial study could be used to design and estimate sample size for future studies of hospitalists and intensivists to elucidate risk reduction. Randomized and multicenter trials are needed to provide more robust data, because our subgroups were small and not accounted for in the sample size calculation. Considering the severe intensivist shortage, 1 strategy to provide effective and efficient coverage of the growing American ICU population may be to ask hospitalists to care independently for lower acuity ICU patientsespecially nonventilated patientswhile encouraging or requiring intensivist care for higher acuity patients, especially once mechanically ventilated.

Conclusion

We anticipate this initial study of hospitalist and intensivist‐led ICU teams will validate a hospitalist ICU staffing model for further investigation. We propose that hospitalists can provide quality care for lower acuity critical care patients. This may improve intensivist availability to higher acuity critically ill patients and allow for judicious utilization of the limited intensivist supply. Future studies may better delineate specific subgroups of critically ill patients who benefit most from intensivist primary involvement. Additional research may also help generate evidence‐based triage standards to appropriate critical care teams and foster guideline development. Hospitalists may be instrumental in the critical care staffing shortage, however, identification of their ideal role requires further study.

Design Overview

We used a prospective observational design to measure patient mortality and LOS within 2 medical ICU staffing paradigms. This was a collaborative study between the Division of Hospital Medicine and the Division of Pulmonary and Critical Care Medicine, with approval from Emory University's Institutional Review Board.

The hospitalist ICU model was staffed by a board certified internal medicine attending, with clinical responsibilities limited to the ICU. An intensivist‐led consult team (members distinct from the intensivist‐led ICU team) was staffed by a board certified pulmonary critical care attending and non‐physician providers. This consult team comanaged mechanically ventilated patients and was available for additional critical care consultation at the hospitalists' discretion. The intensivist‐led ICU model was staffed by a board certified pulmonary critical care attending, a pulmonary critical care fellow (postgraduate years 46) and 4 internal medicine residents (postgraduate years 23).

For their respective patients, the hospitalist and intensivist‐led teams participated in similar multidisciplinary ICU rounds with the charge nurse, respiratory therapist, and pharmacist. Both teams used the same evidence‐based ICU protocols and order sets. The hospitalists and intensivists were aware of the ongoing study.

Setting and Participants

Our study was conducted in an urban, community teaching hospital that is affiliated with a major regional academic university and has 400 medical‐surgical beds, including 56 ICU beds. All medical ICU patients receiving primary medical care from the hospitalist or intensivist‐led team were assessed for inclusion between October 2007 and September 2008. Predetermined exclusion criteria included surgery under general anesthesia, outside hospital transfers, pregnancy, and age under 18.

Selection of the admitting ICU team followed existing institutional referral patterns. For emergency department (ED) patients, the ED physicians made the decision to admit to the ICU and contacted an ICU team based on the respiratory support needs of the patient, not the admitting diagnosis. ED patients with respiratory failure who required invasive ventilatory support were admitted to the intensivist‐led ICU team. Those without invasive ventilatory support were admitted to the hospitalist ICU team, including ones with respiratory failure requiring noninvasive ventilation. Patients transferred from a hospital floor bed to the ICU by non‐hospitalist physicians were assigned to the intensivist‐led ICU team, while those transferred by hospitalist floor teams were assigned to the hospitalist ICU team, regardless of diagnosis or respiratory support needs. Patient assignments deviated from these patterns, however, based on ICU teams' census. The intensivist‐led ICU team had a strict limit of 20 patients, established by the residency program, and the hospitalist ICU team had a preferred limit of 12 patients.

Measurement of Outcomes and Follow‐Up

Study endpoints were in‐hospital and ICU mortality, as well as hospital and ICU LOS. Patient characteristics and outcome data were collected prospectively from medical records and hospital databases by 2 trained research nurses according to study protocol. For data collection training, 1 investigator (K.R.W.) reviewed sample data from 108 patients to ensure consistency and accuracy of data abstraction. Patients with several ICU admissions during 1 hospitalization had ICU data collected only from the first ICU entry, consistent with other trials' methodology.1, 4, 10, 1820 Additional ICU entries did not change ICU LOS derived only from the initial entry, but did contribute to hospital LOS. Data from patients with multiple ICU entries was analyzed with the original team assignment. All patients were followed until death or hospital discharge.

Statistical Analysis

Sample size was determined a priori using an expected inpatient mortality of 10% from historical data, power of 80%, and 2‐sided alpha of 0.05 to demonstrate no difference in outcomes, defined as a mortality difference of <5% between teams. This mortality difference used for the power calculation is consistent with other trial designs.2125 The required sample size was 1306 patients calculated using PASS software (version 2008, NCSS, Kaysville, UT), accounting for an expected 3:2 admission rate to the hospitalists. The statistician was blinded to team assignments.

Clinical characteristics of the groups were compared with the Student t test. The outcome and predictor variable distributions were examined with univariate analyses. Bivariate analyses were calculated for each predictor and endpoint. Multiple logistic and linear regression analyses were performed. Propensity scores were used and defined as the conditional probability of admission to the hospitalist versus intensivist‐led ICU team given a patient's covariates. It included all predictors in Table 1 and was calculated using logistic regression. Outcome measures were excluded from the regression.

A generalized linear model (GENMOD), using a binomial distribution and an identity link function,26 assessed the in‐hospital and ICU mortality rate differences between teams while controlling for major risk factors identified. GENMOD, however, does not accommodate several covariates, as it often fails for lack of convergence. Hence, logistic regression models with adjusted odds ratios (aOR) are reported as well.

The initial logistic regression model for in‐hospital and ICU mortality included all 20 independent variables from patient demographics, comorbidities, simplified acute physiology score (SAPS) II,27 respiratory support, central venous catheter (CVC) utilization, which included peripherally placed central catheters, and all terms for 2‐way interactions with team assignment. To determine the best model, a hierarchical backward elimination was executed while assessing for interactions, confounding, and estimate precision. Before removing a regression term, a likelihood ratio test was applied to each coefficient followed by Wald's chi square test.28 Collinearity diagnostic for nonlinear models was applied to look for multicollinearity. To exclude variables or regression terms, a condition index of 30 and variance decomposition proportion of 0.5 were used. The final model was evaluated for goodness‐of‐fit using the Hosmer and Lemeshow test.

LOS was analyzed with linear regression using the same covariates and backward elimination as the logistic model. Goodness‐of‐fit was evaluated using coefficient of determination (r²). A variance inflation factor of 10 was used to assess for collinearity. Two‐sided P values 0.05 were considered statistically significant. All analyses were performed with SAS software (version 9.1, SAS Institute, Cary, NC).

Patients

A total of 1747 patients received critical care from the hospitalist or intensivist‐led teams (Figure 1). Of the 1367 patients who met inclusion criteria, complete data was available for 1356 patients. The ED was the ICU admission source for 68.8% of hospitalist and 69.9% of intensivist patients. Baseline patient demographics were similar (Table 1). Among preexisting comorbidities, morbid obesity was more prevalent in hospitalist patients, whereas cancer and pulmonary and immunological diseases were more prevalent in intensivist patients (Table 1).

Figure 1

Hospitalist patients, compared to intensivist patients, had a lower mean SAPS II (37.4 vs 45.1, P < 0.001), less noninvasive (17.9% vs 25.8%, P < 0.001) and mechanical (11.0% vs 51.9%, P < 0.001) ventilation utilization, and fewer CVCs (29.1% vs 50.8%, P < 0.001) (Table 1). The intensivist‐led consult team comanaged 18.4% of the hospitalist patients. These 152 patients had a mean SAPS II of 41, and 19.7% required noninvasive ventilation while 44.1% required mechanical ventilation. For mechanically ventilated hospitalist patients, 80.2% were comanaged by the intensivist‐led consult team, 5.5% had palliative care for end‐of‐life management, 6.6% died imminently, and 7.7% received short‐term ventilation managed by the hospitalist. Hospitalist and intensivist‐led teams' ICU readmission rates were similar (7.0% vs 5.9%, P = 0.41).

Outcomes

Of the 1356 patients, there were 168 (12.4%) deaths and 135 (10.0%) occurred in the ICU. The overall mean ICU LOS was 4.0 days (SD 5.9), and mean hospital LOS was 9.1 days (SD 9.0). The mean hospital LOS for survivors was 9.0 days (SD 8.8).

Subgroup Analysis

Since each team's respiratory support utilization differed greatly and was a significant variable in the logistic and linear regression models, we performed subgroup analysis of mechanically ventilated patients (Table 4). Without mechanical ventilation, no significant outcome differences were detected between the intensivist and hospitalist groups when stratified by disease severity (Table 4).

Table 4.Subgroup Analysis: Stratified Mortality and Length of Stay of Patients With and Without Mechanical Ventilation

	Without Mechanical Ventilation			With Mechanical Ventilation
	Hospitalist % (No. Died)	Intensivist % (No. Died)	P Value	Hospitalist % (No. Died)	Intensivist % (No. Died)	P Value
	Hospitalist Days (Patients)	Intensivist Days (Patients)	P Value	Hospitalist Days (Patients)	Intensivist Days (Patients)	P Value
NOTE: All patients included in analyses (hospitalist ICU team patients: n = 828 with 91 requiring mechanical ventilation; intensivist‐led ICU team patients: n = 528 with 274 requiring mechanical ventilation). Abbreviations: LOS, length of stay; SAPS, simplified acute physiology score.
In‐hospital mortality
SAPS 33	1.4 (5)	2.2 (2)	0.63	0.0 (0)	8.9 (4)	0.22
SAPS 3451	5.2 (15)	4.3 (5)	0.72	27.5 (11)	17.4 (19)	0.18
SAPS 52	20.0 (20)	15.6 (7)	0.53	57.1 (20)	50.0 (60)	0.46
ICU mortality
SAPS 33	0.6 (2)	1.1 (1)	0.60	0.0 (0)	8.9 (4)	0.22
SAPS 3451	3.1 (9)	3.5 (4)	0.86	27.5 (11)	15.6 (17)	0.10
SAPS 52	11.0 (11)	11.1 (5)	0.98	54.3 (19)	43.3 (52)	0.26
Hospital LOS
SAPS 33	5.6 (347)	6.5 (93)	0.25	13.8 (16)	11.8 (45)	0.50
SAPS 3451	8.7 (290)	7.9 (116)	0.28	17.8 (40)	10.6 (109)	<0.001
SAPS 52	10.2 (100)	11.0 (45)	0.62	12.7 (35)	14.3 (120)	0.56
ICU LOS (days)
SAPS 33	1.9 (347)	2.2 (93)	0.30	8.0 (16)	7.0 (45)	0.67
SAPS 3451	2.6 (290)	2.8 (116)	0.52	10.6 (40)	7.2 (109)	0.02
SAPS 52	3.5 (100)	2.8 (45)	0.17	8.1 (35)	9.2 (120)	0.61

With mechanical ventilation, patients with intermediate illness severity had a significantly shorter hospital LOS (10.6 vs 17.8 days, P < 0.001) and ICU LOS (7.2 vs 10.6 days, P = 0.02) when managed by the intensivist‐led team (Table 4). When the calculations were repeated for only the patients who survived hospitalization, the shorter ICU LOS (6.5 vs 10.9 days, P = 0.01) remained significant but not the hospital LOS (10.3 vs 19.3 days, P = 0.10). The patients with intermediate acuity also showed a trend toward a decreased ICU mortality (15.6% vs 27.5%, P = 0.10) when managed by the intensivist‐led team (Table 4).

Adjusting for relevant risk factors, no statistically significant mortality rate difference was demonstrated between the hospitalist and intensivist‐led teams when evaluating all patients or patients without mechanical ventilation. The result, however, was inconclusive for patients with mechanical ventilation and did not allow refutation of the null hypothesis because the confidence interval for the mortality rate difference crossed the prespecified mortality difference threshold for clinical significance (Figure 2).

Figure 2

Study Limitations

Our study has several obvious limitations. It uses an observational design within a single hospital. However, this is seen in prior comparisons of intensivists to non‐intensivists.15, 810 Our study is unique with its prospective design and sample‐size calculation to demonstrate no difference in outcomes. Because our data is from a single center, it eliminates practice differences encountered when comparing multiple institutions, but it may also limit its generalizability.

Another major limitation in our comparison of an intensivist‐led ICU team to a hospitalist ICU team is their composition. Instead of 2 multidisciplinary teams, we compared a hospitalist's performance to that of a group of physicians at various levels of training. Similar comparisons have been seen in prior studies. For example, in the large study by Levy et al., half of the intensivists studied were in academic centers affiliated with teaching teams.18 Housestaff involvement, however, may have confounded the intensivist‐led team's patient outcomes. Tenner et al. demonstrated improved survival and decreased LOS in a pediatric ICU when hospitalists provided after‐hours coverage instead of residents.30 Furthermore, the patient census varied between the ICU teams, potentially impacting outcomes. While each service had only 1 attending, the hospitalist team had 1 clinician caring for patients whereas the intensivist‐led team had 5 to 6 clinicians. This study's implications may be more relevant to academic centers. A similar study of hospitalists and intensivists conducted in a nonteaching institution may yield different results.

Our 2 patient groups had substantial differences in illness severity and mechanical ventilation. Despite statistical techniques to address potential confounders in observational trials including stratification, multivariable adjustment, and propensity scores,29 residual confounders may still remain that influence the results and thus our conclusions. SAPS II is a validated method to objectively quantify disease severity and provide predictive mortality,27 however, it has known deficiencies. The use of propensity scores may not fully account for selection biases in team assignments introduced by the ED physicians. Biases may stem from the ICU teams' awareness of the ongoing study, and each team may have tried to maintain improved outcomes.

Additionally, the mortality outcomes represent in‐hospital mortality, not 30‐day mortality. This may be a less‐useful indicator of ICU performance because of post‐ICU transitions to extended care facilities and emphasis on end‐of‐life care. The majority of patients from both ICU models, however, did transfer to inpatient medical units under the care of non‐ICU hospitalist teams. Furthermore, this study did not capture important outcomes reported in other investigations, such as discharge disposition or quality of life after discharge.3132 Finally, the adjusted odds ratio for the intensivist‐led team's in‐hospital mortality (aOR 0.8, P = 0.23), referent to the hospitalist, does not eliminate the possibility that an intensivist‐led model may reduce mortality risk.

Our study suggests that intermediate‐ and high‐acuity, mechanically ventilated patients may benefit from care by intensivists rather than hospitalists. The results from this initial study could be used to design and estimate sample size for future studies of hospitalists and intensivists to elucidate risk reduction. Randomized and multicenter trials are needed to provide more robust data, because our subgroups were small and not accounted for in the sample size calculation. Considering the severe intensivist shortage, 1 strategy to provide effective and efficient coverage of the growing American ICU population may be to ask hospitalists to care independently for lower acuity ICU patientsespecially nonventilated patientswhile encouraging or requiring intensivist care for higher acuity patients, especially once mechanically ventilated.

Conclusion

We anticipate this initial study of hospitalist and intensivist‐led ICU teams will validate a hospitalist ICU staffing model for further investigation. We propose that hospitalists can provide quality care for lower acuity critical care patients. This may improve intensivist availability to higher acuity critically ill patients and allow for judicious utilization of the limited intensivist supply. Future studies may better delineate specific subgroups of critically ill patients who benefit most from intensivist primary involvement. Additional research may also help generate evidence‐based triage standards to appropriate critical care teams and foster guideline development. Hospitalists may be instrumental in the critical care staffing shortage, however, identification of their ideal role requires further study.

Design Overview

We used a prospective observational design to measure patient mortality and LOS within 2 medical ICU staffing paradigms. This was a collaborative study between the Division of Hospital Medicine and the Division of Pulmonary and Critical Care Medicine, with approval from Emory University's Institutional Review Board.

The hospitalist ICU model was staffed by a board certified internal medicine attending, with clinical responsibilities limited to the ICU. An intensivist‐led consult team (members distinct from the intensivist‐led ICU team) was staffed by a board certified pulmonary critical care attending and non‐physician providers. This consult team comanaged mechanically ventilated patients and was available for additional critical care consultation at the hospitalists' discretion. The intensivist‐led ICU model was staffed by a board certified pulmonary critical care attending, a pulmonary critical care fellow (postgraduate years 46) and 4 internal medicine residents (postgraduate years 23).

For their respective patients, the hospitalist and intensivist‐led teams participated in similar multidisciplinary ICU rounds with the charge nurse, respiratory therapist, and pharmacist. Both teams used the same evidence‐based ICU protocols and order sets. The hospitalists and intensivists were aware of the ongoing study.

Setting and Participants

Our study was conducted in an urban, community teaching hospital that is affiliated with a major regional academic university and has 400 medical‐surgical beds, including 56 ICU beds. All medical ICU patients receiving primary medical care from the hospitalist or intensivist‐led team were assessed for inclusion between October 2007 and September 2008. Predetermined exclusion criteria included surgery under general anesthesia, outside hospital transfers, pregnancy, and age under 18.

Selection of the admitting ICU team followed existing institutional referral patterns. For emergency department (ED) patients, the ED physicians made the decision to admit to the ICU and contacted an ICU team based on the respiratory support needs of the patient, not the admitting diagnosis. ED patients with respiratory failure who required invasive ventilatory support were admitted to the intensivist‐led ICU team. Those without invasive ventilatory support were admitted to the hospitalist ICU team, including ones with respiratory failure requiring noninvasive ventilation. Patients transferred from a hospital floor bed to the ICU by non‐hospitalist physicians were assigned to the intensivist‐led ICU team, while those transferred by hospitalist floor teams were assigned to the hospitalist ICU team, regardless of diagnosis or respiratory support needs. Patient assignments deviated from these patterns, however, based on ICU teams' census. The intensivist‐led ICU team had a strict limit of 20 patients, established by the residency program, and the hospitalist ICU team had a preferred limit of 12 patients.

Measurement of Outcomes and Follow‐Up

Study endpoints were in‐hospital and ICU mortality, as well as hospital and ICU LOS. Patient characteristics and outcome data were collected prospectively from medical records and hospital databases by 2 trained research nurses according to study protocol. For data collection training, 1 investigator (K.R.W.) reviewed sample data from 108 patients to ensure consistency and accuracy of data abstraction. Patients with several ICU admissions during 1 hospitalization had ICU data collected only from the first ICU entry, consistent with other trials' methodology.1, 4, 10, 1820 Additional ICU entries did not change ICU LOS derived only from the initial entry, but did contribute to hospital LOS. Data from patients with multiple ICU entries was analyzed with the original team assignment. All patients were followed until death or hospital discharge.

Statistical Analysis

Sample size was determined a priori using an expected inpatient mortality of 10% from historical data, power of 80%, and 2‐sided alpha of 0.05 to demonstrate no difference in outcomes, defined as a mortality difference of <5% between teams. This mortality difference used for the power calculation is consistent with other trial designs.2125 The required sample size was 1306 patients calculated using PASS software (version 2008, NCSS, Kaysville, UT), accounting for an expected 3:2 admission rate to the hospitalists. The statistician was blinded to team assignments.

Clinical characteristics of the groups were compared with the Student t test. The outcome and predictor variable distributions were examined with univariate analyses. Bivariate analyses were calculated for each predictor and endpoint. Multiple logistic and linear regression analyses were performed. Propensity scores were used and defined as the conditional probability of admission to the hospitalist versus intensivist‐led ICU team given a patient's covariates. It included all predictors in Table 1 and was calculated using logistic regression. Outcome measures were excluded from the regression.

A generalized linear model (GENMOD), using a binomial distribution and an identity link function,26 assessed the in‐hospital and ICU mortality rate differences between teams while controlling for major risk factors identified. GENMOD, however, does not accommodate several covariates, as it often fails for lack of convergence. Hence, logistic regression models with adjusted odds ratios (aOR) are reported as well.

The initial logistic regression model for in‐hospital and ICU mortality included all 20 independent variables from patient demographics, comorbidities, simplified acute physiology score (SAPS) II,27 respiratory support, central venous catheter (CVC) utilization, which included peripherally placed central catheters, and all terms for 2‐way interactions with team assignment. To determine the best model, a hierarchical backward elimination was executed while assessing for interactions, confounding, and estimate precision. Before removing a regression term, a likelihood ratio test was applied to each coefficient followed by Wald's chi square test.28 Collinearity diagnostic for nonlinear models was applied to look for multicollinearity. To exclude variables or regression terms, a condition index of 30 and variance decomposition proportion of 0.5 were used. The final model was evaluated for goodness‐of‐fit using the Hosmer and Lemeshow test.

LOS was analyzed with linear regression using the same covariates and backward elimination as the logistic model. Goodness‐of‐fit was evaluated using coefficient of determination (r²). A variance inflation factor of 10 was used to assess for collinearity. Two‐sided P values 0.05 were considered statistically significant. All analyses were performed with SAS software (version 9.1, SAS Institute, Cary, NC).

Patients

A total of 1747 patients received critical care from the hospitalist or intensivist‐led teams (Figure 1). Of the 1367 patients who met inclusion criteria, complete data was available for 1356 patients. The ED was the ICU admission source for 68.8% of hospitalist and 69.9% of intensivist patients. Baseline patient demographics were similar (Table 1). Among preexisting comorbidities, morbid obesity was more prevalent in hospitalist patients, whereas cancer and pulmonary and immunological diseases were more prevalent in intensivist patients (Table 1).

Figure 1

Hospitalist patients, compared to intensivist patients, had a lower mean SAPS II (37.4 vs 45.1, P < 0.001), less noninvasive (17.9% vs 25.8%, P < 0.001) and mechanical (11.0% vs 51.9%, P < 0.001) ventilation utilization, and fewer CVCs (29.1% vs 50.8%, P < 0.001) (Table 1). The intensivist‐led consult team comanaged 18.4% of the hospitalist patients. These 152 patients had a mean SAPS II of 41, and 19.7% required noninvasive ventilation while 44.1% required mechanical ventilation. For mechanically ventilated hospitalist patients, 80.2% were comanaged by the intensivist‐led consult team, 5.5% had palliative care for end‐of‐life management, 6.6% died imminently, and 7.7% received short‐term ventilation managed by the hospitalist. Hospitalist and intensivist‐led teams' ICU readmission rates were similar (7.0% vs 5.9%, P = 0.41).

Outcomes

Of the 1356 patients, there were 168 (12.4%) deaths and 135 (10.0%) occurred in the ICU. The overall mean ICU LOS was 4.0 days (SD 5.9), and mean hospital LOS was 9.1 days (SD 9.0). The mean hospital LOS for survivors was 9.0 days (SD 8.8).

Subgroup Analysis

Since each team's respiratory support utilization differed greatly and was a significant variable in the logistic and linear regression models, we performed subgroup analysis of mechanically ventilated patients (Table 4). Without mechanical ventilation, no significant outcome differences were detected between the intensivist and hospitalist groups when stratified by disease severity (Table 4).

Table 4.Subgroup Analysis: Stratified Mortality and Length of Stay of Patients With and Without Mechanical Ventilation

	Without Mechanical Ventilation			With Mechanical Ventilation
	Hospitalist % (No. Died)	Intensivist % (No. Died)	P Value	Hospitalist % (No. Died)	Intensivist % (No. Died)	P Value
	Hospitalist Days (Patients)	Intensivist Days (Patients)	P Value	Hospitalist Days (Patients)	Intensivist Days (Patients)	P Value
NOTE: All patients included in analyses (hospitalist ICU team patients: n = 828 with 91 requiring mechanical ventilation; intensivist‐led ICU team patients: n = 528 with 274 requiring mechanical ventilation). Abbreviations: LOS, length of stay; SAPS, simplified acute physiology score.
In‐hospital mortality
SAPS 33	1.4 (5)	2.2 (2)	0.63	0.0 (0)	8.9 (4)	0.22
SAPS 3451	5.2 (15)	4.3 (5)	0.72	27.5 (11)	17.4 (19)	0.18
SAPS 52	20.0 (20)	15.6 (7)	0.53	57.1 (20)	50.0 (60)	0.46
ICU mortality
SAPS 33	0.6 (2)	1.1 (1)	0.60	0.0 (0)	8.9 (4)	0.22
SAPS 3451	3.1 (9)	3.5 (4)	0.86	27.5 (11)	15.6 (17)	0.10
SAPS 52	11.0 (11)	11.1 (5)	0.98	54.3 (19)	43.3 (52)	0.26
Hospital LOS
SAPS 33	5.6 (347)	6.5 (93)	0.25	13.8 (16)	11.8 (45)	0.50
SAPS 3451	8.7 (290)	7.9 (116)	0.28	17.8 (40)	10.6 (109)	<0.001
SAPS 52	10.2 (100)	11.0 (45)	0.62	12.7 (35)	14.3 (120)	0.56
ICU LOS (days)
SAPS 33	1.9 (347)	2.2 (93)	0.30	8.0 (16)	7.0 (45)	0.67
SAPS 3451	2.6 (290)	2.8 (116)	0.52	10.6 (40)	7.2 (109)	0.02
SAPS 52	3.5 (100)	2.8 (45)	0.17	8.1 (35)	9.2 (120)	0.61

With mechanical ventilation, patients with intermediate illness severity had a significantly shorter hospital LOS (10.6 vs 17.8 days, P < 0.001) and ICU LOS (7.2 vs 10.6 days, P = 0.02) when managed by the intensivist‐led team (Table 4). When the calculations were repeated for only the patients who survived hospitalization, the shorter ICU LOS (6.5 vs 10.9 days, P = 0.01) remained significant but not the hospital LOS (10.3 vs 19.3 days, P = 0.10). The patients with intermediate acuity also showed a trend toward a decreased ICU mortality (15.6% vs 27.5%, P = 0.10) when managed by the intensivist‐led team (Table 4).

Adjusting for relevant risk factors, no statistically significant mortality rate difference was demonstrated between the hospitalist and intensivist‐led teams when evaluating all patients or patients without mechanical ventilation. The result, however, was inconclusive for patients with mechanical ventilation and did not allow refutation of the null hypothesis because the confidence interval for the mortality rate difference crossed the prespecified mortality difference threshold for clinical significance (Figure 2).

Figure 2

Study Limitations

Our study has several obvious limitations. It uses an observational design within a single hospital. However, this is seen in prior comparisons of intensivists to non‐intensivists.15, 810 Our study is unique with its prospective design and sample‐size calculation to demonstrate no difference in outcomes. Because our data is from a single center, it eliminates practice differences encountered when comparing multiple institutions, but it may also limit its generalizability.

Another major limitation in our comparison of an intensivist‐led ICU team to a hospitalist ICU team is their composition. Instead of 2 multidisciplinary teams, we compared a hospitalist's performance to that of a group of physicians at various levels of training. Similar comparisons have been seen in prior studies. For example, in the large study by Levy et al., half of the intensivists studied were in academic centers affiliated with teaching teams.18 Housestaff involvement, however, may have confounded the intensivist‐led team's patient outcomes. Tenner et al. demonstrated improved survival and decreased LOS in a pediatric ICU when hospitalists provided after‐hours coverage instead of residents.30 Furthermore, the patient census varied between the ICU teams, potentially impacting outcomes. While each service had only 1 attending, the hospitalist team had 1 clinician caring for patients whereas the intensivist‐led team had 5 to 6 clinicians. This study's implications may be more relevant to academic centers. A similar study of hospitalists and intensivists conducted in a nonteaching institution may yield different results.

Our 2 patient groups had substantial differences in illness severity and mechanical ventilation. Despite statistical techniques to address potential confounders in observational trials including stratification, multivariable adjustment, and propensity scores,29 residual confounders may still remain that influence the results and thus our conclusions. SAPS II is a validated method to objectively quantify disease severity and provide predictive mortality,27 however, it has known deficiencies. The use of propensity scores may not fully account for selection biases in team assignments introduced by the ED physicians. Biases may stem from the ICU teams' awareness of the ongoing study, and each team may have tried to maintain improved outcomes.

Additionally, the mortality outcomes represent in‐hospital mortality, not 30‐day mortality. This may be a less‐useful indicator of ICU performance because of post‐ICU transitions to extended care facilities and emphasis on end‐of‐life care. The majority of patients from both ICU models, however, did transfer to inpatient medical units under the care of non‐ICU hospitalist teams. Furthermore, this study did not capture important outcomes reported in other investigations, such as discharge disposition or quality of life after discharge.3132 Finally, the adjusted odds ratio for the intensivist‐led team's in‐hospital mortality (aOR 0.8, P = 0.23), referent to the hospitalist, does not eliminate the possibility that an intensivist‐led model may reduce mortality risk.

Our study suggests that intermediate‐ and high‐acuity, mechanically ventilated patients may benefit from care by intensivists rather than hospitalists. The results from this initial study could be used to design and estimate sample size for future studies of hospitalists and intensivists to elucidate risk reduction. Randomized and multicenter trials are needed to provide more robust data, because our subgroups were small and not accounted for in the sample size calculation. Considering the severe intensivist shortage, 1 strategy to provide effective and efficient coverage of the growing American ICU population may be to ask hospitalists to care independently for lower acuity ICU patientsespecially nonventilated patientswhile encouraging or requiring intensivist care for higher acuity patients, especially once mechanically ventilated.

Conclusion

We anticipate this initial study of hospitalist and intensivist‐led ICU teams will validate a hospitalist ICU staffing model for further investigation. We propose that hospitalists can provide quality care for lower acuity critical care patients. This may improve intensivist availability to higher acuity critically ill patients and allow for judicious utilization of the limited intensivist supply. Future studies may better delineate specific subgroups of critically ill patients who benefit most from intensivist primary involvement. Additional research may also help generate evidence‐based triage standards to appropriate critical care teams and foster guideline development. Hospitalists may be instrumental in the critical care staffing shortage, however, identification of their ideal role requires further study.

Factors Associated With Higher Risk of Violence

Adjusted odds ratios of physical aggression were significantly high in general wards, psychiatric wards, critical care centers/emntensive care units (ICU)/cardiac care units (CCU), and long‐term care wards; and for nurses, nursing aides/care workers; for longer working hours; and for direct interaction with patients (Table 3).

Adjusted odds ratios of verbal abuse were significantly high in hospital with 200 beds or more, in general wards, psychiatric wards, long‐term care wards, outpatient departments, and dialysis departments; and for longer years of experience in their own specialty; for longer working hours; and for direct interaction with patients.

Adjusted odds ratios of sexual harassment were significantly high in general wards, and dialysis departments; for nurses, nursing aides/care workers, technicians, therapists; for females; and for direct interaction with patients.

Adjusted odds ratios for at least 1 of the 3 kinds of workplace violence were significantly high in hospitals with 200 beds or more, in general wards, psychiatric wards, critical care centers/ICU/CCU, long‐term care wards, and dialysis departments; for nurses; for longer years of experience in their own specialty; for longer working hours; for females; and for direct interaction with patients.

Factors Associated With Lower Risk of Violence

Adjusted odds ratios of physical aggression were significantly low in dialysis departments; outpatient departments; operation departments; obstetrics and gynecology wards, perinatal wards, or neonatal intensive care units (NICU); and for clerks.

Adjusted odds ratios of verbal abuse were significantly low in operation departments; obstetrics and gynecology wards, perinatal wards, or NICU; and for technicians.

Adjusted odds ratios of sexual harassment were significantly low in clinical radiology departments; outpatient departments; pediatric wards; operation departments; obstetrics and gynecology wards, perinatal wards, or NICU; and for longer years of experience in their own specialty.

Adjusted odds ratios for at least 1 of the 3 kinds of workplace violence were significantly low in pediatric wards; operation departments; obstetrics and gynecology wards, perinatal wards, or NICU; and for technicians.

Incidence of Workplace Violence

Most previous studies covered only specific professions at hospitals, such as nurses or critical care center staff, and there are only few multicenter studies covering entire sections and professions like this study.510, 1214 A Spanish study including about 8000 healthcare workers reported that 11% had experienced physical aggression, 64% had experienced threatening behavior, intimidation, or insults in the past year.11 The incidence of physical aggression was similar in both studies, but the incidence of verbal abuse was about twice as high as that of this study. The low response rate in the Spanish study (24%) might have contributed to a higher number of verbal abuse incidents, because those with experience of workplace violence would likely have answered the questionnaire. It is difficult to compare the incidence of workplace violence among different studies because the definitions of workplace violence differ widely. Ethnic culture might also affect the acknowledgement of workplace violence. The European NEXT study including about 30,000 nurses in 8 European countries reported a range of 10.4% incidence of workplace violence in the Netherlands, and 39.1% in France.10

Risk Factors of Physical Aggression

Previous studies reported that the prevalence of physical aggression is high in psychiatric wards, critical care centers, or long‐term care wards.1015 In these departments, patients with mental illness, postoperative delirium, or dementia are likely to be admitted.

Nurses and nursing aides are reported to be likely to experience physical aggression.3, 11, 15 Nurses and nursing aides have longer work hours with direct interaction with patients than other professions, and are considered to be at high risk of physical aggression.

Some studies, which did not examine influences by profession or department, reported that male was a risk factor for physical aggression and verbal abuse.6, 10, 11, 13 In this study though, the male gender was not associated with physical aggression or verbal abuse. Male nurses and nursing aides are likely to be assigned to high‐risk departments or to care for high‐risk patients. Gender may be confounded with variables such as profession or department.15

Longer work hours mean more frequent interaction with patients, and the risk for physical aggression might increase. Constant and direct interaction with patients is a risk factor, not only for physical aggression, but also for verbal abuse and sexual harassment.10

Risk Factors of Verbal Abuse

Previous studies reported that incidents of verbal abuse are high for nurses and nursing aides.15 Although in this study the proportion of verbal abuse was the highest in nurses (39.4%), adjusted odds ratio was not significant. Factors other than nurse profession, such as department and direct interaction with patients relating to the nurse profession, might be the higher risk factors for verbal abuse.

As reported in previous studies, psychiatric wards and long‐term care wards are risk factors of verbal abuse also in this study.10, 13, 15 Long waiting time at outpatient departments might increase the risk of verbal abuse.

Dialysis departments are at high risk of verbal abuse and sexual harassment. Compared to other outpatients, dialysis patients are forced to stay a long time in hospitals and there is, therefore, more interaction with nurses and technicians. The characteristic personality of dialysis patients, such as neuroticism or psychoticism, might also affect verbal abuse or sexual harassment.16

Because longer working hours mean more frequent interaction with patients, the risk of verbal abuse might increase.17 Managers with longer work experience might be at high risk for verbal abuse, because they often assume responsibility and take on the task of dealing with patients and their relevant complaints.11

Risk Factors of Sexual Harassment

Nurses are likely to experience sexual harassment because their public image seems to combine sexuality and maternalism.18 Nursing aides, technicians, and therapists also experience sexual harassment. The common risk factor among those professions is the direct contact with the patient's body during the patient's transfer. A lot of studies reported that female gender is a risk factor of sexual harassment.17, 19

Safety Factors of Workplace Violence

Adjusted odds ratios of operation departments, and obstetrics and gynecology wards, perinatal wards, or NICU were common safety factors for each type of workplace violence. Usually, the patients in operation rooms cannot talk or move a finger carelessly during the surgery. The fact that there are no adult male patients in obstetrics and gynecology wards, perinatal wards, or NICU, might have an influence on lower occurrence of workplace violence in these areas.

Limitations

This study used a questionnaire survey asking about subjective experiences of workplace violence over the 1 year before the study. There is the possibility of recall bias, and the same incident recognized as workplace violence by one person might not have been recognized as such by another person, because sensitivity differs among respondents.

In some categories with fewer respondents, such as the pharmaceutical department, it might be difficult to examine the exact confidence intervals of odds ratio. Further study with increased respondents of those categories is needed to confirm the odds ratios and the confidence intervals.

The rates of victims or risk for workplace violence were considered to vary with the character of the residents of each area or policy of each hospital. Therefore, a further analysis with adjustment for those factors is needed.

Factors Associated With Higher Risk of Violence

Adjusted odds ratios of physical aggression were significantly high in general wards, psychiatric wards, critical care centers/emntensive care units (ICU)/cardiac care units (CCU), and long‐term care wards; and for nurses, nursing aides/care workers; for longer working hours; and for direct interaction with patients (Table 3).

Adjusted odds ratios of verbal abuse were significantly high in hospital with 200 beds or more, in general wards, psychiatric wards, long‐term care wards, outpatient departments, and dialysis departments; and for longer years of experience in their own specialty; for longer working hours; and for direct interaction with patients.

Adjusted odds ratios of sexual harassment were significantly high in general wards, and dialysis departments; for nurses, nursing aides/care workers, technicians, therapists; for females; and for direct interaction with patients.

Adjusted odds ratios for at least 1 of the 3 kinds of workplace violence were significantly high in hospitals with 200 beds or more, in general wards, psychiatric wards, critical care centers/ICU/CCU, long‐term care wards, and dialysis departments; for nurses; for longer years of experience in their own specialty; for longer working hours; for females; and for direct interaction with patients.

Factors Associated With Lower Risk of Violence

Adjusted odds ratios of physical aggression were significantly low in dialysis departments; outpatient departments; operation departments; obstetrics and gynecology wards, perinatal wards, or neonatal intensive care units (NICU); and for clerks.

Adjusted odds ratios of verbal abuse were significantly low in operation departments; obstetrics and gynecology wards, perinatal wards, or NICU; and for technicians.

Adjusted odds ratios of sexual harassment were significantly low in clinical radiology departments; outpatient departments; pediatric wards; operation departments; obstetrics and gynecology wards, perinatal wards, or NICU; and for longer years of experience in their own specialty.

Adjusted odds ratios for at least 1 of the 3 kinds of workplace violence were significantly low in pediatric wards; operation departments; obstetrics and gynecology wards, perinatal wards, or NICU; and for technicians.

Incidence of Workplace Violence

Most previous studies covered only specific professions at hospitals, such as nurses or critical care center staff, and there are only few multicenter studies covering entire sections and professions like this study.510, 1214 A Spanish study including about 8000 healthcare workers reported that 11% had experienced physical aggression, 64% had experienced threatening behavior, intimidation, or insults in the past year.11 The incidence of physical aggression was similar in both studies, but the incidence of verbal abuse was about twice as high as that of this study. The low response rate in the Spanish study (24%) might have contributed to a higher number of verbal abuse incidents, because those with experience of workplace violence would likely have answered the questionnaire. It is difficult to compare the incidence of workplace violence among different studies because the definitions of workplace violence differ widely. Ethnic culture might also affect the acknowledgement of workplace violence. The European NEXT study including about 30,000 nurses in 8 European countries reported a range of 10.4% incidence of workplace violence in the Netherlands, and 39.1% in France.10

Risk Factors of Physical Aggression

Previous studies reported that the prevalence of physical aggression is high in psychiatric wards, critical care centers, or long‐term care wards.1015 In these departments, patients with mental illness, postoperative delirium, or dementia are likely to be admitted.

Nurses and nursing aides are reported to be likely to experience physical aggression.3, 11, 15 Nurses and nursing aides have longer work hours with direct interaction with patients than other professions, and are considered to be at high risk of physical aggression.

Some studies, which did not examine influences by profession or department, reported that male was a risk factor for physical aggression and verbal abuse.6, 10, 11, 13 In this study though, the male gender was not associated with physical aggression or verbal abuse. Male nurses and nursing aides are likely to be assigned to high‐risk departments or to care for high‐risk patients. Gender may be confounded with variables such as profession or department.15

Longer work hours mean more frequent interaction with patients, and the risk for physical aggression might increase. Constant and direct interaction with patients is a risk factor, not only for physical aggression, but also for verbal abuse and sexual harassment.10

Risk Factors of Verbal Abuse

Previous studies reported that incidents of verbal abuse are high for nurses and nursing aides.15 Although in this study the proportion of verbal abuse was the highest in nurses (39.4%), adjusted odds ratio was not significant. Factors other than nurse profession, such as department and direct interaction with patients relating to the nurse profession, might be the higher risk factors for verbal abuse.

As reported in previous studies, psychiatric wards and long‐term care wards are risk factors of verbal abuse also in this study.10, 13, 15 Long waiting time at outpatient departments might increase the risk of verbal abuse.

Dialysis departments are at high risk of verbal abuse and sexual harassment. Compared to other outpatients, dialysis patients are forced to stay a long time in hospitals and there is, therefore, more interaction with nurses and technicians. The characteristic personality of dialysis patients, such as neuroticism or psychoticism, might also affect verbal abuse or sexual harassment.16

Because longer working hours mean more frequent interaction with patients, the risk of verbal abuse might increase.17 Managers with longer work experience might be at high risk for verbal abuse, because they often assume responsibility and take on the task of dealing with patients and their relevant complaints.11

Risk Factors of Sexual Harassment

Nurses are likely to experience sexual harassment because their public image seems to combine sexuality and maternalism.18 Nursing aides, technicians, and therapists also experience sexual harassment. The common risk factor among those professions is the direct contact with the patient's body during the patient's transfer. A lot of studies reported that female gender is a risk factor of sexual harassment.17, 19

Safety Factors of Workplace Violence

Adjusted odds ratios of operation departments, and obstetrics and gynecology wards, perinatal wards, or NICU were common safety factors for each type of workplace violence. Usually, the patients in operation rooms cannot talk or move a finger carelessly during the surgery. The fact that there are no adult male patients in obstetrics and gynecology wards, perinatal wards, or NICU, might have an influence on lower occurrence of workplace violence in these areas.

Limitations

This study used a questionnaire survey asking about subjective experiences of workplace violence over the 1 year before the study. There is the possibility of recall bias, and the same incident recognized as workplace violence by one person might not have been recognized as such by another person, because sensitivity differs among respondents.

In some categories with fewer respondents, such as the pharmaceutical department, it might be difficult to examine the exact confidence intervals of odds ratio. Further study with increased respondents of those categories is needed to confirm the odds ratios and the confidence intervals.

The rates of victims or risk for workplace violence were considered to vary with the character of the residents of each area or policy of each hospital. Therefore, a further analysis with adjustment for those factors is needed.