Affiliations
Division of Community Internal Medicine, Mayo Clinic, Scottsdale, Arizona
Given name(s)
Daniel L.
Family name
Roberts
Degrees
MD

Teaching Cases Perception vs Reality

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Resident and hospitalist perspectives on the “great teaching case”: Correlation with actual patient assignment decisions

The advent of work‐hour restrictions and admission limits for teaching services has led many academic hospitals to implement hospitalist‐run staff (ie, nonteaching) services.[1] Although this practice is not new,[2] it is growing in popularity[3] and has been endorsed as a way to protect resident teaching and prevent excessive workload.[4] One potential benefit is the assignment of more educational cases to teaching services, whereas the nonteaching services receive more patients whose care is presumably relatively mundane or routine.[5]

Despite the rapid growth of this system of educational triage,[6] little is known about the factors considered when teaching versus nonteaching decisions are made. Studies of clinical outcomes for patients assigned to teaching versus nonteaching services have understandably used random assignment,[7, 8] whereas a study finding that patients with unhealthy substance use were more likely to be on teaching services than nonteaching services relied on patient assignment based on the identity of the patient's primary care provider or insurer.[9] In 2009, O'Connor et al. reported that implementation of nonteaching services at 2 hospitals had led to unequal distribution of patients in terms of demographics, diagnosis, and illness severity.[10] Triage decisions were made by either a nurse coordinator or a medical chief resident, and sicker patients (and occasionally good teaching cases) were preferentially placed on the teaching services, reportedly out of respect for the comfort level of the midlevel providers who staffed the nonteaching services.

Our institution has used a system of hospitalist educational triage since 1998. Over that time, residents have often expressed concerns about the assignment of patients to the teaching services, reporting in particular that they receive a disproportionate number of complex cases and outside transfers. In 2006, the hospitalist group attempted to address these concerns by collecting real‐time admission data, but the application of the data was limited by suspicion on both sides of a Hawthorne effect (data not published).

If trainee and hospitalist expectations for what constitutes a great teaching case differ substantially, that difference can have significant implications for resident and medical student teaching, self‐perceived roles, and satisfaction. More significantly, an understanding of what faculty perceive as ideal teaching cases would provide valuable information about the strengths and weaknesses of the teachingnonteaching model, which may prove useful to other academic institutions considering such a system. In this study, we endeavored to understand what residents and hospitalists consider an educational admission and to compare these expectations to the actual triage decisions of hospitalists.

METHODS

Mayo Clinic Hospital (Phoenix, Arizona) has used separate teaching and nonteaching services since opening in 1998. At our institution, like many others,[11] a hospitalist is assigned to take all calls for emergency department (ED) admissions, admissions from outpatient clinics, and transfer requests; this physician directs patients to the teaching or nonteaching service. At the time of our study, the 2 teaching services alternated days in which they admitted up to 7 patients, and the 5 nonteaching services admitted all other patients and provided medicine consultative services for the hospital. Teaching services consisted of 1 hospitalist, 2 senior residents, 2 or 3 first‐year residents, and sometimes 1 third‐ or fourth‐year medical student. Nonteaching services consisted of a hospitalist with intermittent assistance from a physician assistant or nurse practitioner.

Although there are no formal guidelines for the hospitalist triage role, hospitalists are encouraged to assign more educational cases to the teaching services and to allow the residents enough time to address the acute needs of the prior admission before receiving the next admission. Residents are not assigned any patients between 4:00 am and 7:00 am. The goals and objectives for the resident rotation on the medicine teaching service include a list of diagnoses with which residents are expected to become familiar during their residency; triage hospitalists have on‐line access to these goals and objectives.

To assess resident and hospitalist opinions about what types of patients should or should not be admitted to teaching services and to compare those characteristics with those of the patients actually admitted to teaching services, we began by administering a simple, open‐ended survey and asked both groups: (1) In an ideal world, what kinds of patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital? (2) In the real world, what kinds of patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital?

Ample space was provided for free‐text entries. Residents were additionally asked their postgraduate year level. The survey was administered in April 2011, at which time all residents would have rotated on the medicine teaching services several times. Survey responses were anonymous and were compiled and retyped by someone unfamiliar with the subjects' handwriting.

Two authors (D.L.R. and H.R.L.) reviewed the results of the first survey and used conventional content analysis to group responses into categories and tally them.[12] Responses from hospitalists and residents were used to determine the content for a second, quantitative survey that asked respondents to rate specific possible factors that affected triage decisions on a Likert scale from 1 (Argues against teaching admission) to 5 (Argues for teaching admission). The second survey, administered to the same residents and hospitalists in May 2011, asked: (1) In an ideal world, how do these factors contribute to the decision about which patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital? (2) In the real world, how do these factors contribute to the decision about which patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital?

Assuming a 3:1 ratio of nonteaching to teaching admissions, we calculated that we would need to analyze 1028 admissions to detect a 10% difference in the proportion of a specific trait present in 50% of patients admitted to the nonteaching service, with the use of a 2‐sided test with 80% statistical power and a significance level of 0.05.

We collected data on patient assignment via retrospective chart review to avoid the possibility of a Hawthorne effect. We studied all admissions to the internal medicine services for a 3‐month period before the administration of the first survey (January 1, 2011 through March 31, 2011). The following patient data were collected: service assignment (teaching vs nonteaching), age, sex, source of admission (ED, direct from clinic, outside transfer, internal transfer from another hospital service), first visit to our institution, prior hematology or oncology visit at our institution (as a surrogate for cancer), prior psychiatry visit at our institution (as a surrogate for psychiatric disease), transplantation history, human immunodeficiency virus (HIV) or acquired immune deficiency syndrome (AIDS) history, chronic or functional pain mentioned in ED or admission note, need for translator, and benefactor status. Additionally, an online calculator was used to determine the Charlson Comorbidity Index score for each patient.[13] We collected actual patient data corresponding to factors reported by survey respondents whenever possible and practical, but not every factor reported by survey respondents was amenable to rigorous analysis; for example, no unbiased method could be devised to rigorously categorize patients whose admissions are likely to take more time or difficult patients and families.

Responses to the second (quantitative) survey and patient data were compared using the Pearson [2] and Fisher exact test for categorical variables and the Student t test or Wilcoxon rank sum test for continuous variables. Categorical variables that achieved statistical significance for overall difference were analyzed on a post hoc basis using the Bonferroni method to control for the overall type I error rate. We also examined the differences between actual and ideal triage decisions using the Wilcoxon signed rank test. Data were analyzed using SAS 9.3 (SAS Institute, Inc., Cary, NC). Statistical significance was defined as P<0.05.

The project was deemed exempt by the Mayo Clinic institutional review board.

RESULTS

We surveyed all categorical internal medicine residents (n=30, 10 each from postgraduate year [PGY]‐1, PGY‐2, and PGY‐3) and hospitalists except the authors (n=21; average years since completing training=13.3; range, 129 years). For both surveys, responses were collected from 29 (96.7%) residents. The nonresponding resident was a PGY‐2. The response rate for hospitalists was 20/21 (95.2%) for the first survey and 16/21 (76.2%) for the second survey.

First Survey

Table 1 compares the most frequent resident and faculty responses to the initial, open‐ended survey about what types of patients should or should not be admitted to teaching services. Residents most commonly indicated that ideal patients were traditional medicine cases (ie, bread‐and‐butter admissions, with 13 residents using that exact phrase), and others supplied specific examples of such cases, including chronic obstructive pulmonary disease, pneumonia, diabetic ketoacidosis, congestive heart failure, chest pain, and gastrointestinal tract bleeding. Only 1 faculty member mentioned bread‐and‐butter admissions, although several listed examples like chest pain and pneumonia. A smaller number of residents pointed to the importance of rare cases, whereas faculty considered rare cases to be ideal for teaching services, followed by variety of pathology and complexity.

Most Frequent Resident and Faculty Responses to an Open‐Ended Survey About Types of Patients Admitted (Ideal vs Actual)
 Residents (n=29)Faculty (n=20)
QuestionCharacteristicNo. (%)CharacteristicNo. (%)
  • NOTE: Abbreviations: AIDS, acquired immunodeficiency syndrome; HIV, human immunodeficiency virus.

  • Similar responses were grouped via content analysis.

  • Specific examples cited include chronic obstructive pulmonary disease, pneumonia, diabetic ketoacidosis, congestive heart failure, chest pain, and gastrointestinal tract bleeding.

In an ideal world, what kinds of patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital?Bread‐and butter admissionsb14 (44.8)Rare cases9 (45.0)
Rare cases9 (31.0)Variety of pathology7 (35.0)
No social admissions7 (24.1)Complex cases5 (25.0)
New diagnoses instead of chronic management4 (13.8)Variety of complexity5 (25.0)
Variety of complexity4 (13.8)Patients with HIV/AIDS3 (15.0)
Diagnostic dilemmas3 (15.0)
New diagnoses instead of chronic management3 (15.0)
In the real world, what kinds of patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital?Patients with cancer11 (37.9)Complex patients6 (30.0)
Complex patients10 (34.5)Difficult patients5 (25.0)
Social admissions9 (31.0)Patients whose admissions are expected to be time consuming5 (25.0)
Acutely ill patients6 (20.7)Rare cases3 (15.0)
Variety of pathology6 (20.7)Cases determined by the time of day3 (15.0)

With regard to actual admissions, residents and faculty agreed that they often were complex, but residents were more likely to suggest high rates of patients with cancer (11 residents vs 2 hospitalists) and social admissions (9 residents vs 2 hospitalists). Four residents each believed that they preferentially received elderly patients, outside transfers, and patients with functional pain, and 2 perceived a disproportionate number of patients making their first visit to Mayo Clinic. One hospitalist believed that residents were more likely to receive non‐English speakers.

Second Survey

Table 2 compares the resident and faculty responses to the second, numerical survey regarding ideal admissions to the teaching services. In contrast to the first survey, residents prioritized rare cases as the feature they most associated with ideal teaching admissions. They also placed a premium on variety of pathology, patients with unique findings, and patients likely to be written up or presented. The patients they believed were least appropriate for a teaching service were social admissions or those with placement issues, patients with functional or chronic pain, and benefactors or public figures.

Resident and Faculty Survey Responses Regarding Ideal Admissions to Teaching Services
FactorResident, n=29Faculty, n=16P Value
  • NOTE: Participants rated each factor on a Likert scale from 1 (Argues against teaching admission) to 5 (Argues for teaching admission), with 3 representing No impact on admission decision. Abbreviations: AIDS, acquired immune deficiency syndrome; HIV, human immunodeficiency virus; SD, standard deviation.

Rare diseases  0.22
Mean (SD)4.8 (0.5)4.9 (0.3) 
Median55 
Variety of pathology  0.22
Mean (SD)4.7 (0.5)4.5 (0.5) 
Median55 
Cases that might be written up or presented  0.35
Mean (SD)4.7 (0.5)4.8 (0.6) 
Median55 
Bread‐and‐butter cases  0.001
Mean (SD)4.6 (0.7)3.7 (0.9) 
Median54 
Unique physical findings  0.67
Mean (SD)4.6 (0.6)4.7 (0.5) 
Median55 
Variety of complexity  0.21
Mean (SD)4.3 (0.7)4.1 (0.6) 
Median44 
Variety of acuity  0.40
Mean (SD)4.2 (0.7)4.1 (0.7) 
Median44 
Spectrum of ages  0.046
Mean (SD)4.1 (0.8)3.6 (0.8) 
Median43 
HIV or AIDS  0.39
Mean (SD)4.1 (0.9)4.4 (0.5) 
Median44 
Acutely ill or unstable  0.54
Mean (SD)4.0 (0.9)3.9 (0.6) 
Median44 
Complex patients  0.94
Mean (SD)4.0 (0.8)3.9 (0.6) 
Median44 
Patients at end of life  0.16
Mean (SD)3.5 (0.8)3.1 (0.6) 
Median33 
First‐time Mayo patients  0.45
Mean (SD)3.5 (0.7)3.3 (0.5) 
Median33 
Younger patients  0.50
Mean (SD)3.5 (0.9)3.3 (0.6) 
Median33 
Stable patients  0.21
Mean (SD)3.3 (0.8)3.1 (0.3) 
Median33 
Patients with cancer  0.67
Mean (SD)3.3 (0.8)3.1 (0.4) 
Median33 
Straightforward patients  0.64
Mean (SD)3.2 (0.8)3.1 (0.8) 
Median33 
Older patients  0.73
Mean (SD)3.2 (0.7)3.1 (0.3) 
Median33 
Patients with a history of transplantation  0.67
Mean (SD)3.1 (1.1)3.3 (0.6) 
Median33 
Time of day of admission  0.71
Mean (SD)3.1 (1.0)3.1 (0.5) 
Median33 
Patients with a history of psychiatric illness  0.59
Mean (SD)3.1 (1.0)3.1 (0.6) 
Median33 
Patients who require a translator  0.49
Mean (SD)3.0 (0.9)3.1 (0.5) 
Median33 
Patients whose admissions are expected to take more time  0.13
Mean (SD)2.9 (0.8)3.2 (0.6) 
Median33 
Difficult patients and families  0.55
Mean (SD)2.8 (1.0)2.6 (0.8) 
Median33 
Transfers from other hospitals  0.11
Mean (SD)2.7 (1.1)3.1 (0.3) 
Median33 
Benefactors and public figures  0.49
Mean (SD)2.7 (1.0)2.5 (0.7) 
Median33 
Patients with functional or chronic pain  0.87
Mean (SD)2.4 (1.1)2.4 (1.0)
Median23 
Social admissions or placement issues  0.99
Mean (SD)2.1 (1.1)2.0 (1.0) 
Median22 

Faculty prioritized many of the same features for ideal teaching cases as residents; 4 of their 5 highest‐scoring factors were the same (rare diseases, patients whose cases might be written up or presented, patients with unique physical findings, and variety of pathology). They also agreed on the least ideal features (social admissions or placement issues, patients with functional or chronic pain, and benefactors or public figures). The only significant differences between resident and faculty ratings for ideal teaching cases were for bread‐and‐butter cases and a spectrum of ages.

Discordance between resident and faculty survey responses on actual admission decisions (Table 3) was starker; residents rated several features significantly higher than faculty as features contributing to triage decisions including older patients; patients with functional or chronic pain, social admissions, or placement issues; patients with cancer; transfers from other hospitals; and difficult patients and families. Relative to residents, faculty reported that patients with HIV or AIDS, and patients whose cases were likely to be written up or presented, were more likely to be admitted to teaching services.

Resident and Faculty Survey Responses Regarding Actual Admissions to Teaching Services
FactorResident, n=29Faculty, n=16P Value
  • NOTE: Participants rated each factor on a Likert scale from 1 (Argues against teaching admission) to 5 (Argues for teaching admission), with 3 representing No impact on admission decision. Abbreviations: AIDS, acquired immune deficiency syndrome; HIV, human immunodeficiency virus; SD, standard deviation.

Rare diseases  0.14
Mean (SD)4.4 (0.6)4.7 (0.6) 
Median45 
Complex patients  0.83
Mean (SD)4.3 (0.6)4.3 (0.6) 
Median44 
Acutely ill or unstable  0.18
Mean (SD)4.3 (0.7)3.9 (0.9) 
Median44 
Unique physical findings  0.18
Mean (SD)4.1 (0.8)4.5 (0.6) 
Median45 
Transfers from other hospitals  0.003
Mean (SD)4.1 (1.0)3.5 (0.5) 
Median43 
Cases that might be written up or presented  0.03
Mean (SD)4.1 (0.7)4.6 (0.6) 
Median45 
Older patients  <0.001
Mean (SD)3.9 (0.8)3.0 (0.7) 
Median43 
Time of day of admission  0.50
Mean (SD)3.9 (1.1)3.7 (0.9) 
Median44 
Patients with cancer  0.01
Mean (SD)3.9 (0.9)3.3 (0.5) 
Median43 
Variety of pathology  0.21
Mean (SD)3.9 (0.8)4.2 (0.7) 
Median44 
Patients whose admissions are expected to take more time  0.13
Mean (SD)3.9 (1.0)3.4 (0.9) 
Median43 
HIV or AIDS  0.008
Mean (SD)3.8 (0.9)4.5 (0.5) 
Median44.5 
Variety of complexity  0.31
Mean (SD)3.7 (0.9)3.9 (0.6) 
Median3.54 
Bread‐and‐butter cases  0.07
Mean (SD)3.6 (1.0)2.9 (1.2) 
Median33 
First‐time Mayo patients  0.82
Mean (SD)3.6 (0.9)3.5 (0.7) 
Median33 
Patients with functional or chronic pain  0.004
Mean (SD)3.6 (1.0)2.8 (0.7) 
Median43 
Social admissions or placement issues  0.03
Mean (SD)3.5 (1.2)2.7 (0.9) 
Median43 
Variety of acuity  0.25
Mean (SD)3.5 (0.8)3.7 (0.6) 
Median34 
Difficult patients and families  0.03
Mean (SD)3.4 (0.9)2.8 (0.7) 
Median33 
Patients at end of life  0.10
Mean (SD)3.4 (0.8)3.0 (0.5) 
Median33 
Spectrum of ages  0.80
Mean (SD)3.3 (0.7)3.3 (0.6) 
Median33 
Patients with a history of psychiatric illness  0.81
Mean (SD)3.3 (0.9)3.1 (0.6) 
Median33 
Patients with a history of transplantation  0.25
Mean (SD)3.2 (0.9)3.5 (0.5) 
Median33 
Patients who require a translator  0.60
Mean (SD)3.2 (0.7)3.2 (0.6) 
Median33 
Younger patients  0.42
Mean (SD)3.0 (0.9)3.1 (0.4) 
Median33 
Benefactors and public figures  0.09
Mean (SD)2.9 (1.0)2.3 (0.7) 
Median32 
Straightforward patients  0.18
Mean (SD)2.8 (1.0)2.4 (1.0) 
Median2.52 
Stable patients  0.53
Mean (SD)2.7 (1.0)2.8 (0.7) 
Median33 

Comparing resident survey ratings for ideal versus actual triage decisions gave some insight into the features that they thought were inappropriately emphasized or ignored when triage decisions were made. Differences in resident scores for ideal versus actual admissions were significantly different for 16 of 28 items (data available upon request), suggesting a degree of perceived discordance. The largest positive differences (ie, features they valued in teaching admissions but thought were less represented in actual admissions) were for bread‐and‐butter admissions, variety of pathology, a spectrum of ages, and variety of acuity. The largest negative differences (ie, features they thought were well represented in actual admissions but were less valuable) were for social admissions or placement issues, transfers from other hospitals, patients with functional or chronic pain, and patients whose admissions were expected to take more time.

In terms of ideal versus actual triage decisions, faculty reported less discordance than residents; ideal and actual triage behavior differed significantly only for 4 of 28 items (data available upon request). They did agree with residents about the relative lack of bread‐and‐butter admissions and the over‐representation of social admissions or placement issues and transfers from other hospitals. They additionally noted a lack of straightforward cases.

We reviewed records of the 1426 patients admitted to the internal medicine services during the study period. Of these, 359 (25.2%) were assigned to the teaching services. Patient characteristics are summarized in Table 4.

Characteristics of Patients Admitted to the Internal Medicine Services (N=1,426)
CharacteristicTeaching Service, n=359Nonteaching Service, n=1,067P Value
  • NOTE: Abbreviations: SD, standard deviation.

Age, y, mean (SD)66.7 (16.5)69.3 (15.7)0.008
Admission type, No. (%)  0.049
Admission from the emergency department315 (87.7)915 (85.8)0.34
Direct admission from Mayo outpatient clinic27 (7.5)114 (10.7)0.08
Transfer from another institution16 (4.5)27 (2.5)0.06
Internal transfer from a different hospital service1 (0.3)11 (1.0)0.31
First‐time Mayo patient, No. (%)61 (17.0)175 (16.4)0.79
Prior hematology or oncology visit, No. (%)86 (24.0)235 (22.0)0.45
History of transplantation, No. (%)20 (5.6)52 (4.9)0.60
Prior psychiatry visit, No. (%)53 (14.8)122 (11.4)0.10
History of chronic or functional pain, No. (%)122 (34.0)330 (30.9)0.28
Required translator, No. (%)5 (1.4)14 (1.3)0.91
Benefactor, No. (%)5 (1.4)24 (2.2)0.32
Charlson comorbidity score, mean (SD)2.7 (2.5)2.6 (2.5)0.49

DISCUSSION

The results of our qualitative and quantitative surveys showed significant differences between resident and staff perceptions of the faculty triage role. Although both groups similarly valued many features, residents expressed a clear preference for more bread‐and‐butter admissions, whereas the staff prioritized selecting the most complex, challenging, and rare cases from among the day's admissions to give to the residents. (Residents were also very interested in rare cases, suggesting that they saw benefit to admitting patients with a variety of degrees of rarity and complexity.) Residents and faculty seemed to agree that the number of social admissions and outside transfers admitted to teaching services was not ideal.

These perceptions have substantial implications. If the current triage process is to continue, there may be benefit to designing a faculty development project focused on the triage process, which previously has been largely unexamined. Efforts to remove or limit time barriers that prevent perceived educational cases from being admitted to teaching services is also a worthy endeavor (eg, structuring the 2 teams to admit simultaneously so that teaching teams can admit patients back to back without exceeding capacity). In addition, residents may benefit from teaching hospitalists who concentrate educational efforts on the learning that can be extracted from the care of any patient, including admissions that initially seem mundane or purely social.[14] A concerted effort to divert more traditional medicine admissions and fewer unusual cases to the teaching service might improve resident perceptions of the triage process. Further, although the care of any patient can have education benefit, the fact that both groups perceived excessive social admissions in the teaching service suggests that a potential benefit of a nonteaching service (ie, absorbing the most mundane admissions) may not yet be fully realized.

Despite the perceived differences noted on the surveys, we found remarkably few differences between patients admitted to the teaching and nonteaching services. Although both groups rated complexity; outside transfers; being seen at the institution for the first time; and histories of transplantation, cancer, chronic or functional pain, and psychiatric disease as increasing the likelihood of admission to a teaching service, no differences were observed for these factors or their quantifiable surrogates. (Although the overall test for admission type achieved marginal statistical significance, none of the individual admission types were significantly different in post hoc analysis.) Residents, but not faculty, thought that older patients were over‐represented on the teaching service, but their assigned patients were significantly younger than those on the nonteaching service.

These findings have several possible explanations. First, although most hospitalists spend time on teaching and nonteaching services (and therefore are familiar with the patient composition of each), residents get very little exposure to the nonteaching services (until they are senior residents with a rotation on a consulting service). Their impression of inequity may be due to misunderstanding the patient composition of the nonteaching services. Second, the mere existence of a triage role may create false expectations about patient composition; that is, simply by knowing that every admission was chosen for its educational merit, residents may have disproportionate perceptions about those cases judged to have less educational value, even ifas our data suggestassignments to teaching versus nonteaching services are occurring fairly equitably.

Study Limitations

We acknowledge several limitations of our study. First, many factors that were reported as important in the qualitative survey did not lend themselves to objective abstraction from patient records. For example, providers did not specifically document when an admission is purely social, nor was there an objective way to identify difficult patients or families or admissions that were expected to take more time. We attempted to limit the analysis to objective patient metrics that were (1) not influenced by the teaching or nonteaching assignment itself (eg, we avoided discharge diagnoses, which might be entered differently by residents and staff hospitalists) and (2) easily available to triage hospitalists. For the latter reason, we used a prior appointment in the hematology or oncology clinic as a surrogate for cancer patients and a prior psychiatry visit as a surrogate for patients with a history of psychiatric disease. These are naturally inexact surrogates, but they reflect the information a busy hospitalist is likely to access when making patient assignment decisions.

Second, it may well be that assigning patients equitably according to a certain trait is not the same as assigning patients ideally for the educational needs of residents. The patients admitted to our medicine services (teaching and nonteaching) were generally older than 60 years, had complex diagnoses, and had substantial pain. Residents on the teaching services potentially would benefit from an intentionally unbalanced admission policy that shunted patients to the teaching services on the basis of features other than individual perceived educational merit. It must also be borne in mind that resident, and for that matter faculty, perceptions of ideal teaching cases are likely inexact correlates of educational best practices; the ideal role of the triage hospitalist is to admit to the teaching services those patients that will best advance the education of the learners, including a consideration of the goals and objectives of the rotation. Future studies correlating different triage practices to actual educational outcomes would be very helpful.

Third, the analysis could not reliably eliminate patients whose admissions did not represent genuine triage decisions (eg, those assigned to the hospitalist service after the teaching service had reached its capacity or immediately after they had received a complex case). Studying admission decisions prospectively could eliminate this variability, but it could introduce a Hawthorne effect, the negative effects of which likely would outweigh this benefit.

CONCLUSION

Triage hospitalists distributed patients fairly evenly between teaching and nonteaching services, but residents and faculty alike perceived that residents would benefit from more bread‐and‐butter cases. Hospitals considering the addition of a nonteaching service may want to incorporate a faculty development project focused on the triage process to ensure that these traditional medicine cases are assigned to resident services and to ensure that the great teaching case is not considered such because of complexity and acuity alone.

Acknowledgements

The authors thank Elizabeth Jones and Lois Bell for their assistance with survey collection and collation.

Disclosures: This study (institutional review board application #11‐3;002635) was deemed exempt by the Mayo Clinic institutional review board on May 16, 2011. The authors report no conflicts of interest.

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The advent of work‐hour restrictions and admission limits for teaching services has led many academic hospitals to implement hospitalist‐run staff (ie, nonteaching) services.[1] Although this practice is not new,[2] it is growing in popularity[3] and has been endorsed as a way to protect resident teaching and prevent excessive workload.[4] One potential benefit is the assignment of more educational cases to teaching services, whereas the nonteaching services receive more patients whose care is presumably relatively mundane or routine.[5]

Despite the rapid growth of this system of educational triage,[6] little is known about the factors considered when teaching versus nonteaching decisions are made. Studies of clinical outcomes for patients assigned to teaching versus nonteaching services have understandably used random assignment,[7, 8] whereas a study finding that patients with unhealthy substance use were more likely to be on teaching services than nonteaching services relied on patient assignment based on the identity of the patient's primary care provider or insurer.[9] In 2009, O'Connor et al. reported that implementation of nonteaching services at 2 hospitals had led to unequal distribution of patients in terms of demographics, diagnosis, and illness severity.[10] Triage decisions were made by either a nurse coordinator or a medical chief resident, and sicker patients (and occasionally good teaching cases) were preferentially placed on the teaching services, reportedly out of respect for the comfort level of the midlevel providers who staffed the nonteaching services.

Our institution has used a system of hospitalist educational triage since 1998. Over that time, residents have often expressed concerns about the assignment of patients to the teaching services, reporting in particular that they receive a disproportionate number of complex cases and outside transfers. In 2006, the hospitalist group attempted to address these concerns by collecting real‐time admission data, but the application of the data was limited by suspicion on both sides of a Hawthorne effect (data not published).

If trainee and hospitalist expectations for what constitutes a great teaching case differ substantially, that difference can have significant implications for resident and medical student teaching, self‐perceived roles, and satisfaction. More significantly, an understanding of what faculty perceive as ideal teaching cases would provide valuable information about the strengths and weaknesses of the teachingnonteaching model, which may prove useful to other academic institutions considering such a system. In this study, we endeavored to understand what residents and hospitalists consider an educational admission and to compare these expectations to the actual triage decisions of hospitalists.

METHODS

Mayo Clinic Hospital (Phoenix, Arizona) has used separate teaching and nonteaching services since opening in 1998. At our institution, like many others,[11] a hospitalist is assigned to take all calls for emergency department (ED) admissions, admissions from outpatient clinics, and transfer requests; this physician directs patients to the teaching or nonteaching service. At the time of our study, the 2 teaching services alternated days in which they admitted up to 7 patients, and the 5 nonteaching services admitted all other patients and provided medicine consultative services for the hospital. Teaching services consisted of 1 hospitalist, 2 senior residents, 2 or 3 first‐year residents, and sometimes 1 third‐ or fourth‐year medical student. Nonteaching services consisted of a hospitalist with intermittent assistance from a physician assistant or nurse practitioner.

Although there are no formal guidelines for the hospitalist triage role, hospitalists are encouraged to assign more educational cases to the teaching services and to allow the residents enough time to address the acute needs of the prior admission before receiving the next admission. Residents are not assigned any patients between 4:00 am and 7:00 am. The goals and objectives for the resident rotation on the medicine teaching service include a list of diagnoses with which residents are expected to become familiar during their residency; triage hospitalists have on‐line access to these goals and objectives.

To assess resident and hospitalist opinions about what types of patients should or should not be admitted to teaching services and to compare those characteristics with those of the patients actually admitted to teaching services, we began by administering a simple, open‐ended survey and asked both groups: (1) In an ideal world, what kinds of patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital? (2) In the real world, what kinds of patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital?

Ample space was provided for free‐text entries. Residents were additionally asked their postgraduate year level. The survey was administered in April 2011, at which time all residents would have rotated on the medicine teaching services several times. Survey responses were anonymous and were compiled and retyped by someone unfamiliar with the subjects' handwriting.

Two authors (D.L.R. and H.R.L.) reviewed the results of the first survey and used conventional content analysis to group responses into categories and tally them.[12] Responses from hospitalists and residents were used to determine the content for a second, quantitative survey that asked respondents to rate specific possible factors that affected triage decisions on a Likert scale from 1 (Argues against teaching admission) to 5 (Argues for teaching admission). The second survey, administered to the same residents and hospitalists in May 2011, asked: (1) In an ideal world, how do these factors contribute to the decision about which patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital? (2) In the real world, how do these factors contribute to the decision about which patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital?

Assuming a 3:1 ratio of nonteaching to teaching admissions, we calculated that we would need to analyze 1028 admissions to detect a 10% difference in the proportion of a specific trait present in 50% of patients admitted to the nonteaching service, with the use of a 2‐sided test with 80% statistical power and a significance level of 0.05.

We collected data on patient assignment via retrospective chart review to avoid the possibility of a Hawthorne effect. We studied all admissions to the internal medicine services for a 3‐month period before the administration of the first survey (January 1, 2011 through March 31, 2011). The following patient data were collected: service assignment (teaching vs nonteaching), age, sex, source of admission (ED, direct from clinic, outside transfer, internal transfer from another hospital service), first visit to our institution, prior hematology or oncology visit at our institution (as a surrogate for cancer), prior psychiatry visit at our institution (as a surrogate for psychiatric disease), transplantation history, human immunodeficiency virus (HIV) or acquired immune deficiency syndrome (AIDS) history, chronic or functional pain mentioned in ED or admission note, need for translator, and benefactor status. Additionally, an online calculator was used to determine the Charlson Comorbidity Index score for each patient.[13] We collected actual patient data corresponding to factors reported by survey respondents whenever possible and practical, but not every factor reported by survey respondents was amenable to rigorous analysis; for example, no unbiased method could be devised to rigorously categorize patients whose admissions are likely to take more time or difficult patients and families.

Responses to the second (quantitative) survey and patient data were compared using the Pearson [2] and Fisher exact test for categorical variables and the Student t test or Wilcoxon rank sum test for continuous variables. Categorical variables that achieved statistical significance for overall difference were analyzed on a post hoc basis using the Bonferroni method to control for the overall type I error rate. We also examined the differences between actual and ideal triage decisions using the Wilcoxon signed rank test. Data were analyzed using SAS 9.3 (SAS Institute, Inc., Cary, NC). Statistical significance was defined as P<0.05.

The project was deemed exempt by the Mayo Clinic institutional review board.

RESULTS

We surveyed all categorical internal medicine residents (n=30, 10 each from postgraduate year [PGY]‐1, PGY‐2, and PGY‐3) and hospitalists except the authors (n=21; average years since completing training=13.3; range, 129 years). For both surveys, responses were collected from 29 (96.7%) residents. The nonresponding resident was a PGY‐2. The response rate for hospitalists was 20/21 (95.2%) for the first survey and 16/21 (76.2%) for the second survey.

First Survey

Table 1 compares the most frequent resident and faculty responses to the initial, open‐ended survey about what types of patients should or should not be admitted to teaching services. Residents most commonly indicated that ideal patients were traditional medicine cases (ie, bread‐and‐butter admissions, with 13 residents using that exact phrase), and others supplied specific examples of such cases, including chronic obstructive pulmonary disease, pneumonia, diabetic ketoacidosis, congestive heart failure, chest pain, and gastrointestinal tract bleeding. Only 1 faculty member mentioned bread‐and‐butter admissions, although several listed examples like chest pain and pneumonia. A smaller number of residents pointed to the importance of rare cases, whereas faculty considered rare cases to be ideal for teaching services, followed by variety of pathology and complexity.

Most Frequent Resident and Faculty Responses to an Open‐Ended Survey About Types of Patients Admitted (Ideal vs Actual)
 Residents (n=29)Faculty (n=20)
QuestionCharacteristicNo. (%)CharacteristicNo. (%)
  • NOTE: Abbreviations: AIDS, acquired immunodeficiency syndrome; HIV, human immunodeficiency virus.

  • Similar responses were grouped via content analysis.

  • Specific examples cited include chronic obstructive pulmonary disease, pneumonia, diabetic ketoacidosis, congestive heart failure, chest pain, and gastrointestinal tract bleeding.

In an ideal world, what kinds of patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital?Bread‐and butter admissionsb14 (44.8)Rare cases9 (45.0)
Rare cases9 (31.0)Variety of pathology7 (35.0)
No social admissions7 (24.1)Complex cases5 (25.0)
New diagnoses instead of chronic management4 (13.8)Variety of complexity5 (25.0)
Variety of complexity4 (13.8)Patients with HIV/AIDS3 (15.0)
Diagnostic dilemmas3 (15.0)
New diagnoses instead of chronic management3 (15.0)
In the real world, what kinds of patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital?Patients with cancer11 (37.9)Complex patients6 (30.0)
Complex patients10 (34.5)Difficult patients5 (25.0)
Social admissions9 (31.0)Patients whose admissions are expected to be time consuming5 (25.0)
Acutely ill patients6 (20.7)Rare cases3 (15.0)
Variety of pathology6 (20.7)Cases determined by the time of day3 (15.0)

With regard to actual admissions, residents and faculty agreed that they often were complex, but residents were more likely to suggest high rates of patients with cancer (11 residents vs 2 hospitalists) and social admissions (9 residents vs 2 hospitalists). Four residents each believed that they preferentially received elderly patients, outside transfers, and patients with functional pain, and 2 perceived a disproportionate number of patients making their first visit to Mayo Clinic. One hospitalist believed that residents were more likely to receive non‐English speakers.

Second Survey

Table 2 compares the resident and faculty responses to the second, numerical survey regarding ideal admissions to the teaching services. In contrast to the first survey, residents prioritized rare cases as the feature they most associated with ideal teaching admissions. They also placed a premium on variety of pathology, patients with unique findings, and patients likely to be written up or presented. The patients they believed were least appropriate for a teaching service were social admissions or those with placement issues, patients with functional or chronic pain, and benefactors or public figures.

Resident and Faculty Survey Responses Regarding Ideal Admissions to Teaching Services
FactorResident, n=29Faculty, n=16P Value
  • NOTE: Participants rated each factor on a Likert scale from 1 (Argues against teaching admission) to 5 (Argues for teaching admission), with 3 representing No impact on admission decision. Abbreviations: AIDS, acquired immune deficiency syndrome; HIV, human immunodeficiency virus; SD, standard deviation.

Rare diseases  0.22
Mean (SD)4.8 (0.5)4.9 (0.3) 
Median55 
Variety of pathology  0.22
Mean (SD)4.7 (0.5)4.5 (0.5) 
Median55 
Cases that might be written up or presented  0.35
Mean (SD)4.7 (0.5)4.8 (0.6) 
Median55 
Bread‐and‐butter cases  0.001
Mean (SD)4.6 (0.7)3.7 (0.9) 
Median54 
Unique physical findings  0.67
Mean (SD)4.6 (0.6)4.7 (0.5) 
Median55 
Variety of complexity  0.21
Mean (SD)4.3 (0.7)4.1 (0.6) 
Median44 
Variety of acuity  0.40
Mean (SD)4.2 (0.7)4.1 (0.7) 
Median44 
Spectrum of ages  0.046
Mean (SD)4.1 (0.8)3.6 (0.8) 
Median43 
HIV or AIDS  0.39
Mean (SD)4.1 (0.9)4.4 (0.5) 
Median44 
Acutely ill or unstable  0.54
Mean (SD)4.0 (0.9)3.9 (0.6) 
Median44 
Complex patients  0.94
Mean (SD)4.0 (0.8)3.9 (0.6) 
Median44 
Patients at end of life  0.16
Mean (SD)3.5 (0.8)3.1 (0.6) 
Median33 
First‐time Mayo patients  0.45
Mean (SD)3.5 (0.7)3.3 (0.5) 
Median33 
Younger patients  0.50
Mean (SD)3.5 (0.9)3.3 (0.6) 
Median33 
Stable patients  0.21
Mean (SD)3.3 (0.8)3.1 (0.3) 
Median33 
Patients with cancer  0.67
Mean (SD)3.3 (0.8)3.1 (0.4) 
Median33 
Straightforward patients  0.64
Mean (SD)3.2 (0.8)3.1 (0.8) 
Median33 
Older patients  0.73
Mean (SD)3.2 (0.7)3.1 (0.3) 
Median33 
Patients with a history of transplantation  0.67
Mean (SD)3.1 (1.1)3.3 (0.6) 
Median33 
Time of day of admission  0.71
Mean (SD)3.1 (1.0)3.1 (0.5) 
Median33 
Patients with a history of psychiatric illness  0.59
Mean (SD)3.1 (1.0)3.1 (0.6) 
Median33 
Patients who require a translator  0.49
Mean (SD)3.0 (0.9)3.1 (0.5) 
Median33 
Patients whose admissions are expected to take more time  0.13
Mean (SD)2.9 (0.8)3.2 (0.6) 
Median33 
Difficult patients and families  0.55
Mean (SD)2.8 (1.0)2.6 (0.8) 
Median33 
Transfers from other hospitals  0.11
Mean (SD)2.7 (1.1)3.1 (0.3) 
Median33 
Benefactors and public figures  0.49
Mean (SD)2.7 (1.0)2.5 (0.7) 
Median33 
Patients with functional or chronic pain  0.87
Mean (SD)2.4 (1.1)2.4 (1.0)
Median23 
Social admissions or placement issues  0.99
Mean (SD)2.1 (1.1)2.0 (1.0) 
Median22 

Faculty prioritized many of the same features for ideal teaching cases as residents; 4 of their 5 highest‐scoring factors were the same (rare diseases, patients whose cases might be written up or presented, patients with unique physical findings, and variety of pathology). They also agreed on the least ideal features (social admissions or placement issues, patients with functional or chronic pain, and benefactors or public figures). The only significant differences between resident and faculty ratings for ideal teaching cases were for bread‐and‐butter cases and a spectrum of ages.

Discordance between resident and faculty survey responses on actual admission decisions (Table 3) was starker; residents rated several features significantly higher than faculty as features contributing to triage decisions including older patients; patients with functional or chronic pain, social admissions, or placement issues; patients with cancer; transfers from other hospitals; and difficult patients and families. Relative to residents, faculty reported that patients with HIV or AIDS, and patients whose cases were likely to be written up or presented, were more likely to be admitted to teaching services.

Resident and Faculty Survey Responses Regarding Actual Admissions to Teaching Services
FactorResident, n=29Faculty, n=16P Value
  • NOTE: Participants rated each factor on a Likert scale from 1 (Argues against teaching admission) to 5 (Argues for teaching admission), with 3 representing No impact on admission decision. Abbreviations: AIDS, acquired immune deficiency syndrome; HIV, human immunodeficiency virus; SD, standard deviation.

Rare diseases  0.14
Mean (SD)4.4 (0.6)4.7 (0.6) 
Median45 
Complex patients  0.83
Mean (SD)4.3 (0.6)4.3 (0.6) 
Median44 
Acutely ill or unstable  0.18
Mean (SD)4.3 (0.7)3.9 (0.9) 
Median44 
Unique physical findings  0.18
Mean (SD)4.1 (0.8)4.5 (0.6) 
Median45 
Transfers from other hospitals  0.003
Mean (SD)4.1 (1.0)3.5 (0.5) 
Median43 
Cases that might be written up or presented  0.03
Mean (SD)4.1 (0.7)4.6 (0.6) 
Median45 
Older patients  <0.001
Mean (SD)3.9 (0.8)3.0 (0.7) 
Median43 
Time of day of admission  0.50
Mean (SD)3.9 (1.1)3.7 (0.9) 
Median44 
Patients with cancer  0.01
Mean (SD)3.9 (0.9)3.3 (0.5) 
Median43 
Variety of pathology  0.21
Mean (SD)3.9 (0.8)4.2 (0.7) 
Median44 
Patients whose admissions are expected to take more time  0.13
Mean (SD)3.9 (1.0)3.4 (0.9) 
Median43 
HIV or AIDS  0.008
Mean (SD)3.8 (0.9)4.5 (0.5) 
Median44.5 
Variety of complexity  0.31
Mean (SD)3.7 (0.9)3.9 (0.6) 
Median3.54 
Bread‐and‐butter cases  0.07
Mean (SD)3.6 (1.0)2.9 (1.2) 
Median33 
First‐time Mayo patients  0.82
Mean (SD)3.6 (0.9)3.5 (0.7) 
Median33 
Patients with functional or chronic pain  0.004
Mean (SD)3.6 (1.0)2.8 (0.7) 
Median43 
Social admissions or placement issues  0.03
Mean (SD)3.5 (1.2)2.7 (0.9) 
Median43 
Variety of acuity  0.25
Mean (SD)3.5 (0.8)3.7 (0.6) 
Median34 
Difficult patients and families  0.03
Mean (SD)3.4 (0.9)2.8 (0.7) 
Median33 
Patients at end of life  0.10
Mean (SD)3.4 (0.8)3.0 (0.5) 
Median33 
Spectrum of ages  0.80
Mean (SD)3.3 (0.7)3.3 (0.6) 
Median33 
Patients with a history of psychiatric illness  0.81
Mean (SD)3.3 (0.9)3.1 (0.6) 
Median33 
Patients with a history of transplantation  0.25
Mean (SD)3.2 (0.9)3.5 (0.5) 
Median33 
Patients who require a translator  0.60
Mean (SD)3.2 (0.7)3.2 (0.6) 
Median33 
Younger patients  0.42
Mean (SD)3.0 (0.9)3.1 (0.4) 
Median33 
Benefactors and public figures  0.09
Mean (SD)2.9 (1.0)2.3 (0.7) 
Median32 
Straightforward patients  0.18
Mean (SD)2.8 (1.0)2.4 (1.0) 
Median2.52 
Stable patients  0.53
Mean (SD)2.7 (1.0)2.8 (0.7) 
Median33 

Comparing resident survey ratings for ideal versus actual triage decisions gave some insight into the features that they thought were inappropriately emphasized or ignored when triage decisions were made. Differences in resident scores for ideal versus actual admissions were significantly different for 16 of 28 items (data available upon request), suggesting a degree of perceived discordance. The largest positive differences (ie, features they valued in teaching admissions but thought were less represented in actual admissions) were for bread‐and‐butter admissions, variety of pathology, a spectrum of ages, and variety of acuity. The largest negative differences (ie, features they thought were well represented in actual admissions but were less valuable) were for social admissions or placement issues, transfers from other hospitals, patients with functional or chronic pain, and patients whose admissions were expected to take more time.

In terms of ideal versus actual triage decisions, faculty reported less discordance than residents; ideal and actual triage behavior differed significantly only for 4 of 28 items (data available upon request). They did agree with residents about the relative lack of bread‐and‐butter admissions and the over‐representation of social admissions or placement issues and transfers from other hospitals. They additionally noted a lack of straightforward cases.

We reviewed records of the 1426 patients admitted to the internal medicine services during the study period. Of these, 359 (25.2%) were assigned to the teaching services. Patient characteristics are summarized in Table 4.

Characteristics of Patients Admitted to the Internal Medicine Services (N=1,426)
CharacteristicTeaching Service, n=359Nonteaching Service, n=1,067P Value
  • NOTE: Abbreviations: SD, standard deviation.

Age, y, mean (SD)66.7 (16.5)69.3 (15.7)0.008
Admission type, No. (%)  0.049
Admission from the emergency department315 (87.7)915 (85.8)0.34
Direct admission from Mayo outpatient clinic27 (7.5)114 (10.7)0.08
Transfer from another institution16 (4.5)27 (2.5)0.06
Internal transfer from a different hospital service1 (0.3)11 (1.0)0.31
First‐time Mayo patient, No. (%)61 (17.0)175 (16.4)0.79
Prior hematology or oncology visit, No. (%)86 (24.0)235 (22.0)0.45
History of transplantation, No. (%)20 (5.6)52 (4.9)0.60
Prior psychiatry visit, No. (%)53 (14.8)122 (11.4)0.10
History of chronic or functional pain, No. (%)122 (34.0)330 (30.9)0.28
Required translator, No. (%)5 (1.4)14 (1.3)0.91
Benefactor, No. (%)5 (1.4)24 (2.2)0.32
Charlson comorbidity score, mean (SD)2.7 (2.5)2.6 (2.5)0.49

DISCUSSION

The results of our qualitative and quantitative surveys showed significant differences between resident and staff perceptions of the faculty triage role. Although both groups similarly valued many features, residents expressed a clear preference for more bread‐and‐butter admissions, whereas the staff prioritized selecting the most complex, challenging, and rare cases from among the day's admissions to give to the residents. (Residents were also very interested in rare cases, suggesting that they saw benefit to admitting patients with a variety of degrees of rarity and complexity.) Residents and faculty seemed to agree that the number of social admissions and outside transfers admitted to teaching services was not ideal.

These perceptions have substantial implications. If the current triage process is to continue, there may be benefit to designing a faculty development project focused on the triage process, which previously has been largely unexamined. Efforts to remove or limit time barriers that prevent perceived educational cases from being admitted to teaching services is also a worthy endeavor (eg, structuring the 2 teams to admit simultaneously so that teaching teams can admit patients back to back without exceeding capacity). In addition, residents may benefit from teaching hospitalists who concentrate educational efforts on the learning that can be extracted from the care of any patient, including admissions that initially seem mundane or purely social.[14] A concerted effort to divert more traditional medicine admissions and fewer unusual cases to the teaching service might improve resident perceptions of the triage process. Further, although the care of any patient can have education benefit, the fact that both groups perceived excessive social admissions in the teaching service suggests that a potential benefit of a nonteaching service (ie, absorbing the most mundane admissions) may not yet be fully realized.

Despite the perceived differences noted on the surveys, we found remarkably few differences between patients admitted to the teaching and nonteaching services. Although both groups rated complexity; outside transfers; being seen at the institution for the first time; and histories of transplantation, cancer, chronic or functional pain, and psychiatric disease as increasing the likelihood of admission to a teaching service, no differences were observed for these factors or their quantifiable surrogates. (Although the overall test for admission type achieved marginal statistical significance, none of the individual admission types were significantly different in post hoc analysis.) Residents, but not faculty, thought that older patients were over‐represented on the teaching service, but their assigned patients were significantly younger than those on the nonteaching service.

These findings have several possible explanations. First, although most hospitalists spend time on teaching and nonteaching services (and therefore are familiar with the patient composition of each), residents get very little exposure to the nonteaching services (until they are senior residents with a rotation on a consulting service). Their impression of inequity may be due to misunderstanding the patient composition of the nonteaching services. Second, the mere existence of a triage role may create false expectations about patient composition; that is, simply by knowing that every admission was chosen for its educational merit, residents may have disproportionate perceptions about those cases judged to have less educational value, even ifas our data suggestassignments to teaching versus nonteaching services are occurring fairly equitably.

Study Limitations

We acknowledge several limitations of our study. First, many factors that were reported as important in the qualitative survey did not lend themselves to objective abstraction from patient records. For example, providers did not specifically document when an admission is purely social, nor was there an objective way to identify difficult patients or families or admissions that were expected to take more time. We attempted to limit the analysis to objective patient metrics that were (1) not influenced by the teaching or nonteaching assignment itself (eg, we avoided discharge diagnoses, which might be entered differently by residents and staff hospitalists) and (2) easily available to triage hospitalists. For the latter reason, we used a prior appointment in the hematology or oncology clinic as a surrogate for cancer patients and a prior psychiatry visit as a surrogate for patients with a history of psychiatric disease. These are naturally inexact surrogates, but they reflect the information a busy hospitalist is likely to access when making patient assignment decisions.

Second, it may well be that assigning patients equitably according to a certain trait is not the same as assigning patients ideally for the educational needs of residents. The patients admitted to our medicine services (teaching and nonteaching) were generally older than 60 years, had complex diagnoses, and had substantial pain. Residents on the teaching services potentially would benefit from an intentionally unbalanced admission policy that shunted patients to the teaching services on the basis of features other than individual perceived educational merit. It must also be borne in mind that resident, and for that matter faculty, perceptions of ideal teaching cases are likely inexact correlates of educational best practices; the ideal role of the triage hospitalist is to admit to the teaching services those patients that will best advance the education of the learners, including a consideration of the goals and objectives of the rotation. Future studies correlating different triage practices to actual educational outcomes would be very helpful.

Third, the analysis could not reliably eliminate patients whose admissions did not represent genuine triage decisions (eg, those assigned to the hospitalist service after the teaching service had reached its capacity or immediately after they had received a complex case). Studying admission decisions prospectively could eliminate this variability, but it could introduce a Hawthorne effect, the negative effects of which likely would outweigh this benefit.

CONCLUSION

Triage hospitalists distributed patients fairly evenly between teaching and nonteaching services, but residents and faculty alike perceived that residents would benefit from more bread‐and‐butter cases. Hospitals considering the addition of a nonteaching service may want to incorporate a faculty development project focused on the triage process to ensure that these traditional medicine cases are assigned to resident services and to ensure that the great teaching case is not considered such because of complexity and acuity alone.

Acknowledgements

The authors thank Elizabeth Jones and Lois Bell for their assistance with survey collection and collation.

Disclosures: This study (institutional review board application #11‐3;002635) was deemed exempt by the Mayo Clinic institutional review board on May 16, 2011. The authors report no conflicts of interest.

The advent of work‐hour restrictions and admission limits for teaching services has led many academic hospitals to implement hospitalist‐run staff (ie, nonteaching) services.[1] Although this practice is not new,[2] it is growing in popularity[3] and has been endorsed as a way to protect resident teaching and prevent excessive workload.[4] One potential benefit is the assignment of more educational cases to teaching services, whereas the nonteaching services receive more patients whose care is presumably relatively mundane or routine.[5]

Despite the rapid growth of this system of educational triage,[6] little is known about the factors considered when teaching versus nonteaching decisions are made. Studies of clinical outcomes for patients assigned to teaching versus nonteaching services have understandably used random assignment,[7, 8] whereas a study finding that patients with unhealthy substance use were more likely to be on teaching services than nonteaching services relied on patient assignment based on the identity of the patient's primary care provider or insurer.[9] In 2009, O'Connor et al. reported that implementation of nonteaching services at 2 hospitals had led to unequal distribution of patients in terms of demographics, diagnosis, and illness severity.[10] Triage decisions were made by either a nurse coordinator or a medical chief resident, and sicker patients (and occasionally good teaching cases) were preferentially placed on the teaching services, reportedly out of respect for the comfort level of the midlevel providers who staffed the nonteaching services.

Our institution has used a system of hospitalist educational triage since 1998. Over that time, residents have often expressed concerns about the assignment of patients to the teaching services, reporting in particular that they receive a disproportionate number of complex cases and outside transfers. In 2006, the hospitalist group attempted to address these concerns by collecting real‐time admission data, but the application of the data was limited by suspicion on both sides of a Hawthorne effect (data not published).

If trainee and hospitalist expectations for what constitutes a great teaching case differ substantially, that difference can have significant implications for resident and medical student teaching, self‐perceived roles, and satisfaction. More significantly, an understanding of what faculty perceive as ideal teaching cases would provide valuable information about the strengths and weaknesses of the teachingnonteaching model, which may prove useful to other academic institutions considering such a system. In this study, we endeavored to understand what residents and hospitalists consider an educational admission and to compare these expectations to the actual triage decisions of hospitalists.

METHODS

Mayo Clinic Hospital (Phoenix, Arizona) has used separate teaching and nonteaching services since opening in 1998. At our institution, like many others,[11] a hospitalist is assigned to take all calls for emergency department (ED) admissions, admissions from outpatient clinics, and transfer requests; this physician directs patients to the teaching or nonteaching service. At the time of our study, the 2 teaching services alternated days in which they admitted up to 7 patients, and the 5 nonteaching services admitted all other patients and provided medicine consultative services for the hospital. Teaching services consisted of 1 hospitalist, 2 senior residents, 2 or 3 first‐year residents, and sometimes 1 third‐ or fourth‐year medical student. Nonteaching services consisted of a hospitalist with intermittent assistance from a physician assistant or nurse practitioner.

Although there are no formal guidelines for the hospitalist triage role, hospitalists are encouraged to assign more educational cases to the teaching services and to allow the residents enough time to address the acute needs of the prior admission before receiving the next admission. Residents are not assigned any patients between 4:00 am and 7:00 am. The goals and objectives for the resident rotation on the medicine teaching service include a list of diagnoses with which residents are expected to become familiar during their residency; triage hospitalists have on‐line access to these goals and objectives.

To assess resident and hospitalist opinions about what types of patients should or should not be admitted to teaching services and to compare those characteristics with those of the patients actually admitted to teaching services, we began by administering a simple, open‐ended survey and asked both groups: (1) In an ideal world, what kinds of patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital? (2) In the real world, what kinds of patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital?

Ample space was provided for free‐text entries. Residents were additionally asked their postgraduate year level. The survey was administered in April 2011, at which time all residents would have rotated on the medicine teaching services several times. Survey responses were anonymous and were compiled and retyped by someone unfamiliar with the subjects' handwriting.

Two authors (D.L.R. and H.R.L.) reviewed the results of the first survey and used conventional content analysis to group responses into categories and tally them.[12] Responses from hospitalists and residents were used to determine the content for a second, quantitative survey that asked respondents to rate specific possible factors that affected triage decisions on a Likert scale from 1 (Argues against teaching admission) to 5 (Argues for teaching admission). The second survey, administered to the same residents and hospitalists in May 2011, asked: (1) In an ideal world, how do these factors contribute to the decision about which patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital? (2) In the real world, how do these factors contribute to the decision about which patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital?

Assuming a 3:1 ratio of nonteaching to teaching admissions, we calculated that we would need to analyze 1028 admissions to detect a 10% difference in the proportion of a specific trait present in 50% of patients admitted to the nonteaching service, with the use of a 2‐sided test with 80% statistical power and a significance level of 0.05.

We collected data on patient assignment via retrospective chart review to avoid the possibility of a Hawthorne effect. We studied all admissions to the internal medicine services for a 3‐month period before the administration of the first survey (January 1, 2011 through March 31, 2011). The following patient data were collected: service assignment (teaching vs nonteaching), age, sex, source of admission (ED, direct from clinic, outside transfer, internal transfer from another hospital service), first visit to our institution, prior hematology or oncology visit at our institution (as a surrogate for cancer), prior psychiatry visit at our institution (as a surrogate for psychiatric disease), transplantation history, human immunodeficiency virus (HIV) or acquired immune deficiency syndrome (AIDS) history, chronic or functional pain mentioned in ED or admission note, need for translator, and benefactor status. Additionally, an online calculator was used to determine the Charlson Comorbidity Index score for each patient.[13] We collected actual patient data corresponding to factors reported by survey respondents whenever possible and practical, but not every factor reported by survey respondents was amenable to rigorous analysis; for example, no unbiased method could be devised to rigorously categorize patients whose admissions are likely to take more time or difficult patients and families.

Responses to the second (quantitative) survey and patient data were compared using the Pearson [2] and Fisher exact test for categorical variables and the Student t test or Wilcoxon rank sum test for continuous variables. Categorical variables that achieved statistical significance for overall difference were analyzed on a post hoc basis using the Bonferroni method to control for the overall type I error rate. We also examined the differences between actual and ideal triage decisions using the Wilcoxon signed rank test. Data were analyzed using SAS 9.3 (SAS Institute, Inc., Cary, NC). Statistical significance was defined as P<0.05.

The project was deemed exempt by the Mayo Clinic institutional review board.

RESULTS

We surveyed all categorical internal medicine residents (n=30, 10 each from postgraduate year [PGY]‐1, PGY‐2, and PGY‐3) and hospitalists except the authors (n=21; average years since completing training=13.3; range, 129 years). For both surveys, responses were collected from 29 (96.7%) residents. The nonresponding resident was a PGY‐2. The response rate for hospitalists was 20/21 (95.2%) for the first survey and 16/21 (76.2%) for the second survey.

First Survey

Table 1 compares the most frequent resident and faculty responses to the initial, open‐ended survey about what types of patients should or should not be admitted to teaching services. Residents most commonly indicated that ideal patients were traditional medicine cases (ie, bread‐and‐butter admissions, with 13 residents using that exact phrase), and others supplied specific examples of such cases, including chronic obstructive pulmonary disease, pneumonia, diabetic ketoacidosis, congestive heart failure, chest pain, and gastrointestinal tract bleeding. Only 1 faculty member mentioned bread‐and‐butter admissions, although several listed examples like chest pain and pneumonia. A smaller number of residents pointed to the importance of rare cases, whereas faculty considered rare cases to be ideal for teaching services, followed by variety of pathology and complexity.

Most Frequent Resident and Faculty Responses to an Open‐Ended Survey About Types of Patients Admitted (Ideal vs Actual)
 Residents (n=29)Faculty (n=20)
QuestionCharacteristicNo. (%)CharacteristicNo. (%)
  • NOTE: Abbreviations: AIDS, acquired immunodeficiency syndrome; HIV, human immunodeficiency virus.

  • Similar responses were grouped via content analysis.

  • Specific examples cited include chronic obstructive pulmonary disease, pneumonia, diabetic ketoacidosis, congestive heart failure, chest pain, and gastrointestinal tract bleeding.

In an ideal world, what kinds of patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital?Bread‐and butter admissionsb14 (44.8)Rare cases9 (45.0)
Rare cases9 (31.0)Variety of pathology7 (35.0)
No social admissions7 (24.1)Complex cases5 (25.0)
New diagnoses instead of chronic management4 (13.8)Variety of complexity5 (25.0)
Variety of complexity4 (13.8)Patients with HIV/AIDS3 (15.0)
Diagnostic dilemmas3 (15.0)
New diagnoses instead of chronic management3 (15.0)
In the real world, what kinds of patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital?Patients with cancer11 (37.9)Complex patients6 (30.0)
Complex patients10 (34.5)Difficult patients5 (25.0)
Social admissions9 (31.0)Patients whose admissions are expected to be time consuming5 (25.0)
Acutely ill patients6 (20.7)Rare cases3 (15.0)
Variety of pathology6 (20.7)Cases determined by the time of day3 (15.0)

With regard to actual admissions, residents and faculty agreed that they often were complex, but residents were more likely to suggest high rates of patients with cancer (11 residents vs 2 hospitalists) and social admissions (9 residents vs 2 hospitalists). Four residents each believed that they preferentially received elderly patients, outside transfers, and patients with functional pain, and 2 perceived a disproportionate number of patients making their first visit to Mayo Clinic. One hospitalist believed that residents were more likely to receive non‐English speakers.

Second Survey

Table 2 compares the resident and faculty responses to the second, numerical survey regarding ideal admissions to the teaching services. In contrast to the first survey, residents prioritized rare cases as the feature they most associated with ideal teaching admissions. They also placed a premium on variety of pathology, patients with unique findings, and patients likely to be written up or presented. The patients they believed were least appropriate for a teaching service were social admissions or those with placement issues, patients with functional or chronic pain, and benefactors or public figures.

Resident and Faculty Survey Responses Regarding Ideal Admissions to Teaching Services
FactorResident, n=29Faculty, n=16P Value
  • NOTE: Participants rated each factor on a Likert scale from 1 (Argues against teaching admission) to 5 (Argues for teaching admission), with 3 representing No impact on admission decision. Abbreviations: AIDS, acquired immune deficiency syndrome; HIV, human immunodeficiency virus; SD, standard deviation.

Rare diseases  0.22
Mean (SD)4.8 (0.5)4.9 (0.3) 
Median55 
Variety of pathology  0.22
Mean (SD)4.7 (0.5)4.5 (0.5) 
Median55 
Cases that might be written up or presented  0.35
Mean (SD)4.7 (0.5)4.8 (0.6) 
Median55 
Bread‐and‐butter cases  0.001
Mean (SD)4.6 (0.7)3.7 (0.9) 
Median54 
Unique physical findings  0.67
Mean (SD)4.6 (0.6)4.7 (0.5) 
Median55 
Variety of complexity  0.21
Mean (SD)4.3 (0.7)4.1 (0.6) 
Median44 
Variety of acuity  0.40
Mean (SD)4.2 (0.7)4.1 (0.7) 
Median44 
Spectrum of ages  0.046
Mean (SD)4.1 (0.8)3.6 (0.8) 
Median43 
HIV or AIDS  0.39
Mean (SD)4.1 (0.9)4.4 (0.5) 
Median44 
Acutely ill or unstable  0.54
Mean (SD)4.0 (0.9)3.9 (0.6) 
Median44 
Complex patients  0.94
Mean (SD)4.0 (0.8)3.9 (0.6) 
Median44 
Patients at end of life  0.16
Mean (SD)3.5 (0.8)3.1 (0.6) 
Median33 
First‐time Mayo patients  0.45
Mean (SD)3.5 (0.7)3.3 (0.5) 
Median33 
Younger patients  0.50
Mean (SD)3.5 (0.9)3.3 (0.6) 
Median33 
Stable patients  0.21
Mean (SD)3.3 (0.8)3.1 (0.3) 
Median33 
Patients with cancer  0.67
Mean (SD)3.3 (0.8)3.1 (0.4) 
Median33 
Straightforward patients  0.64
Mean (SD)3.2 (0.8)3.1 (0.8) 
Median33 
Older patients  0.73
Mean (SD)3.2 (0.7)3.1 (0.3) 
Median33 
Patients with a history of transplantation  0.67
Mean (SD)3.1 (1.1)3.3 (0.6) 
Median33 
Time of day of admission  0.71
Mean (SD)3.1 (1.0)3.1 (0.5) 
Median33 
Patients with a history of psychiatric illness  0.59
Mean (SD)3.1 (1.0)3.1 (0.6) 
Median33 
Patients who require a translator  0.49
Mean (SD)3.0 (0.9)3.1 (0.5) 
Median33 
Patients whose admissions are expected to take more time  0.13
Mean (SD)2.9 (0.8)3.2 (0.6) 
Median33 
Difficult patients and families  0.55
Mean (SD)2.8 (1.0)2.6 (0.8) 
Median33 
Transfers from other hospitals  0.11
Mean (SD)2.7 (1.1)3.1 (0.3) 
Median33 
Benefactors and public figures  0.49
Mean (SD)2.7 (1.0)2.5 (0.7) 
Median33 
Patients with functional or chronic pain  0.87
Mean (SD)2.4 (1.1)2.4 (1.0)
Median23 
Social admissions or placement issues  0.99
Mean (SD)2.1 (1.1)2.0 (1.0) 
Median22 

Faculty prioritized many of the same features for ideal teaching cases as residents; 4 of their 5 highest‐scoring factors were the same (rare diseases, patients whose cases might be written up or presented, patients with unique physical findings, and variety of pathology). They also agreed on the least ideal features (social admissions or placement issues, patients with functional or chronic pain, and benefactors or public figures). The only significant differences between resident and faculty ratings for ideal teaching cases were for bread‐and‐butter cases and a spectrum of ages.

Discordance between resident and faculty survey responses on actual admission decisions (Table 3) was starker; residents rated several features significantly higher than faculty as features contributing to triage decisions including older patients; patients with functional or chronic pain, social admissions, or placement issues; patients with cancer; transfers from other hospitals; and difficult patients and families. Relative to residents, faculty reported that patients with HIV or AIDS, and patients whose cases were likely to be written up or presented, were more likely to be admitted to teaching services.

Resident and Faculty Survey Responses Regarding Actual Admissions to Teaching Services
FactorResident, n=29Faculty, n=16P Value
  • NOTE: Participants rated each factor on a Likert scale from 1 (Argues against teaching admission) to 5 (Argues for teaching admission), with 3 representing No impact on admission decision. Abbreviations: AIDS, acquired immune deficiency syndrome; HIV, human immunodeficiency virus; SD, standard deviation.

Rare diseases  0.14
Mean (SD)4.4 (0.6)4.7 (0.6) 
Median45 
Complex patients  0.83
Mean (SD)4.3 (0.6)4.3 (0.6) 
Median44 
Acutely ill or unstable  0.18
Mean (SD)4.3 (0.7)3.9 (0.9) 
Median44 
Unique physical findings  0.18
Mean (SD)4.1 (0.8)4.5 (0.6) 
Median45 
Transfers from other hospitals  0.003
Mean (SD)4.1 (1.0)3.5 (0.5) 
Median43 
Cases that might be written up or presented  0.03
Mean (SD)4.1 (0.7)4.6 (0.6) 
Median45 
Older patients  <0.001
Mean (SD)3.9 (0.8)3.0 (0.7) 
Median43 
Time of day of admission  0.50
Mean (SD)3.9 (1.1)3.7 (0.9) 
Median44 
Patients with cancer  0.01
Mean (SD)3.9 (0.9)3.3 (0.5) 
Median43 
Variety of pathology  0.21
Mean (SD)3.9 (0.8)4.2 (0.7) 
Median44 
Patients whose admissions are expected to take more time  0.13
Mean (SD)3.9 (1.0)3.4 (0.9) 
Median43 
HIV or AIDS  0.008
Mean (SD)3.8 (0.9)4.5 (0.5) 
Median44.5 
Variety of complexity  0.31
Mean (SD)3.7 (0.9)3.9 (0.6) 
Median3.54 
Bread‐and‐butter cases  0.07
Mean (SD)3.6 (1.0)2.9 (1.2) 
Median33 
First‐time Mayo patients  0.82
Mean (SD)3.6 (0.9)3.5 (0.7) 
Median33 
Patients with functional or chronic pain  0.004
Mean (SD)3.6 (1.0)2.8 (0.7) 
Median43 
Social admissions or placement issues  0.03
Mean (SD)3.5 (1.2)2.7 (0.9) 
Median43 
Variety of acuity  0.25
Mean (SD)3.5 (0.8)3.7 (0.6) 
Median34 
Difficult patients and families  0.03
Mean (SD)3.4 (0.9)2.8 (0.7) 
Median33 
Patients at end of life  0.10
Mean (SD)3.4 (0.8)3.0 (0.5) 
Median33 
Spectrum of ages  0.80
Mean (SD)3.3 (0.7)3.3 (0.6) 
Median33 
Patients with a history of psychiatric illness  0.81
Mean (SD)3.3 (0.9)3.1 (0.6) 
Median33 
Patients with a history of transplantation  0.25
Mean (SD)3.2 (0.9)3.5 (0.5) 
Median33 
Patients who require a translator  0.60
Mean (SD)3.2 (0.7)3.2 (0.6) 
Median33 
Younger patients  0.42
Mean (SD)3.0 (0.9)3.1 (0.4) 
Median33 
Benefactors and public figures  0.09
Mean (SD)2.9 (1.0)2.3 (0.7) 
Median32 
Straightforward patients  0.18
Mean (SD)2.8 (1.0)2.4 (1.0) 
Median2.52 
Stable patients  0.53
Mean (SD)2.7 (1.0)2.8 (0.7) 
Median33 

Comparing resident survey ratings for ideal versus actual triage decisions gave some insight into the features that they thought were inappropriately emphasized or ignored when triage decisions were made. Differences in resident scores for ideal versus actual admissions were significantly different for 16 of 28 items (data available upon request), suggesting a degree of perceived discordance. The largest positive differences (ie, features they valued in teaching admissions but thought were less represented in actual admissions) were for bread‐and‐butter admissions, variety of pathology, a spectrum of ages, and variety of acuity. The largest negative differences (ie, features they thought were well represented in actual admissions but were less valuable) were for social admissions or placement issues, transfers from other hospitals, patients with functional or chronic pain, and patients whose admissions were expected to take more time.

In terms of ideal versus actual triage decisions, faculty reported less discordance than residents; ideal and actual triage behavior differed significantly only for 4 of 28 items (data available upon request). They did agree with residents about the relative lack of bread‐and‐butter admissions and the over‐representation of social admissions or placement issues and transfers from other hospitals. They additionally noted a lack of straightforward cases.

We reviewed records of the 1426 patients admitted to the internal medicine services during the study period. Of these, 359 (25.2%) were assigned to the teaching services. Patient characteristics are summarized in Table 4.

Characteristics of Patients Admitted to the Internal Medicine Services (N=1,426)
CharacteristicTeaching Service, n=359Nonteaching Service, n=1,067P Value
  • NOTE: Abbreviations: SD, standard deviation.

Age, y, mean (SD)66.7 (16.5)69.3 (15.7)0.008
Admission type, No. (%)  0.049
Admission from the emergency department315 (87.7)915 (85.8)0.34
Direct admission from Mayo outpatient clinic27 (7.5)114 (10.7)0.08
Transfer from another institution16 (4.5)27 (2.5)0.06
Internal transfer from a different hospital service1 (0.3)11 (1.0)0.31
First‐time Mayo patient, No. (%)61 (17.0)175 (16.4)0.79
Prior hematology or oncology visit, No. (%)86 (24.0)235 (22.0)0.45
History of transplantation, No. (%)20 (5.6)52 (4.9)0.60
Prior psychiatry visit, No. (%)53 (14.8)122 (11.4)0.10
History of chronic or functional pain, No. (%)122 (34.0)330 (30.9)0.28
Required translator, No. (%)5 (1.4)14 (1.3)0.91
Benefactor, No. (%)5 (1.4)24 (2.2)0.32
Charlson comorbidity score, mean (SD)2.7 (2.5)2.6 (2.5)0.49

DISCUSSION

The results of our qualitative and quantitative surveys showed significant differences between resident and staff perceptions of the faculty triage role. Although both groups similarly valued many features, residents expressed a clear preference for more bread‐and‐butter admissions, whereas the staff prioritized selecting the most complex, challenging, and rare cases from among the day's admissions to give to the residents. (Residents were also very interested in rare cases, suggesting that they saw benefit to admitting patients with a variety of degrees of rarity and complexity.) Residents and faculty seemed to agree that the number of social admissions and outside transfers admitted to teaching services was not ideal.

These perceptions have substantial implications. If the current triage process is to continue, there may be benefit to designing a faculty development project focused on the triage process, which previously has been largely unexamined. Efforts to remove or limit time barriers that prevent perceived educational cases from being admitted to teaching services is also a worthy endeavor (eg, structuring the 2 teams to admit simultaneously so that teaching teams can admit patients back to back without exceeding capacity). In addition, residents may benefit from teaching hospitalists who concentrate educational efforts on the learning that can be extracted from the care of any patient, including admissions that initially seem mundane or purely social.[14] A concerted effort to divert more traditional medicine admissions and fewer unusual cases to the teaching service might improve resident perceptions of the triage process. Further, although the care of any patient can have education benefit, the fact that both groups perceived excessive social admissions in the teaching service suggests that a potential benefit of a nonteaching service (ie, absorbing the most mundane admissions) may not yet be fully realized.

Despite the perceived differences noted on the surveys, we found remarkably few differences between patients admitted to the teaching and nonteaching services. Although both groups rated complexity; outside transfers; being seen at the institution for the first time; and histories of transplantation, cancer, chronic or functional pain, and psychiatric disease as increasing the likelihood of admission to a teaching service, no differences were observed for these factors or their quantifiable surrogates. (Although the overall test for admission type achieved marginal statistical significance, none of the individual admission types were significantly different in post hoc analysis.) Residents, but not faculty, thought that older patients were over‐represented on the teaching service, but their assigned patients were significantly younger than those on the nonteaching service.

These findings have several possible explanations. First, although most hospitalists spend time on teaching and nonteaching services (and therefore are familiar with the patient composition of each), residents get very little exposure to the nonteaching services (until they are senior residents with a rotation on a consulting service). Their impression of inequity may be due to misunderstanding the patient composition of the nonteaching services. Second, the mere existence of a triage role may create false expectations about patient composition; that is, simply by knowing that every admission was chosen for its educational merit, residents may have disproportionate perceptions about those cases judged to have less educational value, even ifas our data suggestassignments to teaching versus nonteaching services are occurring fairly equitably.

Study Limitations

We acknowledge several limitations of our study. First, many factors that were reported as important in the qualitative survey did not lend themselves to objective abstraction from patient records. For example, providers did not specifically document when an admission is purely social, nor was there an objective way to identify difficult patients or families or admissions that were expected to take more time. We attempted to limit the analysis to objective patient metrics that were (1) not influenced by the teaching or nonteaching assignment itself (eg, we avoided discharge diagnoses, which might be entered differently by residents and staff hospitalists) and (2) easily available to triage hospitalists. For the latter reason, we used a prior appointment in the hematology or oncology clinic as a surrogate for cancer patients and a prior psychiatry visit as a surrogate for patients with a history of psychiatric disease. These are naturally inexact surrogates, but they reflect the information a busy hospitalist is likely to access when making patient assignment decisions.

Second, it may well be that assigning patients equitably according to a certain trait is not the same as assigning patients ideally for the educational needs of residents. The patients admitted to our medicine services (teaching and nonteaching) were generally older than 60 years, had complex diagnoses, and had substantial pain. Residents on the teaching services potentially would benefit from an intentionally unbalanced admission policy that shunted patients to the teaching services on the basis of features other than individual perceived educational merit. It must also be borne in mind that resident, and for that matter faculty, perceptions of ideal teaching cases are likely inexact correlates of educational best practices; the ideal role of the triage hospitalist is to admit to the teaching services those patients that will best advance the education of the learners, including a consideration of the goals and objectives of the rotation. Future studies correlating different triage practices to actual educational outcomes would be very helpful.

Third, the analysis could not reliably eliminate patients whose admissions did not represent genuine triage decisions (eg, those assigned to the hospitalist service after the teaching service had reached its capacity or immediately after they had received a complex case). Studying admission decisions prospectively could eliminate this variability, but it could introduce a Hawthorne effect, the negative effects of which likely would outweigh this benefit.

CONCLUSION

Triage hospitalists distributed patients fairly evenly between teaching and nonteaching services, but residents and faculty alike perceived that residents would benefit from more bread‐and‐butter cases. Hospitals considering the addition of a nonteaching service may want to incorporate a faculty development project focused on the triage process to ensure that these traditional medicine cases are assigned to resident services and to ensure that the great teaching case is not considered such because of complexity and acuity alone.

Acknowledgements

The authors thank Elizabeth Jones and Lois Bell for their assistance with survey collection and collation.

Disclosures: This study (institutional review board application #11‐3;002635) was deemed exempt by the Mayo Clinic institutional review board on May 16, 2011. The authors report no conflicts of interest.

References
  1. Weinstein DF. Duty hours for resident physicians: tough choices for teaching hospitals. N Engl J Med. 2002;347(16):12751278.
  2. Simmer TL, Nerenz DR, Rutt WM, Newcomb CS, Benfer DW. A randomized, controlled trial of an attending staff service in general internal medicine. Med Care. 1991;29(7 suppl):JS31JS40.
  3. Sehgal NL, Shah HM, Parekh , Roy CL, Williams MV. Non‐housestaff medicine services in academic centers: models and challenges. J Hosp Med. 2008;3(3):247255.
  4. Weinberger SE, Smith LG, Collier VU; Education Committee of the American College of Physicians. Redesigning training for internal medicine. Ann Intern Med. 2006;144(12):927932.
  5. Myers JS, Bellini LM, Rohrbach J, Shofer FS, Hollander JE. Improving resource utilization in a teaching hospital: development of a nonteaching service for chest pain admissions. Acad Med. 2006;81(5):432435.
  6. Howell EE, Bessman ES, Rubin HR. Hospitalists and an innovative emergency department admission process. J Gen Intern Med. 2004;19(3):266268.
  7. Khaliq AA, Huang CY, Ganti AK, Invie K, Smego RA. Comparison of resource utilization and clinical outcomes between teaching and nonteaching medical services. J Hosp Med. 2007;2(3):150157.
  8. Palacio C, Alexandraki I, House J, Mooradian AD. A comparative study of unscheduled hospital readmissions in a resident‐staffed teaching service and a hospitalist‐based service. South Med J. 2009;102(2):145149.
  9. Holt SR, Ramos J, Harma MA, et al. Prevalence of unhealthy substance use on teaching and hospitalist medical services: implications for education. Am J Addict. 2012;21(2):111119.
  10. O'Connor AB, Lang VJ, Lurie SJ, et al. The effect of nonteaching services on the distribution of inpatient cases for internal medicine residents. Acad Med. 2009;84(2):220225.
  11. Darves B. Teaching and nonteaching services: separate no more? Today's Hospitalist website. Available at: http://www.todayshospita list.com/index.php?b=articles_read15(9):12771288.
  12. Charlson comorbidity scoring system: estimating prognosis for dialysis patients. Touchcalc website. Available at: http://www.touchcalc.com/calculators/cci_js#t2_probability. Accessed January 15, 2014.
  13. Fitzgerald FT. Curiosity. Ann Intern Med. 1999;130(1):7071.
References
  1. Weinstein DF. Duty hours for resident physicians: tough choices for teaching hospitals. N Engl J Med. 2002;347(16):12751278.
  2. Simmer TL, Nerenz DR, Rutt WM, Newcomb CS, Benfer DW. A randomized, controlled trial of an attending staff service in general internal medicine. Med Care. 1991;29(7 suppl):JS31JS40.
  3. Sehgal NL, Shah HM, Parekh , Roy CL, Williams MV. Non‐housestaff medicine services in academic centers: models and challenges. J Hosp Med. 2008;3(3):247255.
  4. Weinberger SE, Smith LG, Collier VU; Education Committee of the American College of Physicians. Redesigning training for internal medicine. Ann Intern Med. 2006;144(12):927932.
  5. Myers JS, Bellini LM, Rohrbach J, Shofer FS, Hollander JE. Improving resource utilization in a teaching hospital: development of a nonteaching service for chest pain admissions. Acad Med. 2006;81(5):432435.
  6. Howell EE, Bessman ES, Rubin HR. Hospitalists and an innovative emergency department admission process. J Gen Intern Med. 2004;19(3):266268.
  7. Khaliq AA, Huang CY, Ganti AK, Invie K, Smego RA. Comparison of resource utilization and clinical outcomes between teaching and nonteaching medical services. J Hosp Med. 2007;2(3):150157.
  8. Palacio C, Alexandraki I, House J, Mooradian AD. A comparative study of unscheduled hospital readmissions in a resident‐staffed teaching service and a hospitalist‐based service. South Med J. 2009;102(2):145149.
  9. Holt SR, Ramos J, Harma MA, et al. Prevalence of unhealthy substance use on teaching and hospitalist medical services: implications for education. Am J Addict. 2012;21(2):111119.
  10. O'Connor AB, Lang VJ, Lurie SJ, et al. The effect of nonteaching services on the distribution of inpatient cases for internal medicine residents. Acad Med. 2009;84(2):220225.
  11. Darves B. Teaching and nonteaching services: separate no more? Today's Hospitalist website. Available at: http://www.todayshospita list.com/index.php?b=articles_read15(9):12771288.
  12. Charlson comorbidity scoring system: estimating prognosis for dialysis patients. Touchcalc website. Available at: http://www.touchcalc.com/calculators/cci_js#t2_probability. Accessed January 15, 2014.
  13. Fitzgerald FT. Curiosity. Ann Intern Med. 1999;130(1):7071.
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Address for correspondence and reprint requests: Daniel L. Roberts, MD, Division of Hospital Internal Medicine, Mayo Clinic Hospital, 5777 East Mayo Boulevard, Phoenix, AZ 85054; Telephone: 480‐342‐1387; Fax: 480‐342‐1388; E‐mail: roberts.daniel@mayo.edu
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Burnout and Work‐Life Balance

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A national comparison of burnout and work‐life balance among internal medicine hospitalists and outpatient general internists

An increasingly robust body of literature has identified burnout as a substantial problem for physicians across specialties and practice settings.[1, 2, 3, 4] Burnout, a work‐related condition characterized by emotional exhaustion, depersonalization, and lack of a sense of personal accomplishment,[5] has been tied to negative consequences for patients, physicians, and the medical profession including medical errors,[6] poor physician health,[7, 8] and decreased professionalism.[9] Studies of burnout among general internists have pointed to time pressures, lack of work control, and difficult patient encounters as possible contributors.[10, 11]

Burnout has been demonstrated to affect a sizable proportion of hospitalists, with prevalence estimates from prior studies varying from 12.9% to 27.2%, although nearly all studies of US hospitalists have relied on single‐item instruments.[12, 13, 14, 15] Hospital‐based physicians have represented a rapidly expanding segment of the internist workforce for more than a decade,[14] but studies of the impact of inpatient vs outpatient practice location on burnout and career satisfaction are limited. A meta‐analysis of the impact of practice location on burnout relied almost exclusively on noncomparative studies from outside the United States.[15] A recent study of US physician burnout and satisfaction with work‐life balance showed that general internists expressed below average satisfaction with work‐life balance and had the second highest rate of burnout among 24 specialties.[4] However, this report did not differentiate between general internists working in inpatient vs outpatient settings.

We therefore examined burnout, satisfaction with work‐life balance, and other aspects of well‐being among internal medicine hospitalists relative to outpatient general internists, using a national sample developed in partnership with the American Medical Association.

METHODS

Physician Sample

As described previously,[4] the American Medical Association Physician Masterfile, a nearly complete record of US physicians, was used to generate a sample of physicians inclusive of all specialty disciplines. The 27,276 physicians who opened at least 1 invitation e‐mail were considered to have received the invitation to participate in the study. Participation was voluntary, and all responses were anonymous. For this analysis, internal medicine hospitalists were compared with general internists reporting primarily outpatient practices. The physician sample provided information on demographics (age, sex, and relationship status) and on characteristics of their practice. Burnout, symptoms of depression, suicidal ideation in the past 12 months, quality of life (QOL), satisfaction with work‐life balance, and certain health behaviors were evaluated as detailed below.

Burnout

Burnout among physicians was measured using the Maslach Burnout Inventory (MBI), a validated 22‐item questionnaire considered the gold standard tool for measuring burnout.[5, 16] The MBI has subscales to evaluate each domain of burnout: emotional exhaustion, depersonalization, and low personal accomplishment. Because other burnout studies have focused on the presence of high levels of emotional exhaustion or depersonalization as the foundation of burnout in physicians,[17, 18, 19] we considered physicians with a high score on the depersonalization or emotional exhaustion subscales to have at least 1 manifestation of professional burnout.

Symptoms of Depression and Suicidal Ideation

Symptoms of depression were assessed using the 2‐item Primary Care Evaluation of Mental Disorders,[20] a standardized and validated assessment for depression screening that performs as well as longer instruments.[21] Recent suicidal ideation was evaluated by asking participants, During the past 12 months, have you had thoughts of taking your own life? This item was designed to measure somewhat recent, but not necessarily active, suicidal ideation. These questions have been used extensively in other studies.[22, 23, 24, 25]

Quality of Life and Fatigue

Overall QOL and mental, physical, and emotional QOL were measured by a single‐item linear analog scale assessment. This instrument measured QOL on a 0 (as bad as it can be) to 10 (as good as it can be) scale validated across a wide range of medical conditions and populations.[26, 27, 28] Fatigue was measured using a similar standardized linear analog scale assessment question, for which respondents indicated their level of fatigue during the past week.[29] The impact of fatigue on daily activities such as driving was also evaluated.

Satisfaction With Work‐Life Balance and Career Plans

Satisfaction with work‐life balance was assessed by the item, My work schedule leaves me enough time for my personal/family life, with response options strongly agree, agree, neutral, disagree, or strongly disagree. Individuals who indicated strongly agree or agree were considered to be satisfied with their work‐life balance, whereas those who indicated strongly disagree or disagree were considered to be dissatisfied with their work‐life balance. Experience of work‐home conflicts was assessed as in prior research.[4] Participants were also asked about plans to change jobs or careers.

Health Behaviors

A limited set of health and wellness behaviors was addressed in the survey to provide insight into other aspects of physician well‐being. These included whether respondents had a primary care provider and questions concerning routine screening and alcohol and substance use. Alcohol use was assessed using the Alcohol Use Disorders Identification Test, version C (AUDIT‐C).[30] An AUDIT‐C score of at least 4 for men and at least 3 for women indicates alcohol misuse, and a score of at least 5 for men and at least 4 for women indicates alcohol abuse and possible dependence.[30]

Statistical Analysis

Standard descriptive summary statistics were used to characterize the physician samples. Associations between variables were evaluated using the Kruskal‐Wallis test (for continuous variables) or [2] test (for categorical variables). All tests were 2‐sided, with a type I error level of 0.05. Multivariate analysis of differences between hospitalists and outpatient general internists was performed using multiple linear or logistic regression for continuous or categorical data, respectively. Covariates in these models included age, sex, weekly work hours, and practice setting. All of the analyses were performed using SAS version 9.2 (SAS Institute, Inc., Cary, NC).

RESULTS

In the full survey across all specialties, 7288 physicians (26.7%) provided survey responses.[4] There were 448 outpatient internists and 130 internal medicine hospitalists who agreed to participate. Demographically, hospitalists were younger, worked longer hours, and were less likely to work in private practice than outpatient general internists (Table 1).

Demographics of Responding Internal Medicine Hospitalists and Outpatient General Internal Medicine Physicians
CharacteristicHospitalists (n=130)Outpatient General Internists (n=448)P
  • NOTE: Abbreviations: SD, standard deviation.

Sex, n (%)  0.56
Male86 (66.2%)284 (63.4%) 
Female44 (33.8%)164 (36.6%) 
Age, mean (SD)46.9 (12.4)53.6 (10.2)<0.001
Median45.055.0 
Years in practice, mean (SD)14.0 (12.0)21.6 (10.7)<0.001
Median10.022.0 
Hours worked per week, mean (SD)55.0 (18.1)50.0 (15.1)0.04
Median50.050.0 
Practice setting, n (%)  <0.001
Private practice/hospital36 (31.0%)303 (69.2%) 
Academic medical center37 (31.9%)41 (9.4%) 
Other (including veterans hospital and active military practice)43 (37.1%)94 (21.5%) 

Distress and Well‐Being Variables

High levels of emotional exhaustion affected 43.8% of hospitalists and 48.1% of outpatient general internists (odds ratio [OR]: 0.91, 95% confidence interval [CI]: 0.56‐1.48), and high levels of depersonalization affected 42.3% of hospitalists and 32.7% of outpatient general internists (OR: 1.42, 95% CI: 0.86‐2.35). Overall burnout affected 52.3% of hospitalists and 54.5% of outpatient general internists (OR: 0.96, 95% CI: 0.58‐1.57). None of these factors differed statistically in multivariate models adjusted for factors known to be associated with burnout, including sex, age, weekly work hours, and practice setting (P=0.71, 0.17, and 0.86, respectively; Table 2). However, low levels of personal accomplishment were reported by 20.3% of hospitalists and 9.6% of outpatient general internists (OR: 1.93, 95% CI: 1.023.65, P=0.04).

Distress and Well‐Being Results for Internal Medicine Hospitalists vs Outpatient General Internists
VariableHospitalists (n=130)Outpatient General Internists (n=448)Pa
  • NOTE: Abbreviations: DP, depersonalization; EE, emotional exhaustion; SD, standard deviation.

  • Adjusted for age, sex, weekly work hours, and practice setting.

Burnout   
Emotional exhaustion high (27)57/130 (43.8%)215/447 (48.1%)0.71
Mean (SD)24.7 (12.5)25.4 (14.0) 
Median24.926.0 
Depersonalization high (10)55/130 (42.3%)146/447 (32.7%)0.17
Mean (SD)9.1 (6.9)7.5 (6.3) 
Median7.06.0 
Personal accomplishment low (33)26/128 (20.3%)43/446 (9.6%)0.04
Mean (SD)39.0 (7.6)41.4 (6.0) 
Median41.043.0 
High burnout (EE27 or DP10)68/130 (52.3%)244/448 (54.5%)0.86
Depression   
Depression screen +52/129 (40.3%)176/440 (40.0%)0.73
Suicidal thoughts in past 12 months12/130 (9.2%)26/445 (5.8%)0.15
Quality of life   
Overall mean (SD)7.3 (2.0)7.4 (1.8)0.85
Median8.08.0 
Low (<6)21/130 (16.2%)73/448 (16.3%) 
Mental mean (SD)7.2 (2.1)7.3 (2.0)0.89
Median8.08.0 
Low (<6)23/130 (17.7%)92/448 (20.5%) 
Physical mean (SD)6.7 (2.3)6.9 (2.1)0.45
Median7.07.0 
Low (<6)35/130 (26.9%)106/448 (23.7%) 
Emotional mean (SD)7.0 (2.3)6.9 (2.2)0.37
Median7.07.0 
Low (<6)30/130 (23.1%)114/448 (25.4%) 
Fatigue   
Mean (SD)5.8 (2.4)5.9 (2.4)0.57
Median6.06.0 
Fallen asleep while driving (among regular drivers only)11/126 (8.7%)19/438 (4.3%)0.23

Approximately 40% of physicians in both groups screened positive for depression (OR: 0.92, 95% CI: 0.56‐1.51, P=0.73). In addition, 9.2% of hospitalists reported suicidal ideation in the last 12 months compared to 5.8% of outpatient internists (OR: 1.86, 95% CI: 0.80‐4.33, P=0.15) (Table 2).

Overall QOL and QOL in mental, physical, and emotional domains were nearly identical in the 2 groups (Table 2). Fatigue was also similar for hospitalists and outpatient general internists, and 8.5% of hospitalists reported falling asleep in traffic while driving compared to 4.2% of outpatient internists (OR: 1.76, 95% CI: 0.70‐4.44, P=0.23).

Work‐Life Balance and Career Variables

Experience of recent work‐home conflicts was similar for hospitalists and outpatient general internists (Table 3). However, hospitalists were more likely to agree or strongly agree that their work schedule leaves enough time for their personal life and family (50.0% vs 42.0%, OR: 2.06, 95% CI: 1.22‐3.47, P=0.007).

Work‐Life Balance and Career Variables for Internal Medicine Hospitalists vs Outpatient General Internists
VariableHospitalists (n=130)Outpatient General Internists (n=448)Pa
  • NOTE: Adjusted for age, sex, weekly work hours, and practice setting.

Work‐home conflict in last 3 weeks62/128 (48.4%)183/443 (41.3%)0.64
Work‐home conflict resolved in favor of:  0.79
Work37/118 (31.4%)131/405 (32.2%) 
Home15/118 (12.7%)43/405 (10.6%) 
Meeting both needs66/118 (55.9%)231/405 (57.0%) 
Work schedule leaves enough time for personal life/family  0.007
Strongly agree20 (15.4%)70 (15.7%) 
Agree45 (34.6%)117 (26.3%) 
Neutral21 (16.2%)66 (14.8%) 
Disagree27 (20.8%)119 (26.7%) 
Strongly disagree17 (13.1%)73 (16.4%) 
Missing03 
Likelihood of leaving current practice  0.002
Definite17 (13.1%)34 (7.6%) 
Likely21 (16.2%)53 (11.9%) 
Moderate21 (16.2%)67 (15.0%) 
Slight38 (29.2%)128 (28.7%) 
None33 (25.4%)164 (36.8%) 
Missing02 
Would choose to become physician again81/130 (62.3%)306/441 (69.4%)0.86

Hospitalists were more likely to express interest in leaving their current practice in the next 2 years, with 13.1% vs 7.6% reporting definite plans to leave and 29.2% vs 19.5% reporting at least likely plans to leave (OR: 2.31, 95% CI: 1.35‐3.97, P=0.002). Among those reporting a likely or definite plan to leave, hospitalists were more likely to plan to look for a different practice and continue to work as a physician (63.2% vs 39.1%), whereas outpatient general internists were more likely to plan to leave medical practice (51.9% vs 22.0%, P=0.004). Hospitalists with plans to reduce their work hours were more likely than their outpatient colleagues to express an interest in administrative and leadership roles (19.4% vs 12.1%) or research and educational roles (9.7% vs 4.0%, P=0.05).

Health Behavior Variables

Hospitalists were less likely to report having a primary care provider in the adjusted analyses (55.0% vs 70.3%, OR: 0.49, 95% CI: 0.29‐0.83, P=0.008). Use of illicit substances was uncommon in both groups (94.6% of hospitalists and 96.0% of outpatient general internists reported never using an illicit substance (OR: 0.87, 95% CI: 0.31‐2.49, P=0.80). Symptoms of alcohol abuse were similar between the 2 groups (11.7% and 13.3%, respectively, OR: 0.64, 95% CI: 0.30‐1.35, P=0.24), but symptoms of alcohol misuse were more common among outpatient general internists (34.2% vs 21.9%, OR: 1.75, 95% CI: 1.013.03, P=0.047).

DISCUSSION

The primary result of this national study applying well‐validated metrics is that the overall rates of burnout among hospitalists and outpatient general internal medicine physicians were similar, as were rates of positive depression screening and QOL. Although these groups did not differ, the absolute rates of distress found in this study were high. Prior research has suggested that possible explanations for these high rates of distress include excessive workload, loss of work‐associated control and meaning, and difficulties with work‐home balance.[4] The present study, in the context of prior work showing that general internists have higher rates of burnout than almost any other specialty, suggests that the front‐line nature of the work of both hospitalists and outpatient general internists may exacerbate these previously cited factors. These results suggest that efforts to address physician well‐being are critically needed for both inpatient and outpatient physicians.

Despite the noted similarities, differences between hospitalists and outpatient general internists in certain aspects of well‐being merit further attention. For example, the lower rate of personal accomplishment among hospitalists relative to outpatient generalists is consistent with prior evidence.[15] The reasons for this difference are unknown, but the relative youth and inexperience of the hospitalists may be a factor. US hospitalists have been noted to feel like glorified residents in at least 1 report,[31] a factor that might also negatively impact personal accomplishment.

It is also worthwhile to place the burnout results for both groups in context with prior studies. Although we found high rates of burnout among outpatient physicians, our outpatient sample's mean MBI subset scores are not higher than previous samples of American[32] and Canadian[33] outpatient physicians, suggesting that this finding is neither new nor artifactual. Placing the hospitalist sample in perspective is more difficult, as very few studies have administered the MBI to US hospitalists, and those that have either administered 1 component only to an exclusive academic sample[34] or administered it to a small mixture of hospitalists and intensivists.[35] The prevalence of burnout we report for our hospitalist sample is higher than that reported by studies that utilized single‐item survey items1214; it is likely that the higher prevalence we report relates more to a more detailed assessment of the components of burnout than to a temporal trend, although this cannot be determined definitively from the data available.

The finding that 9.2% of hospitalists and 5.8% of outpatient general internists reported suicidal thoughts in the past 12 months is alarming, though consistent with prior data on US surgeons.[35] Although the higher rate of suicidal thoughts among hospitalists was not statistically significant, a better understanding of the factors associated with physician suicidality should be the focus of additional research.

Hospitalists were more likely than outpatient internists to report plans to leave their current practice in this study, although their plans after leaving differed. The fact that they were more likely to report plans to find a different role in medicine (rather than to leave medicine entirely or retire) is likely a function of age and career stage. The finding that hospitalists with an interest in changing jobs were more likely than their outpatient colleagues to consider administrative, leadership, education, and research roles may partially reflect the greater number of hospitalists at academic medical centers in this study, but suggests that hospitalists may indeed benefit from the availability of opportunities that have been touted as part of hospitalist diastole.[36]

Finally, rates of alcohol misuse and abuse found in this study were consistent with those reported in prior studies.[37, 38, 39] These rates support ongoing efforts to address alcohol‐related issues among physicians. In addition, the proportion of outpatient general internists and hospitalists reporting having a primary care provider was similar to that seen in prior research.[40] The fact that 1 in 3 physicians in this study did not have a primary care provider suggests there is great room for improvement in access to and prioritization of healthcare for physicians in general. However, it is noteworthy that hospitalists were less likely than outpatient general internists to have a primary care provider even after adjusting for their younger age as a group. The reasons behind this discrepancy are unclear but worthy of further investigation.

Several limitations of our study should be considered. The response rate for the entire study sample was 26.7%, which is similar to other US national physician surveys in this topic area.[41, 42, 43] Demographic comparisons with national data suggest the respondents were reasonably representative of physicians nationally,[4] and all analyses were adjusted for recognized demographic factors affecting our outcomes of interest. We found no statistically significant differences in demographics of early responders compared with late responders (a standard approach to evaluate for response bias),[14, 31] further supporting that responders were representative of US physicians. Despite this, response bias remains possible. For example, it is unclear if burned out physicians might be more likely to respond (eg, due to the personal relevance of the survey topic) or less likely to respond (eg, due to being too overwhelmed to open or complete the survey).

A related limitation is the relatively small number of hospitalists included in this sample, which limits the power of the study to detect differences between the study groups. The hospitalists in this study were also relatively experienced, with a median of 10 years in practice, although the overall demographics match closely to a recent national survey of hospitalists. Although age was considered in the analyses, this study may not fully characterize burnout patterns among very junior or very senior hospitalists. In addition, although analyses were adjusted for observed differences between the study groups for a number of covariates, there may be differences between the study groups in other, unmeasured factors that could act as confounders of the observed results. For example, the allocation of each individual's time to different activities (eg, clinical, research, education, administration), workplace flexibility and control, and meaning may all contribute to distress and well‐being, and could not be assessed in this study.

In conclusion, the degree of burnout, depression, and suicidal ideation in both hospitalists and outpatient general internists is similar and substantial. Urgent attention directed at better understanding the causes of distress and identifying solutions for all internists is needed.

Acknowledgements

The authors acknowledge the role of the American Medical Association in completing this study.

Disclosures: The views expressed in this article are those of the authors and do not represent the views of, and should not be attributed to, the American Medical Association. The authors report no conflicts of interest.

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An increasingly robust body of literature has identified burnout as a substantial problem for physicians across specialties and practice settings.[1, 2, 3, 4] Burnout, a work‐related condition characterized by emotional exhaustion, depersonalization, and lack of a sense of personal accomplishment,[5] has been tied to negative consequences for patients, physicians, and the medical profession including medical errors,[6] poor physician health,[7, 8] and decreased professionalism.[9] Studies of burnout among general internists have pointed to time pressures, lack of work control, and difficult patient encounters as possible contributors.[10, 11]

Burnout has been demonstrated to affect a sizable proportion of hospitalists, with prevalence estimates from prior studies varying from 12.9% to 27.2%, although nearly all studies of US hospitalists have relied on single‐item instruments.[12, 13, 14, 15] Hospital‐based physicians have represented a rapidly expanding segment of the internist workforce for more than a decade,[14] but studies of the impact of inpatient vs outpatient practice location on burnout and career satisfaction are limited. A meta‐analysis of the impact of practice location on burnout relied almost exclusively on noncomparative studies from outside the United States.[15] A recent study of US physician burnout and satisfaction with work‐life balance showed that general internists expressed below average satisfaction with work‐life balance and had the second highest rate of burnout among 24 specialties.[4] However, this report did not differentiate between general internists working in inpatient vs outpatient settings.

We therefore examined burnout, satisfaction with work‐life balance, and other aspects of well‐being among internal medicine hospitalists relative to outpatient general internists, using a national sample developed in partnership with the American Medical Association.

METHODS

Physician Sample

As described previously,[4] the American Medical Association Physician Masterfile, a nearly complete record of US physicians, was used to generate a sample of physicians inclusive of all specialty disciplines. The 27,276 physicians who opened at least 1 invitation e‐mail were considered to have received the invitation to participate in the study. Participation was voluntary, and all responses were anonymous. For this analysis, internal medicine hospitalists were compared with general internists reporting primarily outpatient practices. The physician sample provided information on demographics (age, sex, and relationship status) and on characteristics of their practice. Burnout, symptoms of depression, suicidal ideation in the past 12 months, quality of life (QOL), satisfaction with work‐life balance, and certain health behaviors were evaluated as detailed below.

Burnout

Burnout among physicians was measured using the Maslach Burnout Inventory (MBI), a validated 22‐item questionnaire considered the gold standard tool for measuring burnout.[5, 16] The MBI has subscales to evaluate each domain of burnout: emotional exhaustion, depersonalization, and low personal accomplishment. Because other burnout studies have focused on the presence of high levels of emotional exhaustion or depersonalization as the foundation of burnout in physicians,[17, 18, 19] we considered physicians with a high score on the depersonalization or emotional exhaustion subscales to have at least 1 manifestation of professional burnout.

Symptoms of Depression and Suicidal Ideation

Symptoms of depression were assessed using the 2‐item Primary Care Evaluation of Mental Disorders,[20] a standardized and validated assessment for depression screening that performs as well as longer instruments.[21] Recent suicidal ideation was evaluated by asking participants, During the past 12 months, have you had thoughts of taking your own life? This item was designed to measure somewhat recent, but not necessarily active, suicidal ideation. These questions have been used extensively in other studies.[22, 23, 24, 25]

Quality of Life and Fatigue

Overall QOL and mental, physical, and emotional QOL were measured by a single‐item linear analog scale assessment. This instrument measured QOL on a 0 (as bad as it can be) to 10 (as good as it can be) scale validated across a wide range of medical conditions and populations.[26, 27, 28] Fatigue was measured using a similar standardized linear analog scale assessment question, for which respondents indicated their level of fatigue during the past week.[29] The impact of fatigue on daily activities such as driving was also evaluated.

Satisfaction With Work‐Life Balance and Career Plans

Satisfaction with work‐life balance was assessed by the item, My work schedule leaves me enough time for my personal/family life, with response options strongly agree, agree, neutral, disagree, or strongly disagree. Individuals who indicated strongly agree or agree were considered to be satisfied with their work‐life balance, whereas those who indicated strongly disagree or disagree were considered to be dissatisfied with their work‐life balance. Experience of work‐home conflicts was assessed as in prior research.[4] Participants were also asked about plans to change jobs or careers.

Health Behaviors

A limited set of health and wellness behaviors was addressed in the survey to provide insight into other aspects of physician well‐being. These included whether respondents had a primary care provider and questions concerning routine screening and alcohol and substance use. Alcohol use was assessed using the Alcohol Use Disorders Identification Test, version C (AUDIT‐C).[30] An AUDIT‐C score of at least 4 for men and at least 3 for women indicates alcohol misuse, and a score of at least 5 for men and at least 4 for women indicates alcohol abuse and possible dependence.[30]

Statistical Analysis

Standard descriptive summary statistics were used to characterize the physician samples. Associations between variables were evaluated using the Kruskal‐Wallis test (for continuous variables) or [2] test (for categorical variables). All tests were 2‐sided, with a type I error level of 0.05. Multivariate analysis of differences between hospitalists and outpatient general internists was performed using multiple linear or logistic regression for continuous or categorical data, respectively. Covariates in these models included age, sex, weekly work hours, and practice setting. All of the analyses were performed using SAS version 9.2 (SAS Institute, Inc., Cary, NC).

RESULTS

In the full survey across all specialties, 7288 physicians (26.7%) provided survey responses.[4] There were 448 outpatient internists and 130 internal medicine hospitalists who agreed to participate. Demographically, hospitalists were younger, worked longer hours, and were less likely to work in private practice than outpatient general internists (Table 1).

Demographics of Responding Internal Medicine Hospitalists and Outpatient General Internal Medicine Physicians
CharacteristicHospitalists (n=130)Outpatient General Internists (n=448)P
  • NOTE: Abbreviations: SD, standard deviation.

Sex, n (%)  0.56
Male86 (66.2%)284 (63.4%) 
Female44 (33.8%)164 (36.6%) 
Age, mean (SD)46.9 (12.4)53.6 (10.2)<0.001
Median45.055.0 
Years in practice, mean (SD)14.0 (12.0)21.6 (10.7)<0.001
Median10.022.0 
Hours worked per week, mean (SD)55.0 (18.1)50.0 (15.1)0.04
Median50.050.0 
Practice setting, n (%)  <0.001
Private practice/hospital36 (31.0%)303 (69.2%) 
Academic medical center37 (31.9%)41 (9.4%) 
Other (including veterans hospital and active military practice)43 (37.1%)94 (21.5%) 

Distress and Well‐Being Variables

High levels of emotional exhaustion affected 43.8% of hospitalists and 48.1% of outpatient general internists (odds ratio [OR]: 0.91, 95% confidence interval [CI]: 0.56‐1.48), and high levels of depersonalization affected 42.3% of hospitalists and 32.7% of outpatient general internists (OR: 1.42, 95% CI: 0.86‐2.35). Overall burnout affected 52.3% of hospitalists and 54.5% of outpatient general internists (OR: 0.96, 95% CI: 0.58‐1.57). None of these factors differed statistically in multivariate models adjusted for factors known to be associated with burnout, including sex, age, weekly work hours, and practice setting (P=0.71, 0.17, and 0.86, respectively; Table 2). However, low levels of personal accomplishment were reported by 20.3% of hospitalists and 9.6% of outpatient general internists (OR: 1.93, 95% CI: 1.023.65, P=0.04).

Distress and Well‐Being Results for Internal Medicine Hospitalists vs Outpatient General Internists
VariableHospitalists (n=130)Outpatient General Internists (n=448)Pa
  • NOTE: Abbreviations: DP, depersonalization; EE, emotional exhaustion; SD, standard deviation.

  • Adjusted for age, sex, weekly work hours, and practice setting.

Burnout   
Emotional exhaustion high (27)57/130 (43.8%)215/447 (48.1%)0.71
Mean (SD)24.7 (12.5)25.4 (14.0) 
Median24.926.0 
Depersonalization high (10)55/130 (42.3%)146/447 (32.7%)0.17
Mean (SD)9.1 (6.9)7.5 (6.3) 
Median7.06.0 
Personal accomplishment low (33)26/128 (20.3%)43/446 (9.6%)0.04
Mean (SD)39.0 (7.6)41.4 (6.0) 
Median41.043.0 
High burnout (EE27 or DP10)68/130 (52.3%)244/448 (54.5%)0.86
Depression   
Depression screen +52/129 (40.3%)176/440 (40.0%)0.73
Suicidal thoughts in past 12 months12/130 (9.2%)26/445 (5.8%)0.15
Quality of life   
Overall mean (SD)7.3 (2.0)7.4 (1.8)0.85
Median8.08.0 
Low (<6)21/130 (16.2%)73/448 (16.3%) 
Mental mean (SD)7.2 (2.1)7.3 (2.0)0.89
Median8.08.0 
Low (<6)23/130 (17.7%)92/448 (20.5%) 
Physical mean (SD)6.7 (2.3)6.9 (2.1)0.45
Median7.07.0 
Low (<6)35/130 (26.9%)106/448 (23.7%) 
Emotional mean (SD)7.0 (2.3)6.9 (2.2)0.37
Median7.07.0 
Low (<6)30/130 (23.1%)114/448 (25.4%) 
Fatigue   
Mean (SD)5.8 (2.4)5.9 (2.4)0.57
Median6.06.0 
Fallen asleep while driving (among regular drivers only)11/126 (8.7%)19/438 (4.3%)0.23

Approximately 40% of physicians in both groups screened positive for depression (OR: 0.92, 95% CI: 0.56‐1.51, P=0.73). In addition, 9.2% of hospitalists reported suicidal ideation in the last 12 months compared to 5.8% of outpatient internists (OR: 1.86, 95% CI: 0.80‐4.33, P=0.15) (Table 2).

Overall QOL and QOL in mental, physical, and emotional domains were nearly identical in the 2 groups (Table 2). Fatigue was also similar for hospitalists and outpatient general internists, and 8.5% of hospitalists reported falling asleep in traffic while driving compared to 4.2% of outpatient internists (OR: 1.76, 95% CI: 0.70‐4.44, P=0.23).

Work‐Life Balance and Career Variables

Experience of recent work‐home conflicts was similar for hospitalists and outpatient general internists (Table 3). However, hospitalists were more likely to agree or strongly agree that their work schedule leaves enough time for their personal life and family (50.0% vs 42.0%, OR: 2.06, 95% CI: 1.22‐3.47, P=0.007).

Work‐Life Balance and Career Variables for Internal Medicine Hospitalists vs Outpatient General Internists
VariableHospitalists (n=130)Outpatient General Internists (n=448)Pa
  • NOTE: Adjusted for age, sex, weekly work hours, and practice setting.

Work‐home conflict in last 3 weeks62/128 (48.4%)183/443 (41.3%)0.64
Work‐home conflict resolved in favor of:  0.79
Work37/118 (31.4%)131/405 (32.2%) 
Home15/118 (12.7%)43/405 (10.6%) 
Meeting both needs66/118 (55.9%)231/405 (57.0%) 
Work schedule leaves enough time for personal life/family  0.007
Strongly agree20 (15.4%)70 (15.7%) 
Agree45 (34.6%)117 (26.3%) 
Neutral21 (16.2%)66 (14.8%) 
Disagree27 (20.8%)119 (26.7%) 
Strongly disagree17 (13.1%)73 (16.4%) 
Missing03 
Likelihood of leaving current practice  0.002
Definite17 (13.1%)34 (7.6%) 
Likely21 (16.2%)53 (11.9%) 
Moderate21 (16.2%)67 (15.0%) 
Slight38 (29.2%)128 (28.7%) 
None33 (25.4%)164 (36.8%) 
Missing02 
Would choose to become physician again81/130 (62.3%)306/441 (69.4%)0.86

Hospitalists were more likely to express interest in leaving their current practice in the next 2 years, with 13.1% vs 7.6% reporting definite plans to leave and 29.2% vs 19.5% reporting at least likely plans to leave (OR: 2.31, 95% CI: 1.35‐3.97, P=0.002). Among those reporting a likely or definite plan to leave, hospitalists were more likely to plan to look for a different practice and continue to work as a physician (63.2% vs 39.1%), whereas outpatient general internists were more likely to plan to leave medical practice (51.9% vs 22.0%, P=0.004). Hospitalists with plans to reduce their work hours were more likely than their outpatient colleagues to express an interest in administrative and leadership roles (19.4% vs 12.1%) or research and educational roles (9.7% vs 4.0%, P=0.05).

Health Behavior Variables

Hospitalists were less likely to report having a primary care provider in the adjusted analyses (55.0% vs 70.3%, OR: 0.49, 95% CI: 0.29‐0.83, P=0.008). Use of illicit substances was uncommon in both groups (94.6% of hospitalists and 96.0% of outpatient general internists reported never using an illicit substance (OR: 0.87, 95% CI: 0.31‐2.49, P=0.80). Symptoms of alcohol abuse were similar between the 2 groups (11.7% and 13.3%, respectively, OR: 0.64, 95% CI: 0.30‐1.35, P=0.24), but symptoms of alcohol misuse were more common among outpatient general internists (34.2% vs 21.9%, OR: 1.75, 95% CI: 1.013.03, P=0.047).

DISCUSSION

The primary result of this national study applying well‐validated metrics is that the overall rates of burnout among hospitalists and outpatient general internal medicine physicians were similar, as were rates of positive depression screening and QOL. Although these groups did not differ, the absolute rates of distress found in this study were high. Prior research has suggested that possible explanations for these high rates of distress include excessive workload, loss of work‐associated control and meaning, and difficulties with work‐home balance.[4] The present study, in the context of prior work showing that general internists have higher rates of burnout than almost any other specialty, suggests that the front‐line nature of the work of both hospitalists and outpatient general internists may exacerbate these previously cited factors. These results suggest that efforts to address physician well‐being are critically needed for both inpatient and outpatient physicians.

Despite the noted similarities, differences between hospitalists and outpatient general internists in certain aspects of well‐being merit further attention. For example, the lower rate of personal accomplishment among hospitalists relative to outpatient generalists is consistent with prior evidence.[15] The reasons for this difference are unknown, but the relative youth and inexperience of the hospitalists may be a factor. US hospitalists have been noted to feel like glorified residents in at least 1 report,[31] a factor that might also negatively impact personal accomplishment.

It is also worthwhile to place the burnout results for both groups in context with prior studies. Although we found high rates of burnout among outpatient physicians, our outpatient sample's mean MBI subset scores are not higher than previous samples of American[32] and Canadian[33] outpatient physicians, suggesting that this finding is neither new nor artifactual. Placing the hospitalist sample in perspective is more difficult, as very few studies have administered the MBI to US hospitalists, and those that have either administered 1 component only to an exclusive academic sample[34] or administered it to a small mixture of hospitalists and intensivists.[35] The prevalence of burnout we report for our hospitalist sample is higher than that reported by studies that utilized single‐item survey items1214; it is likely that the higher prevalence we report relates more to a more detailed assessment of the components of burnout than to a temporal trend, although this cannot be determined definitively from the data available.

The finding that 9.2% of hospitalists and 5.8% of outpatient general internists reported suicidal thoughts in the past 12 months is alarming, though consistent with prior data on US surgeons.[35] Although the higher rate of suicidal thoughts among hospitalists was not statistically significant, a better understanding of the factors associated with physician suicidality should be the focus of additional research.

Hospitalists were more likely than outpatient internists to report plans to leave their current practice in this study, although their plans after leaving differed. The fact that they were more likely to report plans to find a different role in medicine (rather than to leave medicine entirely or retire) is likely a function of age and career stage. The finding that hospitalists with an interest in changing jobs were more likely than their outpatient colleagues to consider administrative, leadership, education, and research roles may partially reflect the greater number of hospitalists at academic medical centers in this study, but suggests that hospitalists may indeed benefit from the availability of opportunities that have been touted as part of hospitalist diastole.[36]

Finally, rates of alcohol misuse and abuse found in this study were consistent with those reported in prior studies.[37, 38, 39] These rates support ongoing efforts to address alcohol‐related issues among physicians. In addition, the proportion of outpatient general internists and hospitalists reporting having a primary care provider was similar to that seen in prior research.[40] The fact that 1 in 3 physicians in this study did not have a primary care provider suggests there is great room for improvement in access to and prioritization of healthcare for physicians in general. However, it is noteworthy that hospitalists were less likely than outpatient general internists to have a primary care provider even after adjusting for their younger age as a group. The reasons behind this discrepancy are unclear but worthy of further investigation.

Several limitations of our study should be considered. The response rate for the entire study sample was 26.7%, which is similar to other US national physician surveys in this topic area.[41, 42, 43] Demographic comparisons with national data suggest the respondents were reasonably representative of physicians nationally,[4] and all analyses were adjusted for recognized demographic factors affecting our outcomes of interest. We found no statistically significant differences in demographics of early responders compared with late responders (a standard approach to evaluate for response bias),[14, 31] further supporting that responders were representative of US physicians. Despite this, response bias remains possible. For example, it is unclear if burned out physicians might be more likely to respond (eg, due to the personal relevance of the survey topic) or less likely to respond (eg, due to being too overwhelmed to open or complete the survey).

A related limitation is the relatively small number of hospitalists included in this sample, which limits the power of the study to detect differences between the study groups. The hospitalists in this study were also relatively experienced, with a median of 10 years in practice, although the overall demographics match closely to a recent national survey of hospitalists. Although age was considered in the analyses, this study may not fully characterize burnout patterns among very junior or very senior hospitalists. In addition, although analyses were adjusted for observed differences between the study groups for a number of covariates, there may be differences between the study groups in other, unmeasured factors that could act as confounders of the observed results. For example, the allocation of each individual's time to different activities (eg, clinical, research, education, administration), workplace flexibility and control, and meaning may all contribute to distress and well‐being, and could not be assessed in this study.

In conclusion, the degree of burnout, depression, and suicidal ideation in both hospitalists and outpatient general internists is similar and substantial. Urgent attention directed at better understanding the causes of distress and identifying solutions for all internists is needed.

Acknowledgements

The authors acknowledge the role of the American Medical Association in completing this study.

Disclosures: The views expressed in this article are those of the authors and do not represent the views of, and should not be attributed to, the American Medical Association. The authors report no conflicts of interest.

An increasingly robust body of literature has identified burnout as a substantial problem for physicians across specialties and practice settings.[1, 2, 3, 4] Burnout, a work‐related condition characterized by emotional exhaustion, depersonalization, and lack of a sense of personal accomplishment,[5] has been tied to negative consequences for patients, physicians, and the medical profession including medical errors,[6] poor physician health,[7, 8] and decreased professionalism.[9] Studies of burnout among general internists have pointed to time pressures, lack of work control, and difficult patient encounters as possible contributors.[10, 11]

Burnout has been demonstrated to affect a sizable proportion of hospitalists, with prevalence estimates from prior studies varying from 12.9% to 27.2%, although nearly all studies of US hospitalists have relied on single‐item instruments.[12, 13, 14, 15] Hospital‐based physicians have represented a rapidly expanding segment of the internist workforce for more than a decade,[14] but studies of the impact of inpatient vs outpatient practice location on burnout and career satisfaction are limited. A meta‐analysis of the impact of practice location on burnout relied almost exclusively on noncomparative studies from outside the United States.[15] A recent study of US physician burnout and satisfaction with work‐life balance showed that general internists expressed below average satisfaction with work‐life balance and had the second highest rate of burnout among 24 specialties.[4] However, this report did not differentiate between general internists working in inpatient vs outpatient settings.

We therefore examined burnout, satisfaction with work‐life balance, and other aspects of well‐being among internal medicine hospitalists relative to outpatient general internists, using a national sample developed in partnership with the American Medical Association.

METHODS

Physician Sample

As described previously,[4] the American Medical Association Physician Masterfile, a nearly complete record of US physicians, was used to generate a sample of physicians inclusive of all specialty disciplines. The 27,276 physicians who opened at least 1 invitation e‐mail were considered to have received the invitation to participate in the study. Participation was voluntary, and all responses were anonymous. For this analysis, internal medicine hospitalists were compared with general internists reporting primarily outpatient practices. The physician sample provided information on demographics (age, sex, and relationship status) and on characteristics of their practice. Burnout, symptoms of depression, suicidal ideation in the past 12 months, quality of life (QOL), satisfaction with work‐life balance, and certain health behaviors were evaluated as detailed below.

Burnout

Burnout among physicians was measured using the Maslach Burnout Inventory (MBI), a validated 22‐item questionnaire considered the gold standard tool for measuring burnout.[5, 16] The MBI has subscales to evaluate each domain of burnout: emotional exhaustion, depersonalization, and low personal accomplishment. Because other burnout studies have focused on the presence of high levels of emotional exhaustion or depersonalization as the foundation of burnout in physicians,[17, 18, 19] we considered physicians with a high score on the depersonalization or emotional exhaustion subscales to have at least 1 manifestation of professional burnout.

Symptoms of Depression and Suicidal Ideation

Symptoms of depression were assessed using the 2‐item Primary Care Evaluation of Mental Disorders,[20] a standardized and validated assessment for depression screening that performs as well as longer instruments.[21] Recent suicidal ideation was evaluated by asking participants, During the past 12 months, have you had thoughts of taking your own life? This item was designed to measure somewhat recent, but not necessarily active, suicidal ideation. These questions have been used extensively in other studies.[22, 23, 24, 25]

Quality of Life and Fatigue

Overall QOL and mental, physical, and emotional QOL were measured by a single‐item linear analog scale assessment. This instrument measured QOL on a 0 (as bad as it can be) to 10 (as good as it can be) scale validated across a wide range of medical conditions and populations.[26, 27, 28] Fatigue was measured using a similar standardized linear analog scale assessment question, for which respondents indicated their level of fatigue during the past week.[29] The impact of fatigue on daily activities such as driving was also evaluated.

Satisfaction With Work‐Life Balance and Career Plans

Satisfaction with work‐life balance was assessed by the item, My work schedule leaves me enough time for my personal/family life, with response options strongly agree, agree, neutral, disagree, or strongly disagree. Individuals who indicated strongly agree or agree were considered to be satisfied with their work‐life balance, whereas those who indicated strongly disagree or disagree were considered to be dissatisfied with their work‐life balance. Experience of work‐home conflicts was assessed as in prior research.[4] Participants were also asked about plans to change jobs or careers.

Health Behaviors

A limited set of health and wellness behaviors was addressed in the survey to provide insight into other aspects of physician well‐being. These included whether respondents had a primary care provider and questions concerning routine screening and alcohol and substance use. Alcohol use was assessed using the Alcohol Use Disorders Identification Test, version C (AUDIT‐C).[30] An AUDIT‐C score of at least 4 for men and at least 3 for women indicates alcohol misuse, and a score of at least 5 for men and at least 4 for women indicates alcohol abuse and possible dependence.[30]

Statistical Analysis

Standard descriptive summary statistics were used to characterize the physician samples. Associations between variables were evaluated using the Kruskal‐Wallis test (for continuous variables) or [2] test (for categorical variables). All tests were 2‐sided, with a type I error level of 0.05. Multivariate analysis of differences between hospitalists and outpatient general internists was performed using multiple linear or logistic regression for continuous or categorical data, respectively. Covariates in these models included age, sex, weekly work hours, and practice setting. All of the analyses were performed using SAS version 9.2 (SAS Institute, Inc., Cary, NC).

RESULTS

In the full survey across all specialties, 7288 physicians (26.7%) provided survey responses.[4] There were 448 outpatient internists and 130 internal medicine hospitalists who agreed to participate. Demographically, hospitalists were younger, worked longer hours, and were less likely to work in private practice than outpatient general internists (Table 1).

Demographics of Responding Internal Medicine Hospitalists and Outpatient General Internal Medicine Physicians
CharacteristicHospitalists (n=130)Outpatient General Internists (n=448)P
  • NOTE: Abbreviations: SD, standard deviation.

Sex, n (%)  0.56
Male86 (66.2%)284 (63.4%) 
Female44 (33.8%)164 (36.6%) 
Age, mean (SD)46.9 (12.4)53.6 (10.2)<0.001
Median45.055.0 
Years in practice, mean (SD)14.0 (12.0)21.6 (10.7)<0.001
Median10.022.0 
Hours worked per week, mean (SD)55.0 (18.1)50.0 (15.1)0.04
Median50.050.0 
Practice setting, n (%)  <0.001
Private practice/hospital36 (31.0%)303 (69.2%) 
Academic medical center37 (31.9%)41 (9.4%) 
Other (including veterans hospital and active military practice)43 (37.1%)94 (21.5%) 

Distress and Well‐Being Variables

High levels of emotional exhaustion affected 43.8% of hospitalists and 48.1% of outpatient general internists (odds ratio [OR]: 0.91, 95% confidence interval [CI]: 0.56‐1.48), and high levels of depersonalization affected 42.3% of hospitalists and 32.7% of outpatient general internists (OR: 1.42, 95% CI: 0.86‐2.35). Overall burnout affected 52.3% of hospitalists and 54.5% of outpatient general internists (OR: 0.96, 95% CI: 0.58‐1.57). None of these factors differed statistically in multivariate models adjusted for factors known to be associated with burnout, including sex, age, weekly work hours, and practice setting (P=0.71, 0.17, and 0.86, respectively; Table 2). However, low levels of personal accomplishment were reported by 20.3% of hospitalists and 9.6% of outpatient general internists (OR: 1.93, 95% CI: 1.023.65, P=0.04).

Distress and Well‐Being Results for Internal Medicine Hospitalists vs Outpatient General Internists
VariableHospitalists (n=130)Outpatient General Internists (n=448)Pa
  • NOTE: Abbreviations: DP, depersonalization; EE, emotional exhaustion; SD, standard deviation.

  • Adjusted for age, sex, weekly work hours, and practice setting.

Burnout   
Emotional exhaustion high (27)57/130 (43.8%)215/447 (48.1%)0.71
Mean (SD)24.7 (12.5)25.4 (14.0) 
Median24.926.0 
Depersonalization high (10)55/130 (42.3%)146/447 (32.7%)0.17
Mean (SD)9.1 (6.9)7.5 (6.3) 
Median7.06.0 
Personal accomplishment low (33)26/128 (20.3%)43/446 (9.6%)0.04
Mean (SD)39.0 (7.6)41.4 (6.0) 
Median41.043.0 
High burnout (EE27 or DP10)68/130 (52.3%)244/448 (54.5%)0.86
Depression   
Depression screen +52/129 (40.3%)176/440 (40.0%)0.73
Suicidal thoughts in past 12 months12/130 (9.2%)26/445 (5.8%)0.15
Quality of life   
Overall mean (SD)7.3 (2.0)7.4 (1.8)0.85
Median8.08.0 
Low (<6)21/130 (16.2%)73/448 (16.3%) 
Mental mean (SD)7.2 (2.1)7.3 (2.0)0.89
Median8.08.0 
Low (<6)23/130 (17.7%)92/448 (20.5%) 
Physical mean (SD)6.7 (2.3)6.9 (2.1)0.45
Median7.07.0 
Low (<6)35/130 (26.9%)106/448 (23.7%) 
Emotional mean (SD)7.0 (2.3)6.9 (2.2)0.37
Median7.07.0 
Low (<6)30/130 (23.1%)114/448 (25.4%) 
Fatigue   
Mean (SD)5.8 (2.4)5.9 (2.4)0.57
Median6.06.0 
Fallen asleep while driving (among regular drivers only)11/126 (8.7%)19/438 (4.3%)0.23

Approximately 40% of physicians in both groups screened positive for depression (OR: 0.92, 95% CI: 0.56‐1.51, P=0.73). In addition, 9.2% of hospitalists reported suicidal ideation in the last 12 months compared to 5.8% of outpatient internists (OR: 1.86, 95% CI: 0.80‐4.33, P=0.15) (Table 2).

Overall QOL and QOL in mental, physical, and emotional domains were nearly identical in the 2 groups (Table 2). Fatigue was also similar for hospitalists and outpatient general internists, and 8.5% of hospitalists reported falling asleep in traffic while driving compared to 4.2% of outpatient internists (OR: 1.76, 95% CI: 0.70‐4.44, P=0.23).

Work‐Life Balance and Career Variables

Experience of recent work‐home conflicts was similar for hospitalists and outpatient general internists (Table 3). However, hospitalists were more likely to agree or strongly agree that their work schedule leaves enough time for their personal life and family (50.0% vs 42.0%, OR: 2.06, 95% CI: 1.22‐3.47, P=0.007).

Work‐Life Balance and Career Variables for Internal Medicine Hospitalists vs Outpatient General Internists
VariableHospitalists (n=130)Outpatient General Internists (n=448)Pa
  • NOTE: Adjusted for age, sex, weekly work hours, and practice setting.

Work‐home conflict in last 3 weeks62/128 (48.4%)183/443 (41.3%)0.64
Work‐home conflict resolved in favor of:  0.79
Work37/118 (31.4%)131/405 (32.2%) 
Home15/118 (12.7%)43/405 (10.6%) 
Meeting both needs66/118 (55.9%)231/405 (57.0%) 
Work schedule leaves enough time for personal life/family  0.007
Strongly agree20 (15.4%)70 (15.7%) 
Agree45 (34.6%)117 (26.3%) 
Neutral21 (16.2%)66 (14.8%) 
Disagree27 (20.8%)119 (26.7%) 
Strongly disagree17 (13.1%)73 (16.4%) 
Missing03 
Likelihood of leaving current practice  0.002
Definite17 (13.1%)34 (7.6%) 
Likely21 (16.2%)53 (11.9%) 
Moderate21 (16.2%)67 (15.0%) 
Slight38 (29.2%)128 (28.7%) 
None33 (25.4%)164 (36.8%) 
Missing02 
Would choose to become physician again81/130 (62.3%)306/441 (69.4%)0.86

Hospitalists were more likely to express interest in leaving their current practice in the next 2 years, with 13.1% vs 7.6% reporting definite plans to leave and 29.2% vs 19.5% reporting at least likely plans to leave (OR: 2.31, 95% CI: 1.35‐3.97, P=0.002). Among those reporting a likely or definite plan to leave, hospitalists were more likely to plan to look for a different practice and continue to work as a physician (63.2% vs 39.1%), whereas outpatient general internists were more likely to plan to leave medical practice (51.9% vs 22.0%, P=0.004). Hospitalists with plans to reduce their work hours were more likely than their outpatient colleagues to express an interest in administrative and leadership roles (19.4% vs 12.1%) or research and educational roles (9.7% vs 4.0%, P=0.05).

Health Behavior Variables

Hospitalists were less likely to report having a primary care provider in the adjusted analyses (55.0% vs 70.3%, OR: 0.49, 95% CI: 0.29‐0.83, P=0.008). Use of illicit substances was uncommon in both groups (94.6% of hospitalists and 96.0% of outpatient general internists reported never using an illicit substance (OR: 0.87, 95% CI: 0.31‐2.49, P=0.80). Symptoms of alcohol abuse were similar between the 2 groups (11.7% and 13.3%, respectively, OR: 0.64, 95% CI: 0.30‐1.35, P=0.24), but symptoms of alcohol misuse were more common among outpatient general internists (34.2% vs 21.9%, OR: 1.75, 95% CI: 1.013.03, P=0.047).

DISCUSSION

The primary result of this national study applying well‐validated metrics is that the overall rates of burnout among hospitalists and outpatient general internal medicine physicians were similar, as were rates of positive depression screening and QOL. Although these groups did not differ, the absolute rates of distress found in this study were high. Prior research has suggested that possible explanations for these high rates of distress include excessive workload, loss of work‐associated control and meaning, and difficulties with work‐home balance.[4] The present study, in the context of prior work showing that general internists have higher rates of burnout than almost any other specialty, suggests that the front‐line nature of the work of both hospitalists and outpatient general internists may exacerbate these previously cited factors. These results suggest that efforts to address physician well‐being are critically needed for both inpatient and outpatient physicians.

Despite the noted similarities, differences between hospitalists and outpatient general internists in certain aspects of well‐being merit further attention. For example, the lower rate of personal accomplishment among hospitalists relative to outpatient generalists is consistent with prior evidence.[15] The reasons for this difference are unknown, but the relative youth and inexperience of the hospitalists may be a factor. US hospitalists have been noted to feel like glorified residents in at least 1 report,[31] a factor that might also negatively impact personal accomplishment.

It is also worthwhile to place the burnout results for both groups in context with prior studies. Although we found high rates of burnout among outpatient physicians, our outpatient sample's mean MBI subset scores are not higher than previous samples of American[32] and Canadian[33] outpatient physicians, suggesting that this finding is neither new nor artifactual. Placing the hospitalist sample in perspective is more difficult, as very few studies have administered the MBI to US hospitalists, and those that have either administered 1 component only to an exclusive academic sample[34] or administered it to a small mixture of hospitalists and intensivists.[35] The prevalence of burnout we report for our hospitalist sample is higher than that reported by studies that utilized single‐item survey items1214; it is likely that the higher prevalence we report relates more to a more detailed assessment of the components of burnout than to a temporal trend, although this cannot be determined definitively from the data available.

The finding that 9.2% of hospitalists and 5.8% of outpatient general internists reported suicidal thoughts in the past 12 months is alarming, though consistent with prior data on US surgeons.[35] Although the higher rate of suicidal thoughts among hospitalists was not statistically significant, a better understanding of the factors associated with physician suicidality should be the focus of additional research.

Hospitalists were more likely than outpatient internists to report plans to leave their current practice in this study, although their plans after leaving differed. The fact that they were more likely to report plans to find a different role in medicine (rather than to leave medicine entirely or retire) is likely a function of age and career stage. The finding that hospitalists with an interest in changing jobs were more likely than their outpatient colleagues to consider administrative, leadership, education, and research roles may partially reflect the greater number of hospitalists at academic medical centers in this study, but suggests that hospitalists may indeed benefit from the availability of opportunities that have been touted as part of hospitalist diastole.[36]

Finally, rates of alcohol misuse and abuse found in this study were consistent with those reported in prior studies.[37, 38, 39] These rates support ongoing efforts to address alcohol‐related issues among physicians. In addition, the proportion of outpatient general internists and hospitalists reporting having a primary care provider was similar to that seen in prior research.[40] The fact that 1 in 3 physicians in this study did not have a primary care provider suggests there is great room for improvement in access to and prioritization of healthcare for physicians in general. However, it is noteworthy that hospitalists were less likely than outpatient general internists to have a primary care provider even after adjusting for their younger age as a group. The reasons behind this discrepancy are unclear but worthy of further investigation.

Several limitations of our study should be considered. The response rate for the entire study sample was 26.7%, which is similar to other US national physician surveys in this topic area.[41, 42, 43] Demographic comparisons with national data suggest the respondents were reasonably representative of physicians nationally,[4] and all analyses were adjusted for recognized demographic factors affecting our outcomes of interest. We found no statistically significant differences in demographics of early responders compared with late responders (a standard approach to evaluate for response bias),[14, 31] further supporting that responders were representative of US physicians. Despite this, response bias remains possible. For example, it is unclear if burned out physicians might be more likely to respond (eg, due to the personal relevance of the survey topic) or less likely to respond (eg, due to being too overwhelmed to open or complete the survey).

A related limitation is the relatively small number of hospitalists included in this sample, which limits the power of the study to detect differences between the study groups. The hospitalists in this study were also relatively experienced, with a median of 10 years in practice, although the overall demographics match closely to a recent national survey of hospitalists. Although age was considered in the analyses, this study may not fully characterize burnout patterns among very junior or very senior hospitalists. In addition, although analyses were adjusted for observed differences between the study groups for a number of covariates, there may be differences between the study groups in other, unmeasured factors that could act as confounders of the observed results. For example, the allocation of each individual's time to different activities (eg, clinical, research, education, administration), workplace flexibility and control, and meaning may all contribute to distress and well‐being, and could not be assessed in this study.

In conclusion, the degree of burnout, depression, and suicidal ideation in both hospitalists and outpatient general internists is similar and substantial. Urgent attention directed at better understanding the causes of distress and identifying solutions for all internists is needed.

Acknowledgements

The authors acknowledge the role of the American Medical Association in completing this study.

Disclosures: The views expressed in this article are those of the authors and do not represent the views of, and should not be attributed to, the American Medical Association. The authors report no conflicts of interest.

References
  1. Olkinuora M, Asp S, Juntunen J, Kauttu K, Strid L, Aarimaa M. Stress symptoms, burnout and suicidal thoughts in Finnish physicians. Soc Psychiatry Psychiatr Epidemiol. 1990;25:8186.
  2. Linzer M, Visser MR, Oort FJ, Smets EM, McMurray JE, Haes HC; Society of General Internal Medicine (SGIM) Career Satisfaction Study Group (CSSG). Predicting and preventing physician burnout: results from the United States and the Netherlands. Am J Med. 2001;111:170175.
  3. Bressi C, Porcellana M, Gambini O, et al. Burnout among psychiatrists in Milan: a multicenter study. Psychiatr Serv. 2009;60:985988.
  4. Shanafelt TD, Boone S, Tan L, et al. Burnout and satisfaction with work‐life balance among US physicians relative to the general US population. Arch Intern Med. 2012;172:13771385.
  5. Maslach C, Jackson SE. The measurement of experienced burnout. J Occup Behav. 1981;2:99113.
  6. Shanafelt TD, Balch CM, Bechamps G, et al. Burnout and medical errors among American surgeons. Ann Surg. 2010;251:9951000.
  7. Wallace JE, Lemaire JB, Ghali WA. Physician wellness: a missing quality indicator. Lancet. 2009;374:17141721.
  8. Taylor C, Graham J, Potts HW, Richards MA, Ramirez AJ. Changes in mental health of UK hospital consultants since the mid‐1990s. Lancet. 2005;366:742744.
  9. Dyrbye LN, Massie FS, Eacker A, et al. Relationship between burnout and professional conduct and attitudes among US medical students. JAMA. 2010;304:11731180.
  10. Linzer M, Baler Manwell L, Williams ES, et al.; MEMO (Minimizing Error, Maximizing Outcomes) Investigators. Working conditions in primary care: physician reactions and care quality. Ann Intern Med. 2009;151:2836.
  11. An PG, Rabatin JS, Manwell LB, Linzer M, Brown RL, Schwartz MD; MEMO Investigators. Burden of difficult encounters in primary care: data from the Minimizing Error, Maximizing Outcomes study. Arch Intern Med. 2009;169:410414.
  12. Hoff TH, Whitcomb WF, Williams K, Nelson JR, Cheesman RA. Characteristics and work experiences of hospitalists in the United States. Arch Intern Med. 2001;161(6):851858.
  13. Glasheen JJ, Misky GJ, Reid MB, Harrison RA, Sharpe B, Auerbach A. Career satisfaction and burnout in academic hospital medicine. Arch Intern Med. 2011;25:171(8):782785.
  14. Hinami K, Whelan CT, Miller JA, Wolosin RJ, Wetterneck TB; Society of Hospital Medicine Career Satisfaction Task Force. Job characteristics, satisfaction, and burnout across hospitalist practice models. J Hosp Med. 2012;7:402410.
  15. Roberts DL, Cannon KC, Wellik KE, Wu Q, Budavari AI. Burnout in inpatient‐based vs outpatient‐based physicians: a systematic review and meta‐analysis. J Hosp Med. 2013;8:653664.
  16. Maslach C, Jackson S, Leiter M. Maslach Burnout Inventory Manual. 3rd ed. Palo Alto, CA: Consulting Psychologists Press; 1996.
  17. Thomas NK. Resident burnout. JAMA. 2004;292(23):28802889.
  18. Shanafelt TD, Bradley KA, Wipf JE, Back AL. Burnout and self‐reported patient care in an internal medicine residency program. Ann Intern Med. 2002;136:358367.
  19. Rosen IM, Gimotty PA, Shea JA, Bellini LM. Evolution of sleep quantity, sleep deprivation, mood disturbances, empathy, and burnout among interns. Acad Med. 2006;81:8285.
  20. Spitzer RL, Williams JB, Kroenke K, et al. Utility of a new procedure for diagnosing mental disorders in primary care: the PRIME‐MD 1000 study. JAMA. 1994;272:17491756.
  21. Whooley MA, Avins AL, Miranda J, Browner WS. Case‐finding instruments for depression: two questions are as good as many. J Gen Intern Med. 1997;12:439445.
  22. Meehan PJ, Lamb JA, Saltzman LE, O'Carroll PW. Attempted suicide among young adults: progress toward a meaningful estimate of prevalence. Am J Psychiatry. 1992;149:4144.
  23. Kessler RC, Borges G, Walters EE. Prevalence of and risk factors for lifetime suicide attempts in the National Comorbidity Survey. Arch Gen Psychiatry. 1999;56:617626.
  24. Kessler RC, Berglund P, Borges G, Nock M, Wang PS. Trends in suicide ideation, plans, gestures, and attempts in the United States, 1990–1992 to 2001–2003. JAMA. 2005;293:24872495.
  25. Cooper‐Patrick L, Crum RM, Ford DE. Identifying suicidal ideation in general medical patients. JAMA. 1994;272:17571762.
  26. Gudex C, Dolan P, Kind P, Williams A. Health state valuations from the general public using the visual analogue scale. Qual Life Res. 1996;5:521531.
  27. Shanafelt TD, Novotny P, Johnson ME, et al. The well‐being and personal wellness promotion strategies of medical oncologists in the North Central Cancer Treatment Group. Oncology. 2005;68:2332.
  28. Rummans TA, Clark MM, Sloan JA, et al. Impacting quality of life for patients with advanced cancer with a structured multidisciplinary intervention: a randomized controlled trial. J Clin Oncol. 2006;24:635642.
  29. West CP, Tan AD, Habermann TM, Sloan JA, Shanafelt TD. Association of resident fatigue and distress with perceived medical errors. JAMA. 2009;302:294300.
  30. Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol consumption questions (AUDIT‐C): an effective brief screening test for problem drinking. Arch Intern Med. 1998;158:17891795.
  31. Hinami K, Whelan CT, Wolosin RJ, Miller JA, Wetterneck TB. Worklife and satisfaction of hospitalists: toward flourishing careers. J Gen Intern Med. 2012;27(1):2836.
  32. Krasner MS, Epstein RM, Beckman H, et al. Association of an educational program in mindful communication with burnout, empathy, and attitudes among primary care physicians. JAMA. 2009;302(12):12841293.
  33. Lee FJ, Stewart M, Brown JB. Stress, burnout, and strategies for reducing them: what's the situation among Canadian family physicians? Can Fam Physician. 2008;54(2):234235.
  34. Lucas B, Trick W, Evans A, et al. Emotional exhaustion, life stress, and perceived control among medicine ward attending physicians: a randomized trial of 2‐ versus 4‐week ward rotations [abstract]. J Hosp Med. 2011;6(4 suppl 2):S43S44.
  35. Shanafelt TD, Balch CM, Dyrbye L, et al. Special report: suicidal ideation among American surgeons. Arch Surg. 2011;146:5462.
  36. Arora V, Fang MC, Kripalani S, Amin AN. Preparing for “diastole”: advanced training opportunities for academic hospitalists. J Hosp Med. 2006;1:368377.
  37. Hughes PH, Brandenburg N, Baldwin DC, et al. Prevalence of substance use among US physicians. JAMA. 1992;267:23332339.
  38. Bazargan M, Makar M, Bazargan‐Hejazi S, Ani C, Wolf KE. Preventive, lifestyle, and personal health behaviors among physicians. Acad Psychiatry. 2009;33:289295.
  39. Oreskovich MR, Kaups KL, Balch CM, et al. Prevalence of alcohol use disorders among American surgeons. Arch Surg. 2012;147:168174.
  40. Gross CP, Mead LA, Ford DE, Klag MJ. Physician, heal thyself? Regular source of care and use of preventive health services among physicians. Arch Intern Med. 2000;160:32093214.
  41. Allegra CJ, Hall R, Yothers G. Prevalence of burnout in the U.S. oncologic community: results of a 2003 survey. J Oncol Pract. 2005;1(4):140147.
  42. Kuerer HM, Eberlein TJ, Pollock RE, et al. Career satisfaction, practice patterns and burnout among surgical oncologists: report on the quality of life of members of the Society of Surgical Oncology. Ann Surg Oncol. 2007;14:30423053.
  43. Shanafelt TD, Balch CM, Bechamps G, et al. Burnout and career satisfaction among American surgeons. Ann Surg. 2009;250(3):463471.
References
  1. Olkinuora M, Asp S, Juntunen J, Kauttu K, Strid L, Aarimaa M. Stress symptoms, burnout and suicidal thoughts in Finnish physicians. Soc Psychiatry Psychiatr Epidemiol. 1990;25:8186.
  2. Linzer M, Visser MR, Oort FJ, Smets EM, McMurray JE, Haes HC; Society of General Internal Medicine (SGIM) Career Satisfaction Study Group (CSSG). Predicting and preventing physician burnout: results from the United States and the Netherlands. Am J Med. 2001;111:170175.
  3. Bressi C, Porcellana M, Gambini O, et al. Burnout among psychiatrists in Milan: a multicenter study. Psychiatr Serv. 2009;60:985988.
  4. Shanafelt TD, Boone S, Tan L, et al. Burnout and satisfaction with work‐life balance among US physicians relative to the general US population. Arch Intern Med. 2012;172:13771385.
  5. Maslach C, Jackson SE. The measurement of experienced burnout. J Occup Behav. 1981;2:99113.
  6. Shanafelt TD, Balch CM, Bechamps G, et al. Burnout and medical errors among American surgeons. Ann Surg. 2010;251:9951000.
  7. Wallace JE, Lemaire JB, Ghali WA. Physician wellness: a missing quality indicator. Lancet. 2009;374:17141721.
  8. Taylor C, Graham J, Potts HW, Richards MA, Ramirez AJ. Changes in mental health of UK hospital consultants since the mid‐1990s. Lancet. 2005;366:742744.
  9. Dyrbye LN, Massie FS, Eacker A, et al. Relationship between burnout and professional conduct and attitudes among US medical students. JAMA. 2010;304:11731180.
  10. Linzer M, Baler Manwell L, Williams ES, et al.; MEMO (Minimizing Error, Maximizing Outcomes) Investigators. Working conditions in primary care: physician reactions and care quality. Ann Intern Med. 2009;151:2836.
  11. An PG, Rabatin JS, Manwell LB, Linzer M, Brown RL, Schwartz MD; MEMO Investigators. Burden of difficult encounters in primary care: data from the Minimizing Error, Maximizing Outcomes study. Arch Intern Med. 2009;169:410414.
  12. Hoff TH, Whitcomb WF, Williams K, Nelson JR, Cheesman RA. Characteristics and work experiences of hospitalists in the United States. Arch Intern Med. 2001;161(6):851858.
  13. Glasheen JJ, Misky GJ, Reid MB, Harrison RA, Sharpe B, Auerbach A. Career satisfaction and burnout in academic hospital medicine. Arch Intern Med. 2011;25:171(8):782785.
  14. Hinami K, Whelan CT, Miller JA, Wolosin RJ, Wetterneck TB; Society of Hospital Medicine Career Satisfaction Task Force. Job characteristics, satisfaction, and burnout across hospitalist practice models. J Hosp Med. 2012;7:402410.
  15. Roberts DL, Cannon KC, Wellik KE, Wu Q, Budavari AI. Burnout in inpatient‐based vs outpatient‐based physicians: a systematic review and meta‐analysis. J Hosp Med. 2013;8:653664.
  16. Maslach C, Jackson S, Leiter M. Maslach Burnout Inventory Manual. 3rd ed. Palo Alto, CA: Consulting Psychologists Press; 1996.
  17. Thomas NK. Resident burnout. JAMA. 2004;292(23):28802889.
  18. Shanafelt TD, Bradley KA, Wipf JE, Back AL. Burnout and self‐reported patient care in an internal medicine residency program. Ann Intern Med. 2002;136:358367.
  19. Rosen IM, Gimotty PA, Shea JA, Bellini LM. Evolution of sleep quantity, sleep deprivation, mood disturbances, empathy, and burnout among interns. Acad Med. 2006;81:8285.
  20. Spitzer RL, Williams JB, Kroenke K, et al. Utility of a new procedure for diagnosing mental disorders in primary care: the PRIME‐MD 1000 study. JAMA. 1994;272:17491756.
  21. Whooley MA, Avins AL, Miranda J, Browner WS. Case‐finding instruments for depression: two questions are as good as many. J Gen Intern Med. 1997;12:439445.
  22. Meehan PJ, Lamb JA, Saltzman LE, O'Carroll PW. Attempted suicide among young adults: progress toward a meaningful estimate of prevalence. Am J Psychiatry. 1992;149:4144.
  23. Kessler RC, Borges G, Walters EE. Prevalence of and risk factors for lifetime suicide attempts in the National Comorbidity Survey. Arch Gen Psychiatry. 1999;56:617626.
  24. Kessler RC, Berglund P, Borges G, Nock M, Wang PS. Trends in suicide ideation, plans, gestures, and attempts in the United States, 1990–1992 to 2001–2003. JAMA. 2005;293:24872495.
  25. Cooper‐Patrick L, Crum RM, Ford DE. Identifying suicidal ideation in general medical patients. JAMA. 1994;272:17571762.
  26. Gudex C, Dolan P, Kind P, Williams A. Health state valuations from the general public using the visual analogue scale. Qual Life Res. 1996;5:521531.
  27. Shanafelt TD, Novotny P, Johnson ME, et al. The well‐being and personal wellness promotion strategies of medical oncologists in the North Central Cancer Treatment Group. Oncology. 2005;68:2332.
  28. Rummans TA, Clark MM, Sloan JA, et al. Impacting quality of life for patients with advanced cancer with a structured multidisciplinary intervention: a randomized controlled trial. J Clin Oncol. 2006;24:635642.
  29. West CP, Tan AD, Habermann TM, Sloan JA, Shanafelt TD. Association of resident fatigue and distress with perceived medical errors. JAMA. 2009;302:294300.
  30. Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol consumption questions (AUDIT‐C): an effective brief screening test for problem drinking. Arch Intern Med. 1998;158:17891795.
  31. Hinami K, Whelan CT, Wolosin RJ, Miller JA, Wetterneck TB. Worklife and satisfaction of hospitalists: toward flourishing careers. J Gen Intern Med. 2012;27(1):2836.
  32. Krasner MS, Epstein RM, Beckman H, et al. Association of an educational program in mindful communication with burnout, empathy, and attitudes among primary care physicians. JAMA. 2009;302(12):12841293.
  33. Lee FJ, Stewart M, Brown JB. Stress, burnout, and strategies for reducing them: what's the situation among Canadian family physicians? Can Fam Physician. 2008;54(2):234235.
  34. Lucas B, Trick W, Evans A, et al. Emotional exhaustion, life stress, and perceived control among medicine ward attending physicians: a randomized trial of 2‐ versus 4‐week ward rotations [abstract]. J Hosp Med. 2011;6(4 suppl 2):S43S44.
  35. Shanafelt TD, Balch CM, Dyrbye L, et al. Special report: suicidal ideation among American surgeons. Arch Surg. 2011;146:5462.
  36. Arora V, Fang MC, Kripalani S, Amin AN. Preparing for “diastole”: advanced training opportunities for academic hospitalists. J Hosp Med. 2006;1:368377.
  37. Hughes PH, Brandenburg N, Baldwin DC, et al. Prevalence of substance use among US physicians. JAMA. 1992;267:23332339.
  38. Bazargan M, Makar M, Bazargan‐Hejazi S, Ani C, Wolf KE. Preventive, lifestyle, and personal health behaviors among physicians. Acad Psychiatry. 2009;33:289295.
  39. Oreskovich MR, Kaups KL, Balch CM, et al. Prevalence of alcohol use disorders among American surgeons. Arch Surg. 2012;147:168174.
  40. Gross CP, Mead LA, Ford DE, Klag MJ. Physician, heal thyself? Regular source of care and use of preventive health services among physicians. Arch Intern Med. 2000;160:32093214.
  41. Allegra CJ, Hall R, Yothers G. Prevalence of burnout in the U.S. oncologic community: results of a 2003 survey. J Oncol Pract. 2005;1(4):140147.
  42. Kuerer HM, Eberlein TJ, Pollock RE, et al. Career satisfaction, practice patterns and burnout among surgical oncologists: report on the quality of life of members of the Society of Surgical Oncology. Ann Surg Oncol. 2007;14:30423053.
  43. Shanafelt TD, Balch CM, Bechamps G, et al. Burnout and career satisfaction among American surgeons. Ann Surg. 2009;250(3):463471.
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A national comparison of burnout and work‐life balance among internal medicine hospitalists and outpatient general internists
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Physician Burnout Meta‐analysis

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Burnout in inpatient‐based versus outpatient‐based physicians: A systematic review and meta‐analysis

Hospital medicine is a rapidly growing field of US clinical practice.[1] Almost since its advent, concerns have been expressed about the potential for hospitalists to burn out.[2] Hospitalists are not unique in this; similar concerns heralded the arrival of other location‐defined specialties, including emergency medicine[3] and the full‐time intensivist model,[4] a fact that has not gone unnoted in the literature about hospitalists.[5]

The existing international literature on physician burnout provides good reason for this concern. Inpatient‐based physicians tend to work unpredictable schedules, with substantial impact on home life.[6] They tend to be young, and much burnout literature suggests a higher risk among younger, less‐experienced physicians.[7] When surveyed, hospitalists have expressed more concerns about their potential for burnout than their outpatient‐based colleagues.[8]

In fact, data suggesting a correlation between inpatient practice and burnout predate the advent of the US hospitalist movement. Increased hospital time was reported to correlate with higher rates of burnout in internists,[9] family practitioners,[10] palliative physicians,[11] junior doctors,[12] radiologists,[13] and cystic fibrosis caregivers.[14] In 1987, Keinan and Melamed[15] noted, Hospital work by its very nature, as compared to the work of a general practitioner, deals with the more severe and complicated illnesses, coupled with continuous daily contacts with patients and their anxious families. In addition, these physicians may find themselves embroiled in the power struggles and competition so common in their work environment.

There are other features, however, that may protect inpatient physicians from burnout. Hospital practice can facilitate favorable social relations involving colleagues, co‐workers, and patients,[16] a factor that may be protective.[17] A hospitalist schedule also can allow more focused time for continuing medical education, research, and teaching,[18] which have all been associated with reduced risk of burnout in some studies.[17] Studies of psychiatrists[19] and pediatricians[20] have shown a lower rate of burnout among physicians with more inpatient duties. Finally, a practice model involving a seemingly stable cadre of inpatient physicians has existed in Europe for decades,[2] indicating at least a degree of sustainability.

Information suggesting a higher rate of burnout among inpatient physicians could be used to target therapeutic interventions and to adjust schedules, whereas the opposite outcome could refute a pervasive myth. We therefore endeavored to summarize the literature on burnout among inpatient versus outpatient physicians in a systematic fashion, and to include data not only from the US hospitalist experience but also from other countries that have used a similar model for decades. Our primary hypothesis was that inpatient physicians experience more burnout than outpatient physicians.

It is important to distinguish burnout from depression, job dissatisfaction, and occupational stress, all of which have been studied extensively in physicians. Burnout, as introduced by Freudenberger[21] and further characterized by Maslach,[22] is a condition in which emotional exhaustion, depersonalization, and a low sense of personal accomplishment combine to negatively affect work life (as opposed to clinical depression, which affects all aspects of life). Job satisfaction can correlate inversely with burnout, but it is a separate process[23] and the subject of a recent systematic review.[24] The importance of distinguishing burnout from job dissatisfaction is illustrated by a survey of head and neck surgeons, in which 97% of those surveyed indicated satisfaction with their jobs and 34% of the same group answered in the affirmative when asked if they felt burned out.[25]

One obstacle to the meaningful comparison of burnout prevalence across time, geography, and specialty is the myriad ways in which burnout is measured and reported. The oldest and most commonly used instrument to measure burnout is the Maslach Burnout Inventory (MBI), which contains 22 items assessing 3 components of burnout (emotional exhaustion, depersonalization, and low personal accomplishment).[26] Other measures include the Copenhagen Burnout Inventory[27] (19 items with the components personal burnout, work‐related burnout, and client‐related burnout), Utrecht Burnout Inventory[28] (20‐item modification of the MBI), Boudreau Burnout Questionnaire[29] (30 items), Arbeitsbezogenes Verhaltens und Erlebensmuster[30] (66‐item questionnaire assessing professional commitment, resistance to stress, and emotional well‐being), Shirom‐Melamed Burnout Measure[31] (22 items with subscales for physical fatigue, cognitive weariness, tension, and listlessness), and a validated single‐item questionnaire.[32]

METHODS

Electronic searches of MEDLINE, EMBASE, PsycINFO, SCOPUS, and PubMed were undertaken for articles published from January 1, 1974 (the year in which burnout was first described by Freudenberger[21]) to 2012 (last accessed, September 12, 2012) using the Medical Subject Headings (MeSH) terms stress, psychological; burnout, professional; adaptation, psychological; and the keyword burnout. The same sources were searched to create another set for the MeSH terms hospitalists, physician's practice patterns, physicians/px, professional practice location, and the keyword hospitalist#. Where exact subject headings did not exist in databases, comparable subject headings or keywords were used. The 2 sets were then combined using the operator and. Abstracts from the Society of Hospital Medicine annual conferences were hand‐searched, as were reference lists from identified articles. To ensure that pertinent international literature was captured, there was no language restriction in the search.

A 2‐stage screening process was used. The titles and abstracts of all articles identified in the search were independently reviewed by 2 investigators (D.L.R. and K.J.C.) who had no knowledge of each other's results. An article was obtained when either reviewer deemed it worthy of full‐text review.

All full‐text articles were independently reviewed by the same 2 investigators. The inclusion criterion was the measurement of burnout in physicians who are stated to or can be reasonably assumed to spend the substantial majority of their clinical practice exclusively in either the inpatient or the outpatient setting. Studies of emergency department physicians or specialists who invariably spend substantial amounts of time in both settings (eg, surgeons, anesthesiologists) were excluded. Studies limited to trainees or nonphysicians were also excluded. For both stages of review, agreement between the 2 investigators was assessed by calculating the statistic. Disagreements about inclusion were adjudicated by a third investigator (A.I.B.).

Because our goal was to establish and compare the rate of burnout among US hospitalists and other inpatient physicians around the world, we included studies of hospitalists according to the definition in use at the time of the individual study, noting that the formal definition of a hospitalist has changed over the years.[33] Because practice patterns for physicians described as primary care physicians, family doctors, hospital doctors, and others differ substantially from country to country, we otherwise included only the studies where the practice location was stated explicitly or where the authors confirmed that their study participants either are known or can be reasonably assumed to spend more than 75% of their time caring for hospital inpatients, or are known or can be reasonably assumed to spend the vast majority of their time caring for outpatients.

Data were abstracted using a standardized form and included the measure of burnout used in the study, results, practice location of study subjects, and total number of study subjects. When data were not clear (eg, burnout measured but not reported by the authors, practice location of study subjects not clear), authors were contacted by email, or when no current email address could be located or no response was received, by telephone or letter. In instances where burnout was measured repeatedly over time or before and after a specific intervention, only the baseline measurement was recorded. Because all studies were expected to be nonrandomized, methodological quality was assessed using a version of the tool of Downs and Black,[34] adapted where necessary by omitting questions not applicable to the specific study type (eg randomization for survey studies)[35] and giving a maximum of 1 point for the inclusion of a power calculation.

Two a priori analyses were planned: (1) a statistical comparison of articles directly comparing burnout among inpatient and outpatient physicians, and (2) a statistical comparison of articles measuring burnout among inpatient physicians with articles measuring burnout among outpatient physicians by the most frequently reported measuremean subset scores for emotional exhaustion, depersonalization, and personal accomplishment on the MBI.

The primary outcome measures were the differences between mean subset scores for emotional exhaustion, depersonalization, and personal accomplishment on the MBI. All differences are expressed as (outpatient meaninpatient mean). The variance of each outcome was calculated with standard formulas.[36] To calculate the overall estimate, each study was weighted by the reciprocal of its variance. Studies with fewer than 10 subjects were excluded from statistical analysis but retained in the systematic review.

For studies that reported data for both inpatient and outpatient physicians (double‐armed studies), Cochran Q test and the I2 value were used to assess heterogeneity.[37, 38] Substantial heterogeneity was expected because these individual studies were conducted for different populations in different settings with different study designs, and this expectation was confirmed statistically. Therefore, we used a random effects model to estimate the overall effect, providing a conservative approach that accounted for heterogeneity among studies.[39]

To assess the durability of our findings, we performed separate multivariate meta‐regression analyses by including single‐armed studies only and including both single‐armed and double‐armed studies. For these meta‐regressions, means were again weighted by the reciprocal of their variances, and the arms of 2‐armed studies were considered separately. This approach allowed us to generate an estimate of the differences between MBI subset scores from studies that did not include such an estimate when analyzed separately.[40]

We examined the potential for publication bias in double‐armed studies by constructing a funnel plot, in which mean scores were plotted against their standard errors.[41] The trim‐and‐fill method was used to determine whether adjustment for publication bias was necessary. In addition, Begg's rank correlation test[42] was completed to test for statistically significant publication bias.

Stata 10.0 statistical software (StataCorp, College Station, TX) was used for data analyses. A P value of 0.05 or less was deemed statistically significant. The Preferred Reporting Items for Systematic Reviews and Meta‐analysis checklist was used for the design and execution of the systematic review and meta‐analysis.[43]

Subgroup analyses based on location were undertaken a posteriori. All data (double‐armed meta‐analysis, meta‐regression of single‐armed studies, and meta‐regression of single‐ and double‐armed studies) were analyzed by location (United States vs other; United States vs Europe vs other).

RESULTS

The search results are outlined in Figure 1. In total, 1704 articles met the criteria for full‐text review. A review of pertinent reference lists and author contacts led to the addition of 149 articles. Twenty‐nine references could not be located by any means, despite repeated attempts. Therefore, 1824 articles were subjected to full‐text review by the 2 investigators.

Figure 1
Flow chart of study selection.

Initially, 57 articles were found that met criteria for inclusion. Of these, 2 articles reported data in formats that could not be interpreted.[44, 45] When efforts to clarify the data with the authors were unsuccessful, these studies were excluded. A study specifically designed to assess the response of physicians to a recent series of terrorist attacks[46] was excluded a posteriori because of lack of generalizability. Of the other 54 studies, 15 reported burnout data on both outpatient physicians and inpatient physicians, 22 reported data on outpatient physicians only, and 17 reported data on inpatient physicians only. Table 1 summarizes the results of the 37 studies involving outpatient physicians; Table 2 summarizes the 32 studies involving inpatient physicians.

Summaries of Studies of Outpatient‐Based Physicians Meeting Inclusion Criteria
Lead Author, Publication Year Study Population and Location Instrument No. of Participants EE Score (SD)a DP Score (SD) PA Score (SD) Other Results
  • NOTE: Abbreviations: AVEM, Arbeitsbezogenes Verhaltens und Erlebensmuster; DP, depersonalization subset of MBI; EE, emotional exhaustion subset of MBI; MBI, Maslach Burnout Inventory; PA, personal accomplishment subset of MBI; SMBM, Shirom‐Melamad Burnout Measure; SD, standard deviation.

  • High scores of EE and DP and low scores of PA are features of burnout.

  • Data obtained directly from authors 20102012.

  • SDs calculated from published standard errors. Personal accomplishment scale reversed to match other studies.

  • SDs calculated from published CIs.

Schweitzer, 1994[12] Young physicians of various specialties in South Africa Single‐item survey 7 6 (83%) endorsed burnout
Aasland, 1997 [54]b General practitioners in Norway Modified MBI (22 items; scale, 15) 298 2.65 (0.80) 1.90 (0.59) 3.45 (0.40)
Grassi, 2000 [58] General practitioners in Italy MBI 182 18.49 (11.49) 6.11 (5.86) 38.52 (7.60)
McManus, 2000 [59]b General practitioners in United Kingdom Modified MBI (9 items; scale, 06) 800 8.34 (4.39) 3.18 (3.40) 14.16 (2.95)
Yaman, 2002 [60] General practitioners in 8 European nations MBI 98 25.1 (8.50) 7.3 (4.92) 34.5 (7.67)
Cathbras, 2004 [61] General practitioners in France MBI 306 21.85 (12.4) 9.13 (6.7) 38.7 (7.1)
Goehring, 2005 [63] General practitioners, general internists, pediatricians in Switzerland MBI 1755 17.9 (9.8) 6.5 (4.7) 39.6 (6.5)
Esteva, 2006 [64] General practitioners, pediatricians in Spain MBI 261 27.4 (11.8) 10.07 (6.4) 35.9 (7.06)
Gandini, 2006 [65]b Physicians of various specialties in Argentina MBI 67 31.0 (13.8) 10.2 (6.6) 38.4 (6.8)
Ozyurt, 2006 [66] General practitioners in Turkey Modified MBI (22 items; scale, 04) 55 15.23 (5.80) 4.47 (3.31) 23.38 (4.29)
Deighton, 2007 [67]b Psychiatrists in several German‐speaking nations MBI 19 30.68 (9.92) 13.42 (4.23) 37.16 (3.39)
Dunwoodie, 2007 [68]b Palliative care physicians in Australia MBI 21 14.95 (9.14) 3.95 (3.40) 38.90 (2.88)
Srgaard, 2007 [69]b Psychiatrists in 5 European nations MBI 22 19.41 (8.08) 6.68 (4.93) 39.00 (4.40)
Sosa Oberlin, 2007 [56]b Physicians of various specialties in Argentina Author‐designed instrument 33 26 (78.8%) had 4 burnout symptoms, 6.15 symptoms per physician
Voltmer, 2007 [57]b Physicians of various specialties in Germany AVEM 46 11 (23.9%) exhibited burnout (type B) pattern
dm, 2008 [70]b Physicians of various specialties in Hungary MBI 163 17.45 (11.12) 4.86 (4.91) 36.56 (7.03)
Di Iorio, 2008 [71]b Dialysis physicians in Italy Author‐designed instrument 54 Work: 2.6 (1.5), Material: 3.1 (2.1), Climate: 3.0 (1.1), Objectives: 3.4 (1.6), Quality: 2.2 (1.5), Justification: 3.2 (2.0)
Lee, 2008 [49]b Family physicians in Canada MBI 123 26.26 (9.53) 10.20 (5.22) 38.43 (7.34)
Truchot, 2008 [72] General practitioners in France MBI 259 25.4 (11.7) 7.5 (5.5) 36.5 (7.1)
Twellaar, 2008 [73]b General practitioners in the Netherlands Utrecht Burnout Inventory 349 2.06 (1.11) 1.71 (1.05) 5.08 (0.77)
Arigoni, 2009 [17] General practitioners, pediatricians in Switzerland MBI 258 22.8 (12.0) 6.9 (6.1) 39.0 (7.2)
Bernhardt, 2009 [75] Clinical geneticists in United States MBI 72 25.8 (10.01)c 10.9 (4.16)c 34.8 (5.43)c
Bressi, 2009 [76]b Psychiatrists in Italy MBI 53 23.15 (11.99) 7.02 (6.29) 36.41 (7.54)
Krasner, 2009 [77] General practitioners in United States MBI 60 26.8 (10.9)d 8.4 (5.1)d 40.2 (5.3)d
Lasalvia, 2009 [55]b Psychiatrists in Italy Modified MBI (16 items; scale, 06) 38 2.37 (1.27) 1.51 (1.15) 4.46 (0.87)
Peisah, 2009 [79]b Physicians of various specialties in Australia MBI 28 13.92 (9.24) 3.66 (3.95) 39.34 (8.55)
Shanafelt, 2009 [80]b Physicians of various specialties in United States MBI 408 20.5 (11.10) 4.3 (4.74) 40.8 (6.26)
Zantinge, 2009 [81] General practitioners in the Netherlands Utrecht Burnout Inventory 126 1.58 (0.79) 1.32 (0.72) 4.27 (0.77)
Voltmer, 2010 [83]b Psychiatrists in Germany AVEM 526 114 (21.7%) exhibited burnout (type B) pattern
Maccacaro, 2011 [85]b Physicians of various specialties in Italy MBI 42 14.31 (11.98) 3.62 (4.95) 38.24 (6.22)
Lucas, 2011 [84]b Outpatient physicians periodically staffing an academic hospital teaching service in United States MBI (EE only) 30 24.37 (14.95)
Shanafelt, 2012 [87]b General internists in United States MBI 447 25.4 (14.0) 7.5 (6.3) 41.4 (6.0)
Kushnir, 2004 [62] General practitioners and pediatricians in Israel MBI (DP only) and SMBM 309 9.15 (3.95) SMBM mean (SD), 2.73 per item (0.86)
Vela‐Bueno, 2008 [74]b General practitioners in Spain MBI 240 26.91 (11.61) 9.20 (6.35) 35.92 (7.92)
Lesic, 2009 [78]b General practitioners in Serbia MBI 38 24.71 (10.81) 7.47 (5.51) 37.21 (7.44)
Demirci, 2010 [82]b Medical specialists related to oncology practice in Hungary MBI 26 23.31 (11.2) 6.46 (5.7) 37.7 (8.14)
Putnik, 2011 [86]b General practitioners in Hungary MBI 370 22.22 (11.75) 3.66 (4.40) 41.40 (6.85)
Summary of Studies of Inpatient Physicians Meeting Inclusion Criteria
Lead Author, Publication Year Study Population and Location Instrument No. of Participants EE Score (SD)a DP Score (SD) PA Score (SD) Other Results
  • NOTE: Abbreviations: AVEM, Arbeitsbezogenes Verhaltens und Erlebensmuster; DP, depersonalization subset of MBI; EE, emotional exhaustion subset of MBI; MBI, Maslach Burnout Inventory; PA, personal accomplishment subset of MBI; SD, standard deviation.

  • High scores of EE and DP and low scores of PA are features of burnout.

  • SDs not available; study therefore excluded from statistical comparisons.

  • Different survey item than other studies in this table using a single‐item, 5‐point burnout measure.

  • Data obtained directly from authors 20102012.

  • Personal accomplishment scale reversed to match other studies.

Varga, 1996 [88] Hospital doctors in Spain MBI 179 21.61b 7.33b 35.28b
Aasland, 1997 [54] Hospital doctors in Norway Modified MBI (22 items; scale, 15) 582 2.39 (0.80) 1.81 (0.65) 3.51 (0.46)
Bargellini, 2000 [89] Hospital doctors in Italy MBI 51 17.45 (9.87) 7.06 (5.54) 35.33 (7.90)
Grassi, 2000 [58] Hospital doctors in Italy MBI 146 16.17 (9.64) 5.32 (4.76) 38.71 (7.28)
Hoff, 2001 [33] Hospitalists in United States Single‐item surveyc 393 12.9% burned out (>4/5), 24.9% at risk for burnout (34/5), 62.2% at no current risk (mean, 2.86 on 15 scale)
Trichard, 2005 [90] Hospital doctors in France MBI 199 16 (10.7) 6.6 (5.7) 38.5 (6.5)
Gandini, 2006 [65]d Hospital doctors in Argentina MBI 290 25.0 (12.7) 7.9 (6.2) 40.1 (7.0)
Dunwoodie, 2007 [68]d Palliative care doctors in Australia MBI 14 18.29 (14.24) 5.29 (5.89) 38.86 (3.42)
Srgaard, 2007 [69]d Psychiatrists in 5 European nations MBI 18 18.56 (9.32) 5.50 (3.79) 39.08 (5.39)
Sosa Oberlin, 2007 [56]d Hospital doctors in Argentina Author‐designed instrument 3 3 (100%) had 4 burnout symptoms, 8.67 symptoms per physician
Voltmer, 2007 [57]d Hospital doctors in Germany AVEM 271 77 (28.4%) exhibited burnout (type B) pattern
dm, 2008 [70]b Physicians of various specialties in Hungary MBI 194 19.23 (10.79) 4.88 (4.61) 35.26 (8.42)
Di Iorio, 2008 [71]d Dialysis physicians in Italy Author‐designed instrument 62 Work, mean (SD), 3.1 (1.4); Material, mean (SD), 3.3 (1.5); Climate, mean (SD), 2.9 (1.1); Objectives, mean (SD), 2.5 (1.5); Quality, mean (SD), 3.0 (1.1); Justification, mean (SD), 3.1 (2.1)
Fuss, 2008 [91]d Hospital doctors in Germany Copenhagen Burnout Inventory 292 Mean Copenhagen Burnout Inventory, mean (SD), 46.90 (18.45)
Marner, 2008 [92]d Psychiatrists and 1 generalist in United States MBI 9 20.67 (9.75) 7.78 (5.14) 35.33 (6.44)
Shehabi, 2008 [93]d Intensivists in Australia Modified MBI (6 items; scale, 15) 86 2.85 (0.93) 2.64 (0.85) 2.58 (0.83)
Bressi, 2009 [76]d Psychiatrists in Italy MBI 28 17.89 (14.46) 5.32 (7.01) 34.57 (11.27)
Brown, 2009 [94] Hospital doctors in Australia MBI 12 22.25 (8.59) 6.33 (2.71) 39.83 (7.31)
Lasalvia, 2009 [55]d Psychiatrists in Italy Modified MBI (16 items; scale, 06) 21 1.95 (1.04) 1.35 (0.85) 4.46 (1.04)
Peisah, 2009 [79]d Hospital doctors in Australia MBI 62 20.09 (9.91) 6.34 (4.90) 35.06 (7.33)
Shanafelt, 2009 [80]d Hospitalists and intensivists in United States MBI 19 25.2 (11.59) 4.4 (3.79) 38.5 (8.04)
Tunc, 2009 [95] Hospital doctors in Turkey Modified MBI (22 items; scale, 04) 62 1.18 (0.78) 0.81 (0.73) 3.10 (0.59)e
Cocco, 2010 [96]d Hospital geriatricians in Italy MBI 38 16.21 (11.56) 4.53 (4.63) 39.13 (7.09)
Doppia, 2011 [97]d Hospital doctors in France Copenhagen Burnout Inventory 1,684 Mean work‐related burnout score, 2.72 (0.75)
Glasheen, 2011 [98] Hospitalists in United States Single‐item survey 265 Mean, 2.08 on 15 scale 62 (23.4%) burned out
Lucas, 2011 [84]d Academic hospitalists in United States MBI (EE only) 26 19.54 (12.85)
Thorsen, 2011 [99] Hospital doctors in Malawi MBI 2 25.5 (4.95) 8.5 (6.36) 25.0 (5.66)
Hinami, 2012 [50]d Hospital doctors in United States Single‐item survey 793 Mean, 2.24 on 15 scale 261 (27.2%) burned out
Quenot, 2012 [100]d Intensivists in France MBI 4 33.25 (4.57) 13.50 (5.45) 35.25 (4.86)
Ruitenburg, 2012 [101] Hospital doctors in the Netherlands MBI (EE and DP only) 214 13.3 (8.0) 4.5 (4.1)
Seibt, 2012 [102]d Hospital doctors in Germany Modified MBI (16 items; scale, 06, reported per item rather than totals) 2,154 2.2 (1.4) 1.4 (1.2) 5.1 (0.9)
Shanafelt, 2012 [87]d Hospitalists in United States MBI 130 24.7 (12.5) 9.1 (6.9) 39.0 (7.6)

Table 3 summarizes the results of the 15 studies that reported burnout data for both inpatient and outpatient physicians, allowing direct comparisons to be made. Nine studies reported MBI subset totals with standard deviations, 2 used different modifications of the MBI, 2 used different author‐derived measures, 1 used only the emotional exhaustion subscale of the MBI, and 1 used the Arbeitsbezogenes Verhaltens und Erlebensmuster. Therefore, statistical comparison was attempted only for the 9 studies reporting comparable MBI data, comprising burnout data on 1390 outpatient physicians and 899 inpatient physicians.

Summary of Studies Including Both Inpatient‐Based and Outpatient‐Based Physicians Meeting Inclusion Criteria
Lead Author, Publication Year Location Instrument Inpatient‐Based Physicians Outpatient‐Based Physicians
No. Results, Score (SD)a No. Results, Score (SD)a
  • NOTE: Abbreviations: AVEM, Arbeitsbezogenes Verhaltens und Erlebensmuster; DP, depersonalization subset of MBI; EE, emotional exhaustion subset of MBI; MBI, Maslach Burnout Inventory; PA, personal accomplishment subset of MBI; SD, standard deviation.

  • High scores of EE and DP and low scores of PA are features of burnout.

  • Data obtained directly from authors from 20102012.

Aasland, 1997 [54]b Norway Modified MBI (22 items; scale, 15) 582 EE, 2.39 (0.80); DP, 1.81 (0.65); PA, 3.51 (0.46) 298 EE, 2.65 (0.80); DP, 1.90 (0.59); PA, 3.45 (0.40)
Grassi, 2000 [58] Italy MBI 146 EE, 16.17 (9.64); DP, 5.32 (4.76); PA, 38.71 (7.28) 182 EE, 18.49 (11.49); DP, 6.11 (5.86); PA, 38.52 (7.60)
Gandini, 2006 [65]b Argentina MBI 290 EE, 25.0 (12.7);DP, 7.9 (6.2); PA, 40.1 (7.0) 67 EE, 31.0 (13.8); DP, 10.2 (6.6); PA, 38.4 (6.8)
Dunwoodie, 2007 [68]b Australia MBI 14 EE, 18.29 (14.24); DP, 5.29 (5.89); PA, 38.86 (3.42) 21 EE, 14.95 (9.14); DP, 3.95 (3.40); PA, 38.90 (2.88)
Srgaard, 2007 [69]b 5 European nations MBI 18 EE, 18.56 (9.32); DP, 5.50 (3.79); PA, 39.08 (5.39) 22 EE, 19.41 (8.08); DP, 6.68 (4.93); PA, 39.00 (4.40)
Sosa Oberlin, 2007 [56]b Argentina Author‐designed instrument 3 3 (100%) had 4 burnout symptoms, 8.67 symptoms per physician 33 26 (78.8%) had 4 burnout symptoms, 6.15 symptoms per physician
Voltmer, 2007 [57]b Germany AVEM 271 77 (28.4%) exhibited burnout (type B) pattern 46 11 (23.9%) exhibited burnout (type B) pattern
dm, 2008 [70]b Hungary MBI 194 EE, 19.23 (10.79); DP, 4.88 (4.61); PA, 35.26 (8.42) 163 EE, 17.45 (11.12); DP, 4.86 (4.91); PA, 36.56 (7.03)
Di Iorio, 2008 [71]b Italy Author‐designed instrument 62 Work: 3.1 (1.4); material: 3.3 (1.5); climate: 2.9 (1.1); objectives: 2.5 (1.5); quality: 3.0 (1.1); justification: 3.1 (2.1) 54 Work: 2.6 (1.5); material: 3.1 (2.1); climate: 3.0 (1.1); objectives: 3.4 (1.6); quality: 2.2 (1.5); justification: 3.2 (2.0)
Bressi, 2009 [76]b Italy MBI 28 EE, 17.89 (14.46); DP, 5.32 (7.01); PA, 34.57 (11.27) 53 EE, 23.15 (11.99); DP, 7.02 (6.29); PA, 36.41 (7.54)
Lasalvia, 2009[55]b Italy Modified MBI (16 items; scale, 06) 21 EE, 1.95 (1.04); DP, 1.35 (0.85); PA, 4.46 (1.04) 38 EE, 2.37 (1.27); DP, 1.51 (1.15); PA, 4.46 (0.87)
Peisah, 2009 [79]b Australia MBI 62 EE, 20.09 (9.91); DP, 6.34 (4.90); PA, 35.06 (7.33) 28 EE, 13.92 (9.24); DP, 3.66 (3.95); PA, 39.34 (8.55)
Shanafelt, 2009 [80]b United States MBI 19 EE, 25.2 (11.59); DP, 4.4 (3.79); PA, 38.5 (8.04) 408 EE, 20.5 (11.10); DP, 4.3 (4.74); PA, 40.8 (6.26)
Lucas, 2011 [84]b United States MBI (EE only) 26 EE, 19.54 (12.85) 30 EE, 24.37 (14.95)
Shanafelt, 2012 [87]b United States MBI 130 EE, 24.7 (12.5); DP, 9.1 (6.9); PA, 39.0 (7.6) 447 EE, 25.4 (14.0); DP, 7.5 (6.3); PA, 41.4 (6.0)

Figure 2 shows that no significant difference existed between the groups regarding emotional exhaustion (mean difference, 0.11 points on a 54‐point scale; 95% confidence interval [CI], 2.40 to 2.61; P=0.94). In addition, there was no significant difference between the groups regarding depersonalization (Figure 3; mean difference, 0.00 points on a 30‐point scale; 95% CI, 1.03 to 1.02; P=0.99) and personal accomplishment (Figure 4; mean difference, 0.93 points on a 48‐point scale; 95% CI, 0.23 to 2.09; P=0.11).

Figure 2
Forest plot for double‐armed studies reporting Maslach Burnout Inventory scores for emotional exhaustion. The size of the square represents study size, and the bars represent the 95% confidence interval (CI).
Figure 3
Forest plot for double‐armed studies reporting Maslach Burnout Inventory scores for depersonalization. The size of the square represents study size and the bars represent the 95% confidence interval (CI).
Figure 4
Forest plot for double‐armed studies reporting Maslach Burnout Inventory scores for personal accomplishment. The size of the square represents study size and the bars represent the 95% confidence interval (CI). The direction of the y‐axis has been reversed so that greater burnout in outpatient physicians remains to the right.

We used meta‐regression to allow the incorporation of single‐armed MBI studies. Whether single‐armed studies were analyzed separately (15 outpatient studies comprising 3927 physicians, 4 inpatient studies comprising 300 physicians) or analyzed with double‐armed studies (24 outpatient arms comprising 5318 physicians, 13 inpatient arms comprising 1301 physicians), the lack of a significant difference between the groups persisted for the depersonalization and personal accomplishment scales (Figure 5). Emotional exhaustion was significantly higher in outpatient physicians when single‐armed studies were considered separately (mean difference, 6.36 points; 95% CI, 2.24 to 10.48; P=0.002), and this difference persisted when all studies were combined (mean difference, 3.00 points; 95% CI, 0.05 to 5.94, P=0.046).

Figure 5
Forest plots comparing results of meta‐analysis of 9 double‐armed studies, meta‐regression of 19 single‐armed studies, and meta‐regression of all 28 studies reporting Maslach Burnout Inventory scores. The direction of the y‐axis of the personal accomplishment plot has been reversed so that higher burnout in outpatient physicians remains to the right. Error bars represent the 95% confidence interval.

Subgroup analysis by geographic location showed US outpatient physicians had a significantly higher personal accomplishment score than US inpatient physicians (mean difference, 2.38 points; 95% CI, 1.22 to 3.55; P<0.001) in double‐armed studies. This difference did not persist when single‐armed studies were included through meta‐regression (mean difference, 0.55 points, 95% CI, 4.30 to 5.40, P=0.83).

Table 4 demonstrates that methodological quality was generally good from the standpoint of the reporting and bias subsections of the Downs and Black tool. External validity was scored lower for many studies due to the use of convenience samples and lack of information about physicians who declined to participate.

Assessment of Methodologic Quality
Lead Author, Publication Year Reporting External Validity Internal Validity: Bias Internal Validity: Confounding Power
  • NOTE: For survey studies (all studies except Krasner,[77] Lucas,[84] and Quenot[100]), questions regarding interventions were omitted. For uncontrolled studies (all studies except Lucas[84]), questions regarding controls were omitted. The presence of a power calculation was awarded 1 point.

Schweitzer, 1994 [12] 5 of 6 points 1 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Varga, 1996 [88] 5 of 6 points 1 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Aasland, 1997 [54] 3 of 6 points 2 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Bargellini, 2000 [89] 5 of 6 points 0 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Grassi, 2000 [58] 6 of 6 points 0 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
McManus, 2000 [59] 5 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Hoff, 2001 [33] 6 of 6 points 2 of 2 points 2 of 4 points 1 of 1 point 0 of 1 point
Yaman, 2002 [60] 5 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Cathbras, 2004 [61] 6 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Kushnir, 2004 [62] 5 of 6 points 0 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Goehring, 2005 [63] 6 of 6 points 1 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Trichard, 2005 [90] 3 of 6 points 1 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Esteva, 2006 [64] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Gandini, 2006 [65] 6 of 6 points 1 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Ozyurt, 2006 [66] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Deighton, 2007 [67] 5 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Dunwoodie, 2007 [68] 5 of 6 points 2 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Srgaard, 2007 [69] 6 of 6 points 0 of 2 points 3 of 4 points 1 of 1 point 1 of 1 point
Sosa Oberlin, 2007 [56] 4 of 6 points 0 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Voltmer, 2007 [57] 4 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
dm, 2008 [70] 5 of 6 points 2 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Di Iorio, 2008 [71] 6 of 6 points 0 of 2 points 2 of 4 points 0 of 1 point 0 of 1 point
Fuss, 2008 [91] 6 of 6 points 0 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Lee, 2008 [49] 4 of 6 points 2 of 2 points 4 of 4 points 0 of 1 point 1 of 1 point
Marner, 2008 [92] 6 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Shehabi, 2008 [93] 3 of 6 points 1 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Truchot, 2008 [72] 5 of 6 points 1 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Twellaar, 2008 [73] 6 of 6 points 2 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Vela‐Bueno, 2008 [74] 5 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Arigoni, 2009 [17] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Bernhardt, 2009 [75] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Bressi, 2009 [76] 6 of 6 points 0 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Brown, 2009 [94] 6 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Krasner, 2009 [77] 9 of 11 points 0 of 3 points 6 of 7 points 1 of 2 points 1 of 1 point
Lasalvia, 2009 [55] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Lesic, 2009 [78] 5 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Peisah, 2009 [79] 6 of 6 points 2 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Shanafelt, 2009 [80] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Tunc, 2009 [95] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Zantinge, 2009 [81] 5 of 6 points 0 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Cocco, 2010 [96] 4 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Demirci, 2010 [82] 6 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Voltmer, 2010 [83] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Doppia, 2011 [97] 5 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Glasheen, 2011 [98] 5 of 6 points 0 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Lucas, 2011 [84] 10 of 11 points 2 of 3 points 7 of 7 points 5 of 6 points 1 of 1 point
Maccacaro, 2011 [85] 5 of 6 points 0 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Putnik, 2011 [86] 6 of 6 points 1 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Thorsen, 2011 [99] 6 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Hinami, 2012 [50] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 1 of 1 point
Quenot, 2012 [100] 8 of 11 points 1 of 3 points 6 of 7 points 1 of 2 points 0 of 1 point
Ruitenburg, 2012 [101] 6 of 6 points 2 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Seibt, 2012 [102] 6 of 6 points 0 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Shanafelt, 2012 [87] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point

Funnel plots were used to evaluate for publication bias in the meta‐analysis of the 8 double‐armed studies (Figure 6). We found no significant evidence of bias, which was supported by Begg's test P values of 0.90 for emotional exhaustion, >0.99 for depersonalization, and 0.54 for personal accomplishment. A trim‐and‐fill analysis determined that no adjustment was necessary.

Figure 6
Funnel plots for the 8 double‐armed studies that reported Maslach Burnout Inventory scores for emotional exhaustion, depersonalization, and personal accomplishment. Abbreviations: CI, confidence interval.

DISCUSSION

There appears to be no support for the long‐held belief that inpatient physicians are particularly prone to burnout. Among studies for which practice location was stated explicitly or could be obtained from the authors, and who used the MBI, no differences were found among inpatient and outpatient physicians with regard to depersonalization or personal accomplishment. This finding persisted whether double‐armed studies were compared directly, single‐armed studies were incorporated into this analysis, or single‐armed studies were analyzed separately. Outpatient physicians had a higher degree of emotional exhaustion when all studies were considered.

There are several reasons why outpatient physicians may be more prone to emotional exhaustion than their inpatient colleagues. Although it is by no means true that all inpatient physicians work in shifts, the increased availability of shift work may allow some inpatient physicians to better balance their professional and personal lives, a factor of work with which some outpatient physicians have struggled.[47] Inpatient practice may also afford more opportunity for teamwork, a factor that has been shown to correlate with reduced burnout.[48] When surveyed about burnout, outpatient physicians have cited patient volumes, paperwork, medicolegal concerns, and lack of community support as factors.[49] Inpatient physicians are not immune to these forces, but they arguably experience them to different degrees.

The absence of a higher rate of depersonalization among inpatient physicians is particularly reassuring in light of concerns expressed with the advent of US hospital medicinethat some hospitalists would be prone to viewing patients as an impediment to the efficient running of the hospital,[2] the very definition of depersonalization.

Although the difference in the whole sample was not statistically significant, the consistent tendency toward a greater sense of personal accomplishment among outpatient physicians is also noteworthy, particularly because post hoc subgroup analysis of US physicians did show statistical significance in both 2‐armed studies. Without detailed age data for the physicians in each study, we could not separate the possible impact of age on personal accomplishment; hospital medicine is a newer specialty staffed by generally younger physicians, and hospitalists may not have had time to develop a sense of accomplishment. When surveyed about job satisfaction, hospitalists have also reported the feeling that they were treated as glorified residents,[50] a factor that, if shared by other inpatient physicians, must surely affect their sense of personal accomplishment. The lack of longitudinal care for patients and the substantial provision of end‐of‐life care also may diminish the sense of personal accomplishment among inpatient physicians.

Another important finding from this systematic review is the marked heterogeneity of the instruments used to measure physician burnout. Many of the identified studies could not be subjected to meta‐analysis because of their use of differing burnout measures. Drawing more substantial conclusions about burnout and practice location is limited by the fact that, although the majority of studies used the full MBI, the largest study of European hospital doctors used the Copenhagen Burnout Inventory, and the studies thus far of US hospitalists have used single‐item surveys or portions of the MBI. Not reflected in this review is the fact that a large study of US burnout and job satisfaction[51] did not formally address practice location (M. Linzer, personal communication, August 2012). Similarly, a large study of British hospital doctors[52] is not included herein because many of the physicians involved had substantial outpatient duties (C. Taylor, personal communication, July 2012). Varying burnout measures have complicated a previous systematic review of burnout in oncologists.[53] Two studies that directly compared inpatient and outpatient physicians but that were excluded from our statistical analysis because of their modified versions of the MBI,[54, 55] showed higher burnout scores in outpatient physicians. Two other studies that provided direct inpatient versus outpatient comparisons but that used alternative burnout measures[56, 57] showed a greater frequency of burnout in inpatient physicians, but of these, 1 study[56] involved only 3 inpatient physicians.

Several limitations of our study should be considered. Although we endeavored to obtain information from authors (with some success) about specific local practice patterns and eliminated many studies because of incomplete data or mixed practice patterns (eg, general practitioners who take frequent hospital calls, hospital physicians with extensive outpatient duties in a clinic attached to their hospital), it remains likely that many physicians identified as outpatient provided some inpatient care (attending a few weeks per year on a teaching service, for example) and that some physicians identified as inpatient have minimal outpatient duties.

More importantly, the dataset analyzed is heterogeneous. Studies of the incidence of burnout are naturally observational and therefore not randomized. Inclusion of international studies is necessary to answer the research question (because published data on US hospitalists are sparse) but naturally introduces differences in practice settings, local factors, and other factors for which we cannot possibly account fully.

Our meta‐analysis therefore addressed a broad question about burnout among inpatient and outpatient physicians in various diverse settings. Applying it to any 1 population (including US hospitalists) is, by necessity, imprecise.

Post hoc analysis should be viewed with caution. For example, the finding of a statistical difference between US inpatient and outpatient physicians with regard to personal accomplishment score is compelling from the standpoint of hypothesis generation. However, it is worth bearing in mind that this analysis contained only 2 studies, both by the same primary author, and compared 855 outpatient physicians to only 149 hospitalists. This difference was no longer significant when 2 outpatient studies were added through meta‐regression.

Finally, the specific focus of this study on practice location precluded comparison with emergency physicians and anesthesiologists, 2 specialist types that have been the subject of particularly robust burnout literature. As the literature on hospitalist burnout becomes more extensive, comparative studies with these groups and with intensivists might prove instructive.

In summary, analysis of 24 studies comprising data on 5318 outpatient physicians and 1301 inpatient physicians provides no support for the commonly held belief that hospital‐based physicians are particularly prone to burnout. Outpatient physicians reported higher emotional exhaustion. Further studies of the incidence and severity of burnout according to practice location are indicated. We propose that in future studies, to avoid the difficulties with statistical analysis summarized herein, investigators ask about and explicitly report practice location (inpatient vs outpatient vs both) and report mean MBI subset data and standard deviations. Such information about US hospitalists would allow comparison with a robust (if heterogeneous) international literature on burnout.

Acknowledgments

The authors gratefully acknowledge all of the study authors who contributed clarification and guidance for this project, particularly the following authors who provided unpublished data for further analysis: Olaf Aasland, MD; Szilvia dm, PhD; Annalisa Bargellini, PhD; Cinzia Bressi, MD, PhD; Darrell Campbell Jr, MD; Ennio Cocco, MD; Russell Deighton, PhD; Senem Demirci Alanyali, MD; Biagio Di Iorio, MD, PhD; David Dunwoodie, MBBS; Sharon Einav, MD; Madeleine Estryn‐Behar, PhD; Bernardo Gandini, MD; Keiki Hinami, MD; Antonio Lasalvia, MD, PhD; Joseph Lee, MD; Guido Maccacaro, MD; Swati Marner, EdD; Chris McManus, MD, PhD; Carmelle Peisah, MBBS, MD; Katarina Putnik, MSc; Alfredo Rodrguez‐Muoz, PhD; Yahya Shehabi, MD; Evelyn Sosa Oberlin, MD; Jean Karl Soler, MD, MSc; Knut Srgaard, PhD; Cath Taylor; Viva Thorsen, MPH; Mascha Twellaar, MD; Edgar Voltmer, MD; Colin West, MD, PhD; and Deborah Whippen. The authors also thank the following colleagues for their help with translation: Dusanka Anastasijevic (Norwegian); Joyce Cheung‐Flynn, PhD (simplified Chinese); Ales Hlubocky, MD (Czech); Lena Jungheim, RN (Swedish); Erez Kessler (Hebrew); Kanae Mukai, MD (Japanese); Eliane Purchase (French); Aaron Shmookler, MD (Russian); Jan Stepanek, MD (German); Fernando Tondato, MD (Portuguese); Laszlo Vaszar, MD (Hungarian); and Joseph Verheidje, PhD (Dutch). Finally, the authors thank Cynthia Heltne and Diana Rogers for their expert and tireless library assistance, Bonnie Schimek for her help with figures, and Cindy Laureano and Elizabeth Jones for their help with author contact.

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Hospital medicine is a rapidly growing field of US clinical practice.[1] Almost since its advent, concerns have been expressed about the potential for hospitalists to burn out.[2] Hospitalists are not unique in this; similar concerns heralded the arrival of other location‐defined specialties, including emergency medicine[3] and the full‐time intensivist model,[4] a fact that has not gone unnoted in the literature about hospitalists.[5]

The existing international literature on physician burnout provides good reason for this concern. Inpatient‐based physicians tend to work unpredictable schedules, with substantial impact on home life.[6] They tend to be young, and much burnout literature suggests a higher risk among younger, less‐experienced physicians.[7] When surveyed, hospitalists have expressed more concerns about their potential for burnout than their outpatient‐based colleagues.[8]

In fact, data suggesting a correlation between inpatient practice and burnout predate the advent of the US hospitalist movement. Increased hospital time was reported to correlate with higher rates of burnout in internists,[9] family practitioners,[10] palliative physicians,[11] junior doctors,[12] radiologists,[13] and cystic fibrosis caregivers.[14] In 1987, Keinan and Melamed[15] noted, Hospital work by its very nature, as compared to the work of a general practitioner, deals with the more severe and complicated illnesses, coupled with continuous daily contacts with patients and their anxious families. In addition, these physicians may find themselves embroiled in the power struggles and competition so common in their work environment.

There are other features, however, that may protect inpatient physicians from burnout. Hospital practice can facilitate favorable social relations involving colleagues, co‐workers, and patients,[16] a factor that may be protective.[17] A hospitalist schedule also can allow more focused time for continuing medical education, research, and teaching,[18] which have all been associated with reduced risk of burnout in some studies.[17] Studies of psychiatrists[19] and pediatricians[20] have shown a lower rate of burnout among physicians with more inpatient duties. Finally, a practice model involving a seemingly stable cadre of inpatient physicians has existed in Europe for decades,[2] indicating at least a degree of sustainability.

Information suggesting a higher rate of burnout among inpatient physicians could be used to target therapeutic interventions and to adjust schedules, whereas the opposite outcome could refute a pervasive myth. We therefore endeavored to summarize the literature on burnout among inpatient versus outpatient physicians in a systematic fashion, and to include data not only from the US hospitalist experience but also from other countries that have used a similar model for decades. Our primary hypothesis was that inpatient physicians experience more burnout than outpatient physicians.

It is important to distinguish burnout from depression, job dissatisfaction, and occupational stress, all of which have been studied extensively in physicians. Burnout, as introduced by Freudenberger[21] and further characterized by Maslach,[22] is a condition in which emotional exhaustion, depersonalization, and a low sense of personal accomplishment combine to negatively affect work life (as opposed to clinical depression, which affects all aspects of life). Job satisfaction can correlate inversely with burnout, but it is a separate process[23] and the subject of a recent systematic review.[24] The importance of distinguishing burnout from job dissatisfaction is illustrated by a survey of head and neck surgeons, in which 97% of those surveyed indicated satisfaction with their jobs and 34% of the same group answered in the affirmative when asked if they felt burned out.[25]

One obstacle to the meaningful comparison of burnout prevalence across time, geography, and specialty is the myriad ways in which burnout is measured and reported. The oldest and most commonly used instrument to measure burnout is the Maslach Burnout Inventory (MBI), which contains 22 items assessing 3 components of burnout (emotional exhaustion, depersonalization, and low personal accomplishment).[26] Other measures include the Copenhagen Burnout Inventory[27] (19 items with the components personal burnout, work‐related burnout, and client‐related burnout), Utrecht Burnout Inventory[28] (20‐item modification of the MBI), Boudreau Burnout Questionnaire[29] (30 items), Arbeitsbezogenes Verhaltens und Erlebensmuster[30] (66‐item questionnaire assessing professional commitment, resistance to stress, and emotional well‐being), Shirom‐Melamed Burnout Measure[31] (22 items with subscales for physical fatigue, cognitive weariness, tension, and listlessness), and a validated single‐item questionnaire.[32]

METHODS

Electronic searches of MEDLINE, EMBASE, PsycINFO, SCOPUS, and PubMed were undertaken for articles published from January 1, 1974 (the year in which burnout was first described by Freudenberger[21]) to 2012 (last accessed, September 12, 2012) using the Medical Subject Headings (MeSH) terms stress, psychological; burnout, professional; adaptation, psychological; and the keyword burnout. The same sources were searched to create another set for the MeSH terms hospitalists, physician's practice patterns, physicians/px, professional practice location, and the keyword hospitalist#. Where exact subject headings did not exist in databases, comparable subject headings or keywords were used. The 2 sets were then combined using the operator and. Abstracts from the Society of Hospital Medicine annual conferences were hand‐searched, as were reference lists from identified articles. To ensure that pertinent international literature was captured, there was no language restriction in the search.

A 2‐stage screening process was used. The titles and abstracts of all articles identified in the search were independently reviewed by 2 investigators (D.L.R. and K.J.C.) who had no knowledge of each other's results. An article was obtained when either reviewer deemed it worthy of full‐text review.

All full‐text articles were independently reviewed by the same 2 investigators. The inclusion criterion was the measurement of burnout in physicians who are stated to or can be reasonably assumed to spend the substantial majority of their clinical practice exclusively in either the inpatient or the outpatient setting. Studies of emergency department physicians or specialists who invariably spend substantial amounts of time in both settings (eg, surgeons, anesthesiologists) were excluded. Studies limited to trainees or nonphysicians were also excluded. For both stages of review, agreement between the 2 investigators was assessed by calculating the statistic. Disagreements about inclusion were adjudicated by a third investigator (A.I.B.).

Because our goal was to establish and compare the rate of burnout among US hospitalists and other inpatient physicians around the world, we included studies of hospitalists according to the definition in use at the time of the individual study, noting that the formal definition of a hospitalist has changed over the years.[33] Because practice patterns for physicians described as primary care physicians, family doctors, hospital doctors, and others differ substantially from country to country, we otherwise included only the studies where the practice location was stated explicitly or where the authors confirmed that their study participants either are known or can be reasonably assumed to spend more than 75% of their time caring for hospital inpatients, or are known or can be reasonably assumed to spend the vast majority of their time caring for outpatients.

Data were abstracted using a standardized form and included the measure of burnout used in the study, results, practice location of study subjects, and total number of study subjects. When data were not clear (eg, burnout measured but not reported by the authors, practice location of study subjects not clear), authors were contacted by email, or when no current email address could be located or no response was received, by telephone or letter. In instances where burnout was measured repeatedly over time or before and after a specific intervention, only the baseline measurement was recorded. Because all studies were expected to be nonrandomized, methodological quality was assessed using a version of the tool of Downs and Black,[34] adapted where necessary by omitting questions not applicable to the specific study type (eg randomization for survey studies)[35] and giving a maximum of 1 point for the inclusion of a power calculation.

Two a priori analyses were planned: (1) a statistical comparison of articles directly comparing burnout among inpatient and outpatient physicians, and (2) a statistical comparison of articles measuring burnout among inpatient physicians with articles measuring burnout among outpatient physicians by the most frequently reported measuremean subset scores for emotional exhaustion, depersonalization, and personal accomplishment on the MBI.

The primary outcome measures were the differences between mean subset scores for emotional exhaustion, depersonalization, and personal accomplishment on the MBI. All differences are expressed as (outpatient meaninpatient mean). The variance of each outcome was calculated with standard formulas.[36] To calculate the overall estimate, each study was weighted by the reciprocal of its variance. Studies with fewer than 10 subjects were excluded from statistical analysis but retained in the systematic review.

For studies that reported data for both inpatient and outpatient physicians (double‐armed studies), Cochran Q test and the I2 value were used to assess heterogeneity.[37, 38] Substantial heterogeneity was expected because these individual studies were conducted for different populations in different settings with different study designs, and this expectation was confirmed statistically. Therefore, we used a random effects model to estimate the overall effect, providing a conservative approach that accounted for heterogeneity among studies.[39]

To assess the durability of our findings, we performed separate multivariate meta‐regression analyses by including single‐armed studies only and including both single‐armed and double‐armed studies. For these meta‐regressions, means were again weighted by the reciprocal of their variances, and the arms of 2‐armed studies were considered separately. This approach allowed us to generate an estimate of the differences between MBI subset scores from studies that did not include such an estimate when analyzed separately.[40]

We examined the potential for publication bias in double‐armed studies by constructing a funnel plot, in which mean scores were plotted against their standard errors.[41] The trim‐and‐fill method was used to determine whether adjustment for publication bias was necessary. In addition, Begg's rank correlation test[42] was completed to test for statistically significant publication bias.

Stata 10.0 statistical software (StataCorp, College Station, TX) was used for data analyses. A P value of 0.05 or less was deemed statistically significant. The Preferred Reporting Items for Systematic Reviews and Meta‐analysis checklist was used for the design and execution of the systematic review and meta‐analysis.[43]

Subgroup analyses based on location were undertaken a posteriori. All data (double‐armed meta‐analysis, meta‐regression of single‐armed studies, and meta‐regression of single‐ and double‐armed studies) were analyzed by location (United States vs other; United States vs Europe vs other).

RESULTS

The search results are outlined in Figure 1. In total, 1704 articles met the criteria for full‐text review. A review of pertinent reference lists and author contacts led to the addition of 149 articles. Twenty‐nine references could not be located by any means, despite repeated attempts. Therefore, 1824 articles were subjected to full‐text review by the 2 investigators.

Figure 1
Flow chart of study selection.

Initially, 57 articles were found that met criteria for inclusion. Of these, 2 articles reported data in formats that could not be interpreted.[44, 45] When efforts to clarify the data with the authors were unsuccessful, these studies were excluded. A study specifically designed to assess the response of physicians to a recent series of terrorist attacks[46] was excluded a posteriori because of lack of generalizability. Of the other 54 studies, 15 reported burnout data on both outpatient physicians and inpatient physicians, 22 reported data on outpatient physicians only, and 17 reported data on inpatient physicians only. Table 1 summarizes the results of the 37 studies involving outpatient physicians; Table 2 summarizes the 32 studies involving inpatient physicians.

Summaries of Studies of Outpatient‐Based Physicians Meeting Inclusion Criteria
Lead Author, Publication Year Study Population and Location Instrument No. of Participants EE Score (SD)a DP Score (SD) PA Score (SD) Other Results
  • NOTE: Abbreviations: AVEM, Arbeitsbezogenes Verhaltens und Erlebensmuster; DP, depersonalization subset of MBI; EE, emotional exhaustion subset of MBI; MBI, Maslach Burnout Inventory; PA, personal accomplishment subset of MBI; SMBM, Shirom‐Melamad Burnout Measure; SD, standard deviation.

  • High scores of EE and DP and low scores of PA are features of burnout.

  • Data obtained directly from authors 20102012.

  • SDs calculated from published standard errors. Personal accomplishment scale reversed to match other studies.

  • SDs calculated from published CIs.

Schweitzer, 1994[12] Young physicians of various specialties in South Africa Single‐item survey 7 6 (83%) endorsed burnout
Aasland, 1997 [54]b General practitioners in Norway Modified MBI (22 items; scale, 15) 298 2.65 (0.80) 1.90 (0.59) 3.45 (0.40)
Grassi, 2000 [58] General practitioners in Italy MBI 182 18.49 (11.49) 6.11 (5.86) 38.52 (7.60)
McManus, 2000 [59]b General practitioners in United Kingdom Modified MBI (9 items; scale, 06) 800 8.34 (4.39) 3.18 (3.40) 14.16 (2.95)
Yaman, 2002 [60] General practitioners in 8 European nations MBI 98 25.1 (8.50) 7.3 (4.92) 34.5 (7.67)
Cathbras, 2004 [61] General practitioners in France MBI 306 21.85 (12.4) 9.13 (6.7) 38.7 (7.1)
Goehring, 2005 [63] General practitioners, general internists, pediatricians in Switzerland MBI 1755 17.9 (9.8) 6.5 (4.7) 39.6 (6.5)
Esteva, 2006 [64] General practitioners, pediatricians in Spain MBI 261 27.4 (11.8) 10.07 (6.4) 35.9 (7.06)
Gandini, 2006 [65]b Physicians of various specialties in Argentina MBI 67 31.0 (13.8) 10.2 (6.6) 38.4 (6.8)
Ozyurt, 2006 [66] General practitioners in Turkey Modified MBI (22 items; scale, 04) 55 15.23 (5.80) 4.47 (3.31) 23.38 (4.29)
Deighton, 2007 [67]b Psychiatrists in several German‐speaking nations MBI 19 30.68 (9.92) 13.42 (4.23) 37.16 (3.39)
Dunwoodie, 2007 [68]b Palliative care physicians in Australia MBI 21 14.95 (9.14) 3.95 (3.40) 38.90 (2.88)
Srgaard, 2007 [69]b Psychiatrists in 5 European nations MBI 22 19.41 (8.08) 6.68 (4.93) 39.00 (4.40)
Sosa Oberlin, 2007 [56]b Physicians of various specialties in Argentina Author‐designed instrument 33 26 (78.8%) had 4 burnout symptoms, 6.15 symptoms per physician
Voltmer, 2007 [57]b Physicians of various specialties in Germany AVEM 46 11 (23.9%) exhibited burnout (type B) pattern
dm, 2008 [70]b Physicians of various specialties in Hungary MBI 163 17.45 (11.12) 4.86 (4.91) 36.56 (7.03)
Di Iorio, 2008 [71]b Dialysis physicians in Italy Author‐designed instrument 54 Work: 2.6 (1.5), Material: 3.1 (2.1), Climate: 3.0 (1.1), Objectives: 3.4 (1.6), Quality: 2.2 (1.5), Justification: 3.2 (2.0)
Lee, 2008 [49]b Family physicians in Canada MBI 123 26.26 (9.53) 10.20 (5.22) 38.43 (7.34)
Truchot, 2008 [72] General practitioners in France MBI 259 25.4 (11.7) 7.5 (5.5) 36.5 (7.1)
Twellaar, 2008 [73]b General practitioners in the Netherlands Utrecht Burnout Inventory 349 2.06 (1.11) 1.71 (1.05) 5.08 (0.77)
Arigoni, 2009 [17] General practitioners, pediatricians in Switzerland MBI 258 22.8 (12.0) 6.9 (6.1) 39.0 (7.2)
Bernhardt, 2009 [75] Clinical geneticists in United States MBI 72 25.8 (10.01)c 10.9 (4.16)c 34.8 (5.43)c
Bressi, 2009 [76]b Psychiatrists in Italy MBI 53 23.15 (11.99) 7.02 (6.29) 36.41 (7.54)
Krasner, 2009 [77] General practitioners in United States MBI 60 26.8 (10.9)d 8.4 (5.1)d 40.2 (5.3)d
Lasalvia, 2009 [55]b Psychiatrists in Italy Modified MBI (16 items; scale, 06) 38 2.37 (1.27) 1.51 (1.15) 4.46 (0.87)
Peisah, 2009 [79]b Physicians of various specialties in Australia MBI 28 13.92 (9.24) 3.66 (3.95) 39.34 (8.55)
Shanafelt, 2009 [80]b Physicians of various specialties in United States MBI 408 20.5 (11.10) 4.3 (4.74) 40.8 (6.26)
Zantinge, 2009 [81] General practitioners in the Netherlands Utrecht Burnout Inventory 126 1.58 (0.79) 1.32 (0.72) 4.27 (0.77)
Voltmer, 2010 [83]b Psychiatrists in Germany AVEM 526 114 (21.7%) exhibited burnout (type B) pattern
Maccacaro, 2011 [85]b Physicians of various specialties in Italy MBI 42 14.31 (11.98) 3.62 (4.95) 38.24 (6.22)
Lucas, 2011 [84]b Outpatient physicians periodically staffing an academic hospital teaching service in United States MBI (EE only) 30 24.37 (14.95)
Shanafelt, 2012 [87]b General internists in United States MBI 447 25.4 (14.0) 7.5 (6.3) 41.4 (6.0)
Kushnir, 2004 [62] General practitioners and pediatricians in Israel MBI (DP only) and SMBM 309 9.15 (3.95) SMBM mean (SD), 2.73 per item (0.86)
Vela‐Bueno, 2008 [74]b General practitioners in Spain MBI 240 26.91 (11.61) 9.20 (6.35) 35.92 (7.92)
Lesic, 2009 [78]b General practitioners in Serbia MBI 38 24.71 (10.81) 7.47 (5.51) 37.21 (7.44)
Demirci, 2010 [82]b Medical specialists related to oncology practice in Hungary MBI 26 23.31 (11.2) 6.46 (5.7) 37.7 (8.14)
Putnik, 2011 [86]b General practitioners in Hungary MBI 370 22.22 (11.75) 3.66 (4.40) 41.40 (6.85)
Summary of Studies of Inpatient Physicians Meeting Inclusion Criteria
Lead Author, Publication Year Study Population and Location Instrument No. of Participants EE Score (SD)a DP Score (SD) PA Score (SD) Other Results
  • NOTE: Abbreviations: AVEM, Arbeitsbezogenes Verhaltens und Erlebensmuster; DP, depersonalization subset of MBI; EE, emotional exhaustion subset of MBI; MBI, Maslach Burnout Inventory; PA, personal accomplishment subset of MBI; SD, standard deviation.

  • High scores of EE and DP and low scores of PA are features of burnout.

  • SDs not available; study therefore excluded from statistical comparisons.

  • Different survey item than other studies in this table using a single‐item, 5‐point burnout measure.

  • Data obtained directly from authors 20102012.

  • Personal accomplishment scale reversed to match other studies.

Varga, 1996 [88] Hospital doctors in Spain MBI 179 21.61b 7.33b 35.28b
Aasland, 1997 [54] Hospital doctors in Norway Modified MBI (22 items; scale, 15) 582 2.39 (0.80) 1.81 (0.65) 3.51 (0.46)
Bargellini, 2000 [89] Hospital doctors in Italy MBI 51 17.45 (9.87) 7.06 (5.54) 35.33 (7.90)
Grassi, 2000 [58] Hospital doctors in Italy MBI 146 16.17 (9.64) 5.32 (4.76) 38.71 (7.28)
Hoff, 2001 [33] Hospitalists in United States Single‐item surveyc 393 12.9% burned out (>4/5), 24.9% at risk for burnout (34/5), 62.2% at no current risk (mean, 2.86 on 15 scale)
Trichard, 2005 [90] Hospital doctors in France MBI 199 16 (10.7) 6.6 (5.7) 38.5 (6.5)
Gandini, 2006 [65]d Hospital doctors in Argentina MBI 290 25.0 (12.7) 7.9 (6.2) 40.1 (7.0)
Dunwoodie, 2007 [68]d Palliative care doctors in Australia MBI 14 18.29 (14.24) 5.29 (5.89) 38.86 (3.42)
Srgaard, 2007 [69]d Psychiatrists in 5 European nations MBI 18 18.56 (9.32) 5.50 (3.79) 39.08 (5.39)
Sosa Oberlin, 2007 [56]d Hospital doctors in Argentina Author‐designed instrument 3 3 (100%) had 4 burnout symptoms, 8.67 symptoms per physician
Voltmer, 2007 [57]d Hospital doctors in Germany AVEM 271 77 (28.4%) exhibited burnout (type B) pattern
dm, 2008 [70]b Physicians of various specialties in Hungary MBI 194 19.23 (10.79) 4.88 (4.61) 35.26 (8.42)
Di Iorio, 2008 [71]d Dialysis physicians in Italy Author‐designed instrument 62 Work, mean (SD), 3.1 (1.4); Material, mean (SD), 3.3 (1.5); Climate, mean (SD), 2.9 (1.1); Objectives, mean (SD), 2.5 (1.5); Quality, mean (SD), 3.0 (1.1); Justification, mean (SD), 3.1 (2.1)
Fuss, 2008 [91]d Hospital doctors in Germany Copenhagen Burnout Inventory 292 Mean Copenhagen Burnout Inventory, mean (SD), 46.90 (18.45)
Marner, 2008 [92]d Psychiatrists and 1 generalist in United States MBI 9 20.67 (9.75) 7.78 (5.14) 35.33 (6.44)
Shehabi, 2008 [93]d Intensivists in Australia Modified MBI (6 items; scale, 15) 86 2.85 (0.93) 2.64 (0.85) 2.58 (0.83)
Bressi, 2009 [76]d Psychiatrists in Italy MBI 28 17.89 (14.46) 5.32 (7.01) 34.57 (11.27)
Brown, 2009 [94] Hospital doctors in Australia MBI 12 22.25 (8.59) 6.33 (2.71) 39.83 (7.31)
Lasalvia, 2009 [55]d Psychiatrists in Italy Modified MBI (16 items; scale, 06) 21 1.95 (1.04) 1.35 (0.85) 4.46 (1.04)
Peisah, 2009 [79]d Hospital doctors in Australia MBI 62 20.09 (9.91) 6.34 (4.90) 35.06 (7.33)
Shanafelt, 2009 [80]d Hospitalists and intensivists in United States MBI 19 25.2 (11.59) 4.4 (3.79) 38.5 (8.04)
Tunc, 2009 [95] Hospital doctors in Turkey Modified MBI (22 items; scale, 04) 62 1.18 (0.78) 0.81 (0.73) 3.10 (0.59)e
Cocco, 2010 [96]d Hospital geriatricians in Italy MBI 38 16.21 (11.56) 4.53 (4.63) 39.13 (7.09)
Doppia, 2011 [97]d Hospital doctors in France Copenhagen Burnout Inventory 1,684 Mean work‐related burnout score, 2.72 (0.75)
Glasheen, 2011 [98] Hospitalists in United States Single‐item survey 265 Mean, 2.08 on 15 scale 62 (23.4%) burned out
Lucas, 2011 [84]d Academic hospitalists in United States MBI (EE only) 26 19.54 (12.85)
Thorsen, 2011 [99] Hospital doctors in Malawi MBI 2 25.5 (4.95) 8.5 (6.36) 25.0 (5.66)
Hinami, 2012 [50]d Hospital doctors in United States Single‐item survey 793 Mean, 2.24 on 15 scale 261 (27.2%) burned out
Quenot, 2012 [100]d Intensivists in France MBI 4 33.25 (4.57) 13.50 (5.45) 35.25 (4.86)
Ruitenburg, 2012 [101] Hospital doctors in the Netherlands MBI (EE and DP only) 214 13.3 (8.0) 4.5 (4.1)
Seibt, 2012 [102]d Hospital doctors in Germany Modified MBI (16 items; scale, 06, reported per item rather than totals) 2,154 2.2 (1.4) 1.4 (1.2) 5.1 (0.9)
Shanafelt, 2012 [87]d Hospitalists in United States MBI 130 24.7 (12.5) 9.1 (6.9) 39.0 (7.6)

Table 3 summarizes the results of the 15 studies that reported burnout data for both inpatient and outpatient physicians, allowing direct comparisons to be made. Nine studies reported MBI subset totals with standard deviations, 2 used different modifications of the MBI, 2 used different author‐derived measures, 1 used only the emotional exhaustion subscale of the MBI, and 1 used the Arbeitsbezogenes Verhaltens und Erlebensmuster. Therefore, statistical comparison was attempted only for the 9 studies reporting comparable MBI data, comprising burnout data on 1390 outpatient physicians and 899 inpatient physicians.

Summary of Studies Including Both Inpatient‐Based and Outpatient‐Based Physicians Meeting Inclusion Criteria
Lead Author, Publication Year Location Instrument Inpatient‐Based Physicians Outpatient‐Based Physicians
No. Results, Score (SD)a No. Results, Score (SD)a
  • NOTE: Abbreviations: AVEM, Arbeitsbezogenes Verhaltens und Erlebensmuster; DP, depersonalization subset of MBI; EE, emotional exhaustion subset of MBI; MBI, Maslach Burnout Inventory; PA, personal accomplishment subset of MBI; SD, standard deviation.

  • High scores of EE and DP and low scores of PA are features of burnout.

  • Data obtained directly from authors from 20102012.

Aasland, 1997 [54]b Norway Modified MBI (22 items; scale, 15) 582 EE, 2.39 (0.80); DP, 1.81 (0.65); PA, 3.51 (0.46) 298 EE, 2.65 (0.80); DP, 1.90 (0.59); PA, 3.45 (0.40)
Grassi, 2000 [58] Italy MBI 146 EE, 16.17 (9.64); DP, 5.32 (4.76); PA, 38.71 (7.28) 182 EE, 18.49 (11.49); DP, 6.11 (5.86); PA, 38.52 (7.60)
Gandini, 2006 [65]b Argentina MBI 290 EE, 25.0 (12.7);DP, 7.9 (6.2); PA, 40.1 (7.0) 67 EE, 31.0 (13.8); DP, 10.2 (6.6); PA, 38.4 (6.8)
Dunwoodie, 2007 [68]b Australia MBI 14 EE, 18.29 (14.24); DP, 5.29 (5.89); PA, 38.86 (3.42) 21 EE, 14.95 (9.14); DP, 3.95 (3.40); PA, 38.90 (2.88)
Srgaard, 2007 [69]b 5 European nations MBI 18 EE, 18.56 (9.32); DP, 5.50 (3.79); PA, 39.08 (5.39) 22 EE, 19.41 (8.08); DP, 6.68 (4.93); PA, 39.00 (4.40)
Sosa Oberlin, 2007 [56]b Argentina Author‐designed instrument 3 3 (100%) had 4 burnout symptoms, 8.67 symptoms per physician 33 26 (78.8%) had 4 burnout symptoms, 6.15 symptoms per physician
Voltmer, 2007 [57]b Germany AVEM 271 77 (28.4%) exhibited burnout (type B) pattern 46 11 (23.9%) exhibited burnout (type B) pattern
dm, 2008 [70]b Hungary MBI 194 EE, 19.23 (10.79); DP, 4.88 (4.61); PA, 35.26 (8.42) 163 EE, 17.45 (11.12); DP, 4.86 (4.91); PA, 36.56 (7.03)
Di Iorio, 2008 [71]b Italy Author‐designed instrument 62 Work: 3.1 (1.4); material: 3.3 (1.5); climate: 2.9 (1.1); objectives: 2.5 (1.5); quality: 3.0 (1.1); justification: 3.1 (2.1) 54 Work: 2.6 (1.5); material: 3.1 (2.1); climate: 3.0 (1.1); objectives: 3.4 (1.6); quality: 2.2 (1.5); justification: 3.2 (2.0)
Bressi, 2009 [76]b Italy MBI 28 EE, 17.89 (14.46); DP, 5.32 (7.01); PA, 34.57 (11.27) 53 EE, 23.15 (11.99); DP, 7.02 (6.29); PA, 36.41 (7.54)
Lasalvia, 2009[55]b Italy Modified MBI (16 items; scale, 06) 21 EE, 1.95 (1.04); DP, 1.35 (0.85); PA, 4.46 (1.04) 38 EE, 2.37 (1.27); DP, 1.51 (1.15); PA, 4.46 (0.87)
Peisah, 2009 [79]b Australia MBI 62 EE, 20.09 (9.91); DP, 6.34 (4.90); PA, 35.06 (7.33) 28 EE, 13.92 (9.24); DP, 3.66 (3.95); PA, 39.34 (8.55)
Shanafelt, 2009 [80]b United States MBI 19 EE, 25.2 (11.59); DP, 4.4 (3.79); PA, 38.5 (8.04) 408 EE, 20.5 (11.10); DP, 4.3 (4.74); PA, 40.8 (6.26)
Lucas, 2011 [84]b United States MBI (EE only) 26 EE, 19.54 (12.85) 30 EE, 24.37 (14.95)
Shanafelt, 2012 [87]b United States MBI 130 EE, 24.7 (12.5); DP, 9.1 (6.9); PA, 39.0 (7.6) 447 EE, 25.4 (14.0); DP, 7.5 (6.3); PA, 41.4 (6.0)

Figure 2 shows that no significant difference existed between the groups regarding emotional exhaustion (mean difference, 0.11 points on a 54‐point scale; 95% confidence interval [CI], 2.40 to 2.61; P=0.94). In addition, there was no significant difference between the groups regarding depersonalization (Figure 3; mean difference, 0.00 points on a 30‐point scale; 95% CI, 1.03 to 1.02; P=0.99) and personal accomplishment (Figure 4; mean difference, 0.93 points on a 48‐point scale; 95% CI, 0.23 to 2.09; P=0.11).

Figure 2
Forest plot for double‐armed studies reporting Maslach Burnout Inventory scores for emotional exhaustion. The size of the square represents study size, and the bars represent the 95% confidence interval (CI).
Figure 3
Forest plot for double‐armed studies reporting Maslach Burnout Inventory scores for depersonalization. The size of the square represents study size and the bars represent the 95% confidence interval (CI).
Figure 4
Forest plot for double‐armed studies reporting Maslach Burnout Inventory scores for personal accomplishment. The size of the square represents study size and the bars represent the 95% confidence interval (CI). The direction of the y‐axis has been reversed so that greater burnout in outpatient physicians remains to the right.

We used meta‐regression to allow the incorporation of single‐armed MBI studies. Whether single‐armed studies were analyzed separately (15 outpatient studies comprising 3927 physicians, 4 inpatient studies comprising 300 physicians) or analyzed with double‐armed studies (24 outpatient arms comprising 5318 physicians, 13 inpatient arms comprising 1301 physicians), the lack of a significant difference between the groups persisted for the depersonalization and personal accomplishment scales (Figure 5). Emotional exhaustion was significantly higher in outpatient physicians when single‐armed studies were considered separately (mean difference, 6.36 points; 95% CI, 2.24 to 10.48; P=0.002), and this difference persisted when all studies were combined (mean difference, 3.00 points; 95% CI, 0.05 to 5.94, P=0.046).

Figure 5
Forest plots comparing results of meta‐analysis of 9 double‐armed studies, meta‐regression of 19 single‐armed studies, and meta‐regression of all 28 studies reporting Maslach Burnout Inventory scores. The direction of the y‐axis of the personal accomplishment plot has been reversed so that higher burnout in outpatient physicians remains to the right. Error bars represent the 95% confidence interval.

Subgroup analysis by geographic location showed US outpatient physicians had a significantly higher personal accomplishment score than US inpatient physicians (mean difference, 2.38 points; 95% CI, 1.22 to 3.55; P<0.001) in double‐armed studies. This difference did not persist when single‐armed studies were included through meta‐regression (mean difference, 0.55 points, 95% CI, 4.30 to 5.40, P=0.83).

Table 4 demonstrates that methodological quality was generally good from the standpoint of the reporting and bias subsections of the Downs and Black tool. External validity was scored lower for many studies due to the use of convenience samples and lack of information about physicians who declined to participate.

Assessment of Methodologic Quality
Lead Author, Publication Year Reporting External Validity Internal Validity: Bias Internal Validity: Confounding Power
  • NOTE: For survey studies (all studies except Krasner,[77] Lucas,[84] and Quenot[100]), questions regarding interventions were omitted. For uncontrolled studies (all studies except Lucas[84]), questions regarding controls were omitted. The presence of a power calculation was awarded 1 point.

Schweitzer, 1994 [12] 5 of 6 points 1 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Varga, 1996 [88] 5 of 6 points 1 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Aasland, 1997 [54] 3 of 6 points 2 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Bargellini, 2000 [89] 5 of 6 points 0 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Grassi, 2000 [58] 6 of 6 points 0 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
McManus, 2000 [59] 5 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Hoff, 2001 [33] 6 of 6 points 2 of 2 points 2 of 4 points 1 of 1 point 0 of 1 point
Yaman, 2002 [60] 5 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Cathbras, 2004 [61] 6 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Kushnir, 2004 [62] 5 of 6 points 0 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Goehring, 2005 [63] 6 of 6 points 1 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Trichard, 2005 [90] 3 of 6 points 1 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Esteva, 2006 [64] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Gandini, 2006 [65] 6 of 6 points 1 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Ozyurt, 2006 [66] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Deighton, 2007 [67] 5 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Dunwoodie, 2007 [68] 5 of 6 points 2 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Srgaard, 2007 [69] 6 of 6 points 0 of 2 points 3 of 4 points 1 of 1 point 1 of 1 point
Sosa Oberlin, 2007 [56] 4 of 6 points 0 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Voltmer, 2007 [57] 4 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
dm, 2008 [70] 5 of 6 points 2 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Di Iorio, 2008 [71] 6 of 6 points 0 of 2 points 2 of 4 points 0 of 1 point 0 of 1 point
Fuss, 2008 [91] 6 of 6 points 0 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Lee, 2008 [49] 4 of 6 points 2 of 2 points 4 of 4 points 0 of 1 point 1 of 1 point
Marner, 2008 [92] 6 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Shehabi, 2008 [93] 3 of 6 points 1 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Truchot, 2008 [72] 5 of 6 points 1 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Twellaar, 2008 [73] 6 of 6 points 2 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Vela‐Bueno, 2008 [74] 5 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Arigoni, 2009 [17] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Bernhardt, 2009 [75] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Bressi, 2009 [76] 6 of 6 points 0 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Brown, 2009 [94] 6 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Krasner, 2009 [77] 9 of 11 points 0 of 3 points 6 of 7 points 1 of 2 points 1 of 1 point
Lasalvia, 2009 [55] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Lesic, 2009 [78] 5 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Peisah, 2009 [79] 6 of 6 points 2 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Shanafelt, 2009 [80] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Tunc, 2009 [95] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Zantinge, 2009 [81] 5 of 6 points 0 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Cocco, 2010 [96] 4 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Demirci, 2010 [82] 6 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Voltmer, 2010 [83] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Doppia, 2011 [97] 5 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Glasheen, 2011 [98] 5 of 6 points 0 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Lucas, 2011 [84] 10 of 11 points 2 of 3 points 7 of 7 points 5 of 6 points 1 of 1 point
Maccacaro, 2011 [85] 5 of 6 points 0 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Putnik, 2011 [86] 6 of 6 points 1 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Thorsen, 2011 [99] 6 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Hinami, 2012 [50] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 1 of 1 point
Quenot, 2012 [100] 8 of 11 points 1 of 3 points 6 of 7 points 1 of 2 points 0 of 1 point
Ruitenburg, 2012 [101] 6 of 6 points 2 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Seibt, 2012 [102] 6 of 6 points 0 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Shanafelt, 2012 [87] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point

Funnel plots were used to evaluate for publication bias in the meta‐analysis of the 8 double‐armed studies (Figure 6). We found no significant evidence of bias, which was supported by Begg's test P values of 0.90 for emotional exhaustion, >0.99 for depersonalization, and 0.54 for personal accomplishment. A trim‐and‐fill analysis determined that no adjustment was necessary.

Figure 6
Funnel plots for the 8 double‐armed studies that reported Maslach Burnout Inventory scores for emotional exhaustion, depersonalization, and personal accomplishment. Abbreviations: CI, confidence interval.

DISCUSSION

There appears to be no support for the long‐held belief that inpatient physicians are particularly prone to burnout. Among studies for which practice location was stated explicitly or could be obtained from the authors, and who used the MBI, no differences were found among inpatient and outpatient physicians with regard to depersonalization or personal accomplishment. This finding persisted whether double‐armed studies were compared directly, single‐armed studies were incorporated into this analysis, or single‐armed studies were analyzed separately. Outpatient physicians had a higher degree of emotional exhaustion when all studies were considered.

There are several reasons why outpatient physicians may be more prone to emotional exhaustion than their inpatient colleagues. Although it is by no means true that all inpatient physicians work in shifts, the increased availability of shift work may allow some inpatient physicians to better balance their professional and personal lives, a factor of work with which some outpatient physicians have struggled.[47] Inpatient practice may also afford more opportunity for teamwork, a factor that has been shown to correlate with reduced burnout.[48] When surveyed about burnout, outpatient physicians have cited patient volumes, paperwork, medicolegal concerns, and lack of community support as factors.[49] Inpatient physicians are not immune to these forces, but they arguably experience them to different degrees.

The absence of a higher rate of depersonalization among inpatient physicians is particularly reassuring in light of concerns expressed with the advent of US hospital medicinethat some hospitalists would be prone to viewing patients as an impediment to the efficient running of the hospital,[2] the very definition of depersonalization.

Although the difference in the whole sample was not statistically significant, the consistent tendency toward a greater sense of personal accomplishment among outpatient physicians is also noteworthy, particularly because post hoc subgroup analysis of US physicians did show statistical significance in both 2‐armed studies. Without detailed age data for the physicians in each study, we could not separate the possible impact of age on personal accomplishment; hospital medicine is a newer specialty staffed by generally younger physicians, and hospitalists may not have had time to develop a sense of accomplishment. When surveyed about job satisfaction, hospitalists have also reported the feeling that they were treated as glorified residents,[50] a factor that, if shared by other inpatient physicians, must surely affect their sense of personal accomplishment. The lack of longitudinal care for patients and the substantial provision of end‐of‐life care also may diminish the sense of personal accomplishment among inpatient physicians.

Another important finding from this systematic review is the marked heterogeneity of the instruments used to measure physician burnout. Many of the identified studies could not be subjected to meta‐analysis because of their use of differing burnout measures. Drawing more substantial conclusions about burnout and practice location is limited by the fact that, although the majority of studies used the full MBI, the largest study of European hospital doctors used the Copenhagen Burnout Inventory, and the studies thus far of US hospitalists have used single‐item surveys or portions of the MBI. Not reflected in this review is the fact that a large study of US burnout and job satisfaction[51] did not formally address practice location (M. Linzer, personal communication, August 2012). Similarly, a large study of British hospital doctors[52] is not included herein because many of the physicians involved had substantial outpatient duties (C. Taylor, personal communication, July 2012). Varying burnout measures have complicated a previous systematic review of burnout in oncologists.[53] Two studies that directly compared inpatient and outpatient physicians but that were excluded from our statistical analysis because of their modified versions of the MBI,[54, 55] showed higher burnout scores in outpatient physicians. Two other studies that provided direct inpatient versus outpatient comparisons but that used alternative burnout measures[56, 57] showed a greater frequency of burnout in inpatient physicians, but of these, 1 study[56] involved only 3 inpatient physicians.

Several limitations of our study should be considered. Although we endeavored to obtain information from authors (with some success) about specific local practice patterns and eliminated many studies because of incomplete data or mixed practice patterns (eg, general practitioners who take frequent hospital calls, hospital physicians with extensive outpatient duties in a clinic attached to their hospital), it remains likely that many physicians identified as outpatient provided some inpatient care (attending a few weeks per year on a teaching service, for example) and that some physicians identified as inpatient have minimal outpatient duties.

More importantly, the dataset analyzed is heterogeneous. Studies of the incidence of burnout are naturally observational and therefore not randomized. Inclusion of international studies is necessary to answer the research question (because published data on US hospitalists are sparse) but naturally introduces differences in practice settings, local factors, and other factors for which we cannot possibly account fully.

Our meta‐analysis therefore addressed a broad question about burnout among inpatient and outpatient physicians in various diverse settings. Applying it to any 1 population (including US hospitalists) is, by necessity, imprecise.

Post hoc analysis should be viewed with caution. For example, the finding of a statistical difference between US inpatient and outpatient physicians with regard to personal accomplishment score is compelling from the standpoint of hypothesis generation. However, it is worth bearing in mind that this analysis contained only 2 studies, both by the same primary author, and compared 855 outpatient physicians to only 149 hospitalists. This difference was no longer significant when 2 outpatient studies were added through meta‐regression.

Finally, the specific focus of this study on practice location precluded comparison with emergency physicians and anesthesiologists, 2 specialist types that have been the subject of particularly robust burnout literature. As the literature on hospitalist burnout becomes more extensive, comparative studies with these groups and with intensivists might prove instructive.

In summary, analysis of 24 studies comprising data on 5318 outpatient physicians and 1301 inpatient physicians provides no support for the commonly held belief that hospital‐based physicians are particularly prone to burnout. Outpatient physicians reported higher emotional exhaustion. Further studies of the incidence and severity of burnout according to practice location are indicated. We propose that in future studies, to avoid the difficulties with statistical analysis summarized herein, investigators ask about and explicitly report practice location (inpatient vs outpatient vs both) and report mean MBI subset data and standard deviations. Such information about US hospitalists would allow comparison with a robust (if heterogeneous) international literature on burnout.

Acknowledgments

The authors gratefully acknowledge all of the study authors who contributed clarification and guidance for this project, particularly the following authors who provided unpublished data for further analysis: Olaf Aasland, MD; Szilvia dm, PhD; Annalisa Bargellini, PhD; Cinzia Bressi, MD, PhD; Darrell Campbell Jr, MD; Ennio Cocco, MD; Russell Deighton, PhD; Senem Demirci Alanyali, MD; Biagio Di Iorio, MD, PhD; David Dunwoodie, MBBS; Sharon Einav, MD; Madeleine Estryn‐Behar, PhD; Bernardo Gandini, MD; Keiki Hinami, MD; Antonio Lasalvia, MD, PhD; Joseph Lee, MD; Guido Maccacaro, MD; Swati Marner, EdD; Chris McManus, MD, PhD; Carmelle Peisah, MBBS, MD; Katarina Putnik, MSc; Alfredo Rodrguez‐Muoz, PhD; Yahya Shehabi, MD; Evelyn Sosa Oberlin, MD; Jean Karl Soler, MD, MSc; Knut Srgaard, PhD; Cath Taylor; Viva Thorsen, MPH; Mascha Twellaar, MD; Edgar Voltmer, MD; Colin West, MD, PhD; and Deborah Whippen. The authors also thank the following colleagues for their help with translation: Dusanka Anastasijevic (Norwegian); Joyce Cheung‐Flynn, PhD (simplified Chinese); Ales Hlubocky, MD (Czech); Lena Jungheim, RN (Swedish); Erez Kessler (Hebrew); Kanae Mukai, MD (Japanese); Eliane Purchase (French); Aaron Shmookler, MD (Russian); Jan Stepanek, MD (German); Fernando Tondato, MD (Portuguese); Laszlo Vaszar, MD (Hungarian); and Joseph Verheidje, PhD (Dutch). Finally, the authors thank Cynthia Heltne and Diana Rogers for their expert and tireless library assistance, Bonnie Schimek for her help with figures, and Cindy Laureano and Elizabeth Jones for their help with author contact.

Hospital medicine is a rapidly growing field of US clinical practice.[1] Almost since its advent, concerns have been expressed about the potential for hospitalists to burn out.[2] Hospitalists are not unique in this; similar concerns heralded the arrival of other location‐defined specialties, including emergency medicine[3] and the full‐time intensivist model,[4] a fact that has not gone unnoted in the literature about hospitalists.[5]

The existing international literature on physician burnout provides good reason for this concern. Inpatient‐based physicians tend to work unpredictable schedules, with substantial impact on home life.[6] They tend to be young, and much burnout literature suggests a higher risk among younger, less‐experienced physicians.[7] When surveyed, hospitalists have expressed more concerns about their potential for burnout than their outpatient‐based colleagues.[8]

In fact, data suggesting a correlation between inpatient practice and burnout predate the advent of the US hospitalist movement. Increased hospital time was reported to correlate with higher rates of burnout in internists,[9] family practitioners,[10] palliative physicians,[11] junior doctors,[12] radiologists,[13] and cystic fibrosis caregivers.[14] In 1987, Keinan and Melamed[15] noted, Hospital work by its very nature, as compared to the work of a general practitioner, deals with the more severe and complicated illnesses, coupled with continuous daily contacts with patients and their anxious families. In addition, these physicians may find themselves embroiled in the power struggles and competition so common in their work environment.

There are other features, however, that may protect inpatient physicians from burnout. Hospital practice can facilitate favorable social relations involving colleagues, co‐workers, and patients,[16] a factor that may be protective.[17] A hospitalist schedule also can allow more focused time for continuing medical education, research, and teaching,[18] which have all been associated with reduced risk of burnout in some studies.[17] Studies of psychiatrists[19] and pediatricians[20] have shown a lower rate of burnout among physicians with more inpatient duties. Finally, a practice model involving a seemingly stable cadre of inpatient physicians has existed in Europe for decades,[2] indicating at least a degree of sustainability.

Information suggesting a higher rate of burnout among inpatient physicians could be used to target therapeutic interventions and to adjust schedules, whereas the opposite outcome could refute a pervasive myth. We therefore endeavored to summarize the literature on burnout among inpatient versus outpatient physicians in a systematic fashion, and to include data not only from the US hospitalist experience but also from other countries that have used a similar model for decades. Our primary hypothesis was that inpatient physicians experience more burnout than outpatient physicians.

It is important to distinguish burnout from depression, job dissatisfaction, and occupational stress, all of which have been studied extensively in physicians. Burnout, as introduced by Freudenberger[21] and further characterized by Maslach,[22] is a condition in which emotional exhaustion, depersonalization, and a low sense of personal accomplishment combine to negatively affect work life (as opposed to clinical depression, which affects all aspects of life). Job satisfaction can correlate inversely with burnout, but it is a separate process[23] and the subject of a recent systematic review.[24] The importance of distinguishing burnout from job dissatisfaction is illustrated by a survey of head and neck surgeons, in which 97% of those surveyed indicated satisfaction with their jobs and 34% of the same group answered in the affirmative when asked if they felt burned out.[25]

One obstacle to the meaningful comparison of burnout prevalence across time, geography, and specialty is the myriad ways in which burnout is measured and reported. The oldest and most commonly used instrument to measure burnout is the Maslach Burnout Inventory (MBI), which contains 22 items assessing 3 components of burnout (emotional exhaustion, depersonalization, and low personal accomplishment).[26] Other measures include the Copenhagen Burnout Inventory[27] (19 items with the components personal burnout, work‐related burnout, and client‐related burnout), Utrecht Burnout Inventory[28] (20‐item modification of the MBI), Boudreau Burnout Questionnaire[29] (30 items), Arbeitsbezogenes Verhaltens und Erlebensmuster[30] (66‐item questionnaire assessing professional commitment, resistance to stress, and emotional well‐being), Shirom‐Melamed Burnout Measure[31] (22 items with subscales for physical fatigue, cognitive weariness, tension, and listlessness), and a validated single‐item questionnaire.[32]

METHODS

Electronic searches of MEDLINE, EMBASE, PsycINFO, SCOPUS, and PubMed were undertaken for articles published from January 1, 1974 (the year in which burnout was first described by Freudenberger[21]) to 2012 (last accessed, September 12, 2012) using the Medical Subject Headings (MeSH) terms stress, psychological; burnout, professional; adaptation, psychological; and the keyword burnout. The same sources were searched to create another set for the MeSH terms hospitalists, physician's practice patterns, physicians/px, professional practice location, and the keyword hospitalist#. Where exact subject headings did not exist in databases, comparable subject headings or keywords were used. The 2 sets were then combined using the operator and. Abstracts from the Society of Hospital Medicine annual conferences were hand‐searched, as were reference lists from identified articles. To ensure that pertinent international literature was captured, there was no language restriction in the search.

A 2‐stage screening process was used. The titles and abstracts of all articles identified in the search were independently reviewed by 2 investigators (D.L.R. and K.J.C.) who had no knowledge of each other's results. An article was obtained when either reviewer deemed it worthy of full‐text review.

All full‐text articles were independently reviewed by the same 2 investigators. The inclusion criterion was the measurement of burnout in physicians who are stated to or can be reasonably assumed to spend the substantial majority of their clinical practice exclusively in either the inpatient or the outpatient setting. Studies of emergency department physicians or specialists who invariably spend substantial amounts of time in both settings (eg, surgeons, anesthesiologists) were excluded. Studies limited to trainees or nonphysicians were also excluded. For both stages of review, agreement between the 2 investigators was assessed by calculating the statistic. Disagreements about inclusion were adjudicated by a third investigator (A.I.B.).

Because our goal was to establish and compare the rate of burnout among US hospitalists and other inpatient physicians around the world, we included studies of hospitalists according to the definition in use at the time of the individual study, noting that the formal definition of a hospitalist has changed over the years.[33] Because practice patterns for physicians described as primary care physicians, family doctors, hospital doctors, and others differ substantially from country to country, we otherwise included only the studies where the practice location was stated explicitly or where the authors confirmed that their study participants either are known or can be reasonably assumed to spend more than 75% of their time caring for hospital inpatients, or are known or can be reasonably assumed to spend the vast majority of their time caring for outpatients.

Data were abstracted using a standardized form and included the measure of burnout used in the study, results, practice location of study subjects, and total number of study subjects. When data were not clear (eg, burnout measured but not reported by the authors, practice location of study subjects not clear), authors were contacted by email, or when no current email address could be located or no response was received, by telephone or letter. In instances where burnout was measured repeatedly over time or before and after a specific intervention, only the baseline measurement was recorded. Because all studies were expected to be nonrandomized, methodological quality was assessed using a version of the tool of Downs and Black,[34] adapted where necessary by omitting questions not applicable to the specific study type (eg randomization for survey studies)[35] and giving a maximum of 1 point for the inclusion of a power calculation.

Two a priori analyses were planned: (1) a statistical comparison of articles directly comparing burnout among inpatient and outpatient physicians, and (2) a statistical comparison of articles measuring burnout among inpatient physicians with articles measuring burnout among outpatient physicians by the most frequently reported measuremean subset scores for emotional exhaustion, depersonalization, and personal accomplishment on the MBI.

The primary outcome measures were the differences between mean subset scores for emotional exhaustion, depersonalization, and personal accomplishment on the MBI. All differences are expressed as (outpatient meaninpatient mean). The variance of each outcome was calculated with standard formulas.[36] To calculate the overall estimate, each study was weighted by the reciprocal of its variance. Studies with fewer than 10 subjects were excluded from statistical analysis but retained in the systematic review.

For studies that reported data for both inpatient and outpatient physicians (double‐armed studies), Cochran Q test and the I2 value were used to assess heterogeneity.[37, 38] Substantial heterogeneity was expected because these individual studies were conducted for different populations in different settings with different study designs, and this expectation was confirmed statistically. Therefore, we used a random effects model to estimate the overall effect, providing a conservative approach that accounted for heterogeneity among studies.[39]

To assess the durability of our findings, we performed separate multivariate meta‐regression analyses by including single‐armed studies only and including both single‐armed and double‐armed studies. For these meta‐regressions, means were again weighted by the reciprocal of their variances, and the arms of 2‐armed studies were considered separately. This approach allowed us to generate an estimate of the differences between MBI subset scores from studies that did not include such an estimate when analyzed separately.[40]

We examined the potential for publication bias in double‐armed studies by constructing a funnel plot, in which mean scores were plotted against their standard errors.[41] The trim‐and‐fill method was used to determine whether adjustment for publication bias was necessary. In addition, Begg's rank correlation test[42] was completed to test for statistically significant publication bias.

Stata 10.0 statistical software (StataCorp, College Station, TX) was used for data analyses. A P value of 0.05 or less was deemed statistically significant. The Preferred Reporting Items for Systematic Reviews and Meta‐analysis checklist was used for the design and execution of the systematic review and meta‐analysis.[43]

Subgroup analyses based on location were undertaken a posteriori. All data (double‐armed meta‐analysis, meta‐regression of single‐armed studies, and meta‐regression of single‐ and double‐armed studies) were analyzed by location (United States vs other; United States vs Europe vs other).

RESULTS

The search results are outlined in Figure 1. In total, 1704 articles met the criteria for full‐text review. A review of pertinent reference lists and author contacts led to the addition of 149 articles. Twenty‐nine references could not be located by any means, despite repeated attempts. Therefore, 1824 articles were subjected to full‐text review by the 2 investigators.

Figure 1
Flow chart of study selection.

Initially, 57 articles were found that met criteria for inclusion. Of these, 2 articles reported data in formats that could not be interpreted.[44, 45] When efforts to clarify the data with the authors were unsuccessful, these studies were excluded. A study specifically designed to assess the response of physicians to a recent series of terrorist attacks[46] was excluded a posteriori because of lack of generalizability. Of the other 54 studies, 15 reported burnout data on both outpatient physicians and inpatient physicians, 22 reported data on outpatient physicians only, and 17 reported data on inpatient physicians only. Table 1 summarizes the results of the 37 studies involving outpatient physicians; Table 2 summarizes the 32 studies involving inpatient physicians.

Summaries of Studies of Outpatient‐Based Physicians Meeting Inclusion Criteria
Lead Author, Publication Year Study Population and Location Instrument No. of Participants EE Score (SD)a DP Score (SD) PA Score (SD) Other Results
  • NOTE: Abbreviations: AVEM, Arbeitsbezogenes Verhaltens und Erlebensmuster; DP, depersonalization subset of MBI; EE, emotional exhaustion subset of MBI; MBI, Maslach Burnout Inventory; PA, personal accomplishment subset of MBI; SMBM, Shirom‐Melamad Burnout Measure; SD, standard deviation.

  • High scores of EE and DP and low scores of PA are features of burnout.

  • Data obtained directly from authors 20102012.

  • SDs calculated from published standard errors. Personal accomplishment scale reversed to match other studies.

  • SDs calculated from published CIs.

Schweitzer, 1994[12] Young physicians of various specialties in South Africa Single‐item survey 7 6 (83%) endorsed burnout
Aasland, 1997 [54]b General practitioners in Norway Modified MBI (22 items; scale, 15) 298 2.65 (0.80) 1.90 (0.59) 3.45 (0.40)
Grassi, 2000 [58] General practitioners in Italy MBI 182 18.49 (11.49) 6.11 (5.86) 38.52 (7.60)
McManus, 2000 [59]b General practitioners in United Kingdom Modified MBI (9 items; scale, 06) 800 8.34 (4.39) 3.18 (3.40) 14.16 (2.95)
Yaman, 2002 [60] General practitioners in 8 European nations MBI 98 25.1 (8.50) 7.3 (4.92) 34.5 (7.67)
Cathbras, 2004 [61] General practitioners in France MBI 306 21.85 (12.4) 9.13 (6.7) 38.7 (7.1)
Goehring, 2005 [63] General practitioners, general internists, pediatricians in Switzerland MBI 1755 17.9 (9.8) 6.5 (4.7) 39.6 (6.5)
Esteva, 2006 [64] General practitioners, pediatricians in Spain MBI 261 27.4 (11.8) 10.07 (6.4) 35.9 (7.06)
Gandini, 2006 [65]b Physicians of various specialties in Argentina MBI 67 31.0 (13.8) 10.2 (6.6) 38.4 (6.8)
Ozyurt, 2006 [66] General practitioners in Turkey Modified MBI (22 items; scale, 04) 55 15.23 (5.80) 4.47 (3.31) 23.38 (4.29)
Deighton, 2007 [67]b Psychiatrists in several German‐speaking nations MBI 19 30.68 (9.92) 13.42 (4.23) 37.16 (3.39)
Dunwoodie, 2007 [68]b Palliative care physicians in Australia MBI 21 14.95 (9.14) 3.95 (3.40) 38.90 (2.88)
Srgaard, 2007 [69]b Psychiatrists in 5 European nations MBI 22 19.41 (8.08) 6.68 (4.93) 39.00 (4.40)
Sosa Oberlin, 2007 [56]b Physicians of various specialties in Argentina Author‐designed instrument 33 26 (78.8%) had 4 burnout symptoms, 6.15 symptoms per physician
Voltmer, 2007 [57]b Physicians of various specialties in Germany AVEM 46 11 (23.9%) exhibited burnout (type B) pattern
dm, 2008 [70]b Physicians of various specialties in Hungary MBI 163 17.45 (11.12) 4.86 (4.91) 36.56 (7.03)
Di Iorio, 2008 [71]b Dialysis physicians in Italy Author‐designed instrument 54 Work: 2.6 (1.5), Material: 3.1 (2.1), Climate: 3.0 (1.1), Objectives: 3.4 (1.6), Quality: 2.2 (1.5), Justification: 3.2 (2.0)
Lee, 2008 [49]b Family physicians in Canada MBI 123 26.26 (9.53) 10.20 (5.22) 38.43 (7.34)
Truchot, 2008 [72] General practitioners in France MBI 259 25.4 (11.7) 7.5 (5.5) 36.5 (7.1)
Twellaar, 2008 [73]b General practitioners in the Netherlands Utrecht Burnout Inventory 349 2.06 (1.11) 1.71 (1.05) 5.08 (0.77)
Arigoni, 2009 [17] General practitioners, pediatricians in Switzerland MBI 258 22.8 (12.0) 6.9 (6.1) 39.0 (7.2)
Bernhardt, 2009 [75] Clinical geneticists in United States MBI 72 25.8 (10.01)c 10.9 (4.16)c 34.8 (5.43)c
Bressi, 2009 [76]b Psychiatrists in Italy MBI 53 23.15 (11.99) 7.02 (6.29) 36.41 (7.54)
Krasner, 2009 [77] General practitioners in United States MBI 60 26.8 (10.9)d 8.4 (5.1)d 40.2 (5.3)d
Lasalvia, 2009 [55]b Psychiatrists in Italy Modified MBI (16 items; scale, 06) 38 2.37 (1.27) 1.51 (1.15) 4.46 (0.87)
Peisah, 2009 [79]b Physicians of various specialties in Australia MBI 28 13.92 (9.24) 3.66 (3.95) 39.34 (8.55)
Shanafelt, 2009 [80]b Physicians of various specialties in United States MBI 408 20.5 (11.10) 4.3 (4.74) 40.8 (6.26)
Zantinge, 2009 [81] General practitioners in the Netherlands Utrecht Burnout Inventory 126 1.58 (0.79) 1.32 (0.72) 4.27 (0.77)
Voltmer, 2010 [83]b Psychiatrists in Germany AVEM 526 114 (21.7%) exhibited burnout (type B) pattern
Maccacaro, 2011 [85]b Physicians of various specialties in Italy MBI 42 14.31 (11.98) 3.62 (4.95) 38.24 (6.22)
Lucas, 2011 [84]b Outpatient physicians periodically staffing an academic hospital teaching service in United States MBI (EE only) 30 24.37 (14.95)
Shanafelt, 2012 [87]b General internists in United States MBI 447 25.4 (14.0) 7.5 (6.3) 41.4 (6.0)
Kushnir, 2004 [62] General practitioners and pediatricians in Israel MBI (DP only) and SMBM 309 9.15 (3.95) SMBM mean (SD), 2.73 per item (0.86)
Vela‐Bueno, 2008 [74]b General practitioners in Spain MBI 240 26.91 (11.61) 9.20 (6.35) 35.92 (7.92)
Lesic, 2009 [78]b General practitioners in Serbia MBI 38 24.71 (10.81) 7.47 (5.51) 37.21 (7.44)
Demirci, 2010 [82]b Medical specialists related to oncology practice in Hungary MBI 26 23.31 (11.2) 6.46 (5.7) 37.7 (8.14)
Putnik, 2011 [86]b General practitioners in Hungary MBI 370 22.22 (11.75) 3.66 (4.40) 41.40 (6.85)
Summary of Studies of Inpatient Physicians Meeting Inclusion Criteria
Lead Author, Publication Year Study Population and Location Instrument No. of Participants EE Score (SD)a DP Score (SD) PA Score (SD) Other Results
  • NOTE: Abbreviations: AVEM, Arbeitsbezogenes Verhaltens und Erlebensmuster; DP, depersonalization subset of MBI; EE, emotional exhaustion subset of MBI; MBI, Maslach Burnout Inventory; PA, personal accomplishment subset of MBI; SD, standard deviation.

  • High scores of EE and DP and low scores of PA are features of burnout.

  • SDs not available; study therefore excluded from statistical comparisons.

  • Different survey item than other studies in this table using a single‐item, 5‐point burnout measure.

  • Data obtained directly from authors 20102012.

  • Personal accomplishment scale reversed to match other studies.

Varga, 1996 [88] Hospital doctors in Spain MBI 179 21.61b 7.33b 35.28b
Aasland, 1997 [54] Hospital doctors in Norway Modified MBI (22 items; scale, 15) 582 2.39 (0.80) 1.81 (0.65) 3.51 (0.46)
Bargellini, 2000 [89] Hospital doctors in Italy MBI 51 17.45 (9.87) 7.06 (5.54) 35.33 (7.90)
Grassi, 2000 [58] Hospital doctors in Italy MBI 146 16.17 (9.64) 5.32 (4.76) 38.71 (7.28)
Hoff, 2001 [33] Hospitalists in United States Single‐item surveyc 393 12.9% burned out (>4/5), 24.9% at risk for burnout (34/5), 62.2% at no current risk (mean, 2.86 on 15 scale)
Trichard, 2005 [90] Hospital doctors in France MBI 199 16 (10.7) 6.6 (5.7) 38.5 (6.5)
Gandini, 2006 [65]d Hospital doctors in Argentina MBI 290 25.0 (12.7) 7.9 (6.2) 40.1 (7.0)
Dunwoodie, 2007 [68]d Palliative care doctors in Australia MBI 14 18.29 (14.24) 5.29 (5.89) 38.86 (3.42)
Srgaard, 2007 [69]d Psychiatrists in 5 European nations MBI 18 18.56 (9.32) 5.50 (3.79) 39.08 (5.39)
Sosa Oberlin, 2007 [56]d Hospital doctors in Argentina Author‐designed instrument 3 3 (100%) had 4 burnout symptoms, 8.67 symptoms per physician
Voltmer, 2007 [57]d Hospital doctors in Germany AVEM 271 77 (28.4%) exhibited burnout (type B) pattern
dm, 2008 [70]b Physicians of various specialties in Hungary MBI 194 19.23 (10.79) 4.88 (4.61) 35.26 (8.42)
Di Iorio, 2008 [71]d Dialysis physicians in Italy Author‐designed instrument 62 Work, mean (SD), 3.1 (1.4); Material, mean (SD), 3.3 (1.5); Climate, mean (SD), 2.9 (1.1); Objectives, mean (SD), 2.5 (1.5); Quality, mean (SD), 3.0 (1.1); Justification, mean (SD), 3.1 (2.1)
Fuss, 2008 [91]d Hospital doctors in Germany Copenhagen Burnout Inventory 292 Mean Copenhagen Burnout Inventory, mean (SD), 46.90 (18.45)
Marner, 2008 [92]d Psychiatrists and 1 generalist in United States MBI 9 20.67 (9.75) 7.78 (5.14) 35.33 (6.44)
Shehabi, 2008 [93]d Intensivists in Australia Modified MBI (6 items; scale, 15) 86 2.85 (0.93) 2.64 (0.85) 2.58 (0.83)
Bressi, 2009 [76]d Psychiatrists in Italy MBI 28 17.89 (14.46) 5.32 (7.01) 34.57 (11.27)
Brown, 2009 [94] Hospital doctors in Australia MBI 12 22.25 (8.59) 6.33 (2.71) 39.83 (7.31)
Lasalvia, 2009 [55]d Psychiatrists in Italy Modified MBI (16 items; scale, 06) 21 1.95 (1.04) 1.35 (0.85) 4.46 (1.04)
Peisah, 2009 [79]d Hospital doctors in Australia MBI 62 20.09 (9.91) 6.34 (4.90) 35.06 (7.33)
Shanafelt, 2009 [80]d Hospitalists and intensivists in United States MBI 19 25.2 (11.59) 4.4 (3.79) 38.5 (8.04)
Tunc, 2009 [95] Hospital doctors in Turkey Modified MBI (22 items; scale, 04) 62 1.18 (0.78) 0.81 (0.73) 3.10 (0.59)e
Cocco, 2010 [96]d Hospital geriatricians in Italy MBI 38 16.21 (11.56) 4.53 (4.63) 39.13 (7.09)
Doppia, 2011 [97]d Hospital doctors in France Copenhagen Burnout Inventory 1,684 Mean work‐related burnout score, 2.72 (0.75)
Glasheen, 2011 [98] Hospitalists in United States Single‐item survey 265 Mean, 2.08 on 15 scale 62 (23.4%) burned out
Lucas, 2011 [84]d Academic hospitalists in United States MBI (EE only) 26 19.54 (12.85)
Thorsen, 2011 [99] Hospital doctors in Malawi MBI 2 25.5 (4.95) 8.5 (6.36) 25.0 (5.66)
Hinami, 2012 [50]d Hospital doctors in United States Single‐item survey 793 Mean, 2.24 on 15 scale 261 (27.2%) burned out
Quenot, 2012 [100]d Intensivists in France MBI 4 33.25 (4.57) 13.50 (5.45) 35.25 (4.86)
Ruitenburg, 2012 [101] Hospital doctors in the Netherlands MBI (EE and DP only) 214 13.3 (8.0) 4.5 (4.1)
Seibt, 2012 [102]d Hospital doctors in Germany Modified MBI (16 items; scale, 06, reported per item rather than totals) 2,154 2.2 (1.4) 1.4 (1.2) 5.1 (0.9)
Shanafelt, 2012 [87]d Hospitalists in United States MBI 130 24.7 (12.5) 9.1 (6.9) 39.0 (7.6)

Table 3 summarizes the results of the 15 studies that reported burnout data for both inpatient and outpatient physicians, allowing direct comparisons to be made. Nine studies reported MBI subset totals with standard deviations, 2 used different modifications of the MBI, 2 used different author‐derived measures, 1 used only the emotional exhaustion subscale of the MBI, and 1 used the Arbeitsbezogenes Verhaltens und Erlebensmuster. Therefore, statistical comparison was attempted only for the 9 studies reporting comparable MBI data, comprising burnout data on 1390 outpatient physicians and 899 inpatient physicians.

Summary of Studies Including Both Inpatient‐Based and Outpatient‐Based Physicians Meeting Inclusion Criteria
Lead Author, Publication Year Location Instrument Inpatient‐Based Physicians Outpatient‐Based Physicians
No. Results, Score (SD)a No. Results, Score (SD)a
  • NOTE: Abbreviations: AVEM, Arbeitsbezogenes Verhaltens und Erlebensmuster; DP, depersonalization subset of MBI; EE, emotional exhaustion subset of MBI; MBI, Maslach Burnout Inventory; PA, personal accomplishment subset of MBI; SD, standard deviation.

  • High scores of EE and DP and low scores of PA are features of burnout.

  • Data obtained directly from authors from 20102012.

Aasland, 1997 [54]b Norway Modified MBI (22 items; scale, 15) 582 EE, 2.39 (0.80); DP, 1.81 (0.65); PA, 3.51 (0.46) 298 EE, 2.65 (0.80); DP, 1.90 (0.59); PA, 3.45 (0.40)
Grassi, 2000 [58] Italy MBI 146 EE, 16.17 (9.64); DP, 5.32 (4.76); PA, 38.71 (7.28) 182 EE, 18.49 (11.49); DP, 6.11 (5.86); PA, 38.52 (7.60)
Gandini, 2006 [65]b Argentina MBI 290 EE, 25.0 (12.7);DP, 7.9 (6.2); PA, 40.1 (7.0) 67 EE, 31.0 (13.8); DP, 10.2 (6.6); PA, 38.4 (6.8)
Dunwoodie, 2007 [68]b Australia MBI 14 EE, 18.29 (14.24); DP, 5.29 (5.89); PA, 38.86 (3.42) 21 EE, 14.95 (9.14); DP, 3.95 (3.40); PA, 38.90 (2.88)
Srgaard, 2007 [69]b 5 European nations MBI 18 EE, 18.56 (9.32); DP, 5.50 (3.79); PA, 39.08 (5.39) 22 EE, 19.41 (8.08); DP, 6.68 (4.93); PA, 39.00 (4.40)
Sosa Oberlin, 2007 [56]b Argentina Author‐designed instrument 3 3 (100%) had 4 burnout symptoms, 8.67 symptoms per physician 33 26 (78.8%) had 4 burnout symptoms, 6.15 symptoms per physician
Voltmer, 2007 [57]b Germany AVEM 271 77 (28.4%) exhibited burnout (type B) pattern 46 11 (23.9%) exhibited burnout (type B) pattern
dm, 2008 [70]b Hungary MBI 194 EE, 19.23 (10.79); DP, 4.88 (4.61); PA, 35.26 (8.42) 163 EE, 17.45 (11.12); DP, 4.86 (4.91); PA, 36.56 (7.03)
Di Iorio, 2008 [71]b Italy Author‐designed instrument 62 Work: 3.1 (1.4); material: 3.3 (1.5); climate: 2.9 (1.1); objectives: 2.5 (1.5); quality: 3.0 (1.1); justification: 3.1 (2.1) 54 Work: 2.6 (1.5); material: 3.1 (2.1); climate: 3.0 (1.1); objectives: 3.4 (1.6); quality: 2.2 (1.5); justification: 3.2 (2.0)
Bressi, 2009 [76]b Italy MBI 28 EE, 17.89 (14.46); DP, 5.32 (7.01); PA, 34.57 (11.27) 53 EE, 23.15 (11.99); DP, 7.02 (6.29); PA, 36.41 (7.54)
Lasalvia, 2009[55]b Italy Modified MBI (16 items; scale, 06) 21 EE, 1.95 (1.04); DP, 1.35 (0.85); PA, 4.46 (1.04) 38 EE, 2.37 (1.27); DP, 1.51 (1.15); PA, 4.46 (0.87)
Peisah, 2009 [79]b Australia MBI 62 EE, 20.09 (9.91); DP, 6.34 (4.90); PA, 35.06 (7.33) 28 EE, 13.92 (9.24); DP, 3.66 (3.95); PA, 39.34 (8.55)
Shanafelt, 2009 [80]b United States MBI 19 EE, 25.2 (11.59); DP, 4.4 (3.79); PA, 38.5 (8.04) 408 EE, 20.5 (11.10); DP, 4.3 (4.74); PA, 40.8 (6.26)
Lucas, 2011 [84]b United States MBI (EE only) 26 EE, 19.54 (12.85) 30 EE, 24.37 (14.95)
Shanafelt, 2012 [87]b United States MBI 130 EE, 24.7 (12.5); DP, 9.1 (6.9); PA, 39.0 (7.6) 447 EE, 25.4 (14.0); DP, 7.5 (6.3); PA, 41.4 (6.0)

Figure 2 shows that no significant difference existed between the groups regarding emotional exhaustion (mean difference, 0.11 points on a 54‐point scale; 95% confidence interval [CI], 2.40 to 2.61; P=0.94). In addition, there was no significant difference between the groups regarding depersonalization (Figure 3; mean difference, 0.00 points on a 30‐point scale; 95% CI, 1.03 to 1.02; P=0.99) and personal accomplishment (Figure 4; mean difference, 0.93 points on a 48‐point scale; 95% CI, 0.23 to 2.09; P=0.11).

Figure 2
Forest plot for double‐armed studies reporting Maslach Burnout Inventory scores for emotional exhaustion. The size of the square represents study size, and the bars represent the 95% confidence interval (CI).
Figure 3
Forest plot for double‐armed studies reporting Maslach Burnout Inventory scores for depersonalization. The size of the square represents study size and the bars represent the 95% confidence interval (CI).
Figure 4
Forest plot for double‐armed studies reporting Maslach Burnout Inventory scores for personal accomplishment. The size of the square represents study size and the bars represent the 95% confidence interval (CI). The direction of the y‐axis has been reversed so that greater burnout in outpatient physicians remains to the right.

We used meta‐regression to allow the incorporation of single‐armed MBI studies. Whether single‐armed studies were analyzed separately (15 outpatient studies comprising 3927 physicians, 4 inpatient studies comprising 300 physicians) or analyzed with double‐armed studies (24 outpatient arms comprising 5318 physicians, 13 inpatient arms comprising 1301 physicians), the lack of a significant difference between the groups persisted for the depersonalization and personal accomplishment scales (Figure 5). Emotional exhaustion was significantly higher in outpatient physicians when single‐armed studies were considered separately (mean difference, 6.36 points; 95% CI, 2.24 to 10.48; P=0.002), and this difference persisted when all studies were combined (mean difference, 3.00 points; 95% CI, 0.05 to 5.94, P=0.046).

Figure 5
Forest plots comparing results of meta‐analysis of 9 double‐armed studies, meta‐regression of 19 single‐armed studies, and meta‐regression of all 28 studies reporting Maslach Burnout Inventory scores. The direction of the y‐axis of the personal accomplishment plot has been reversed so that higher burnout in outpatient physicians remains to the right. Error bars represent the 95% confidence interval.

Subgroup analysis by geographic location showed US outpatient physicians had a significantly higher personal accomplishment score than US inpatient physicians (mean difference, 2.38 points; 95% CI, 1.22 to 3.55; P<0.001) in double‐armed studies. This difference did not persist when single‐armed studies were included through meta‐regression (mean difference, 0.55 points, 95% CI, 4.30 to 5.40, P=0.83).

Table 4 demonstrates that methodological quality was generally good from the standpoint of the reporting and bias subsections of the Downs and Black tool. External validity was scored lower for many studies due to the use of convenience samples and lack of information about physicians who declined to participate.

Assessment of Methodologic Quality
Lead Author, Publication Year Reporting External Validity Internal Validity: Bias Internal Validity: Confounding Power
  • NOTE: For survey studies (all studies except Krasner,[77] Lucas,[84] and Quenot[100]), questions regarding interventions were omitted. For uncontrolled studies (all studies except Lucas[84]), questions regarding controls were omitted. The presence of a power calculation was awarded 1 point.

Schweitzer, 1994 [12] 5 of 6 points 1 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Varga, 1996 [88] 5 of 6 points 1 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Aasland, 1997 [54] 3 of 6 points 2 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Bargellini, 2000 [89] 5 of 6 points 0 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Grassi, 2000 [58] 6 of 6 points 0 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
McManus, 2000 [59] 5 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Hoff, 2001 [33] 6 of 6 points 2 of 2 points 2 of 4 points 1 of 1 point 0 of 1 point
Yaman, 2002 [60] 5 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Cathbras, 2004 [61] 6 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Kushnir, 2004 [62] 5 of 6 points 0 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Goehring, 2005 [63] 6 of 6 points 1 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Trichard, 2005 [90] 3 of 6 points 1 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Esteva, 2006 [64] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Gandini, 2006 [65] 6 of 6 points 1 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Ozyurt, 2006 [66] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Deighton, 2007 [67] 5 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Dunwoodie, 2007 [68] 5 of 6 points 2 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Srgaard, 2007 [69] 6 of 6 points 0 of 2 points 3 of 4 points 1 of 1 point 1 of 1 point
Sosa Oberlin, 2007 [56] 4 of 6 points 0 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Voltmer, 2007 [57] 4 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
dm, 2008 [70] 5 of 6 points 2 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Di Iorio, 2008 [71] 6 of 6 points 0 of 2 points 2 of 4 points 0 of 1 point 0 of 1 point
Fuss, 2008 [91] 6 of 6 points 0 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Lee, 2008 [49] 4 of 6 points 2 of 2 points 4 of 4 points 0 of 1 point 1 of 1 point
Marner, 2008 [92] 6 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Shehabi, 2008 [93] 3 of 6 points 1 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Truchot, 2008 [72] 5 of 6 points 1 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Twellaar, 2008 [73] 6 of 6 points 2 of 2 points 3 of 4 points 0 of 1 point 0 of 1 point
Vela‐Bueno, 2008 [74] 5 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Arigoni, 2009 [17] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Bernhardt, 2009 [75] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Bressi, 2009 [76] 6 of 6 points 0 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Brown, 2009 [94] 6 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Krasner, 2009 [77] 9 of 11 points 0 of 3 points 6 of 7 points 1 of 2 points 1 of 1 point
Lasalvia, 2009 [55] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Lesic, 2009 [78] 5 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Peisah, 2009 [79] 6 of 6 points 2 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Shanafelt, 2009 [80] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Tunc, 2009 [95] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Zantinge, 2009 [81] 5 of 6 points 0 of 2 points 3 of 4 points 1 of 1 point 0 of 1 point
Cocco, 2010 [96] 4 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Demirci, 2010 [82] 6 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Voltmer, 2010 [83] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Doppia, 2011 [97] 5 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Glasheen, 2011 [98] 5 of 6 points 0 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Lucas, 2011 [84] 10 of 11 points 2 of 3 points 7 of 7 points 5 of 6 points 1 of 1 point
Maccacaro, 2011 [85] 5 of 6 points 0 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Putnik, 2011 [86] 6 of 6 points 1 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Thorsen, 2011 [99] 6 of 6 points 0 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Hinami, 2012 [50] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 1 of 1 point
Quenot, 2012 [100] 8 of 11 points 1 of 3 points 6 of 7 points 1 of 2 points 0 of 1 point
Ruitenburg, 2012 [101] 6 of 6 points 2 of 2 points 4 of 4 points 0 of 1 point 0 of 1 point
Seibt, 2012 [102] 6 of 6 points 0 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point
Shanafelt, 2012 [87] 6 of 6 points 2 of 2 points 4 of 4 points 1 of 1 point 0 of 1 point

Funnel plots were used to evaluate for publication bias in the meta‐analysis of the 8 double‐armed studies (Figure 6). We found no significant evidence of bias, which was supported by Begg's test P values of 0.90 for emotional exhaustion, >0.99 for depersonalization, and 0.54 for personal accomplishment. A trim‐and‐fill analysis determined that no adjustment was necessary.

Figure 6
Funnel plots for the 8 double‐armed studies that reported Maslach Burnout Inventory scores for emotional exhaustion, depersonalization, and personal accomplishment. Abbreviations: CI, confidence interval.

DISCUSSION

There appears to be no support for the long‐held belief that inpatient physicians are particularly prone to burnout. Among studies for which practice location was stated explicitly or could be obtained from the authors, and who used the MBI, no differences were found among inpatient and outpatient physicians with regard to depersonalization or personal accomplishment. This finding persisted whether double‐armed studies were compared directly, single‐armed studies were incorporated into this analysis, or single‐armed studies were analyzed separately. Outpatient physicians had a higher degree of emotional exhaustion when all studies were considered.

There are several reasons why outpatient physicians may be more prone to emotional exhaustion than their inpatient colleagues. Although it is by no means true that all inpatient physicians work in shifts, the increased availability of shift work may allow some inpatient physicians to better balance their professional and personal lives, a factor of work with which some outpatient physicians have struggled.[47] Inpatient practice may also afford more opportunity for teamwork, a factor that has been shown to correlate with reduced burnout.[48] When surveyed about burnout, outpatient physicians have cited patient volumes, paperwork, medicolegal concerns, and lack of community support as factors.[49] Inpatient physicians are not immune to these forces, but they arguably experience them to different degrees.

The absence of a higher rate of depersonalization among inpatient physicians is particularly reassuring in light of concerns expressed with the advent of US hospital medicinethat some hospitalists would be prone to viewing patients as an impediment to the efficient running of the hospital,[2] the very definition of depersonalization.

Although the difference in the whole sample was not statistically significant, the consistent tendency toward a greater sense of personal accomplishment among outpatient physicians is also noteworthy, particularly because post hoc subgroup analysis of US physicians did show statistical significance in both 2‐armed studies. Without detailed age data for the physicians in each study, we could not separate the possible impact of age on personal accomplishment; hospital medicine is a newer specialty staffed by generally younger physicians, and hospitalists may not have had time to develop a sense of accomplishment. When surveyed about job satisfaction, hospitalists have also reported the feeling that they were treated as glorified residents,[50] a factor that, if shared by other inpatient physicians, must surely affect their sense of personal accomplishment. The lack of longitudinal care for patients and the substantial provision of end‐of‐life care also may diminish the sense of personal accomplishment among inpatient physicians.

Another important finding from this systematic review is the marked heterogeneity of the instruments used to measure physician burnout. Many of the identified studies could not be subjected to meta‐analysis because of their use of differing burnout measures. Drawing more substantial conclusions about burnout and practice location is limited by the fact that, although the majority of studies used the full MBI, the largest study of European hospital doctors used the Copenhagen Burnout Inventory, and the studies thus far of US hospitalists have used single‐item surveys or portions of the MBI. Not reflected in this review is the fact that a large study of US burnout and job satisfaction[51] did not formally address practice location (M. Linzer, personal communication, August 2012). Similarly, a large study of British hospital doctors[52] is not included herein because many of the physicians involved had substantial outpatient duties (C. Taylor, personal communication, July 2012). Varying burnout measures have complicated a previous systematic review of burnout in oncologists.[53] Two studies that directly compared inpatient and outpatient physicians but that were excluded from our statistical analysis because of their modified versions of the MBI,[54, 55] showed higher burnout scores in outpatient physicians. Two other studies that provided direct inpatient versus outpatient comparisons but that used alternative burnout measures[56, 57] showed a greater frequency of burnout in inpatient physicians, but of these, 1 study[56] involved only 3 inpatient physicians.

Several limitations of our study should be considered. Although we endeavored to obtain information from authors (with some success) about specific local practice patterns and eliminated many studies because of incomplete data or mixed practice patterns (eg, general practitioners who take frequent hospital calls, hospital physicians with extensive outpatient duties in a clinic attached to their hospital), it remains likely that many physicians identified as outpatient provided some inpatient care (attending a few weeks per year on a teaching service, for example) and that some physicians identified as inpatient have minimal outpatient duties.

More importantly, the dataset analyzed is heterogeneous. Studies of the incidence of burnout are naturally observational and therefore not randomized. Inclusion of international studies is necessary to answer the research question (because published data on US hospitalists are sparse) but naturally introduces differences in practice settings, local factors, and other factors for which we cannot possibly account fully.

Our meta‐analysis therefore addressed a broad question about burnout among inpatient and outpatient physicians in various diverse settings. Applying it to any 1 population (including US hospitalists) is, by necessity, imprecise.

Post hoc analysis should be viewed with caution. For example, the finding of a statistical difference between US inpatient and outpatient physicians with regard to personal accomplishment score is compelling from the standpoint of hypothesis generation. However, it is worth bearing in mind that this analysis contained only 2 studies, both by the same primary author, and compared 855 outpatient physicians to only 149 hospitalists. This difference was no longer significant when 2 outpatient studies were added through meta‐regression.

Finally, the specific focus of this study on practice location precluded comparison with emergency physicians and anesthesiologists, 2 specialist types that have been the subject of particularly robust burnout literature. As the literature on hospitalist burnout becomes more extensive, comparative studies with these groups and with intensivists might prove instructive.

In summary, analysis of 24 studies comprising data on 5318 outpatient physicians and 1301 inpatient physicians provides no support for the commonly held belief that hospital‐based physicians are particularly prone to burnout. Outpatient physicians reported higher emotional exhaustion. Further studies of the incidence and severity of burnout according to practice location are indicated. We propose that in future studies, to avoid the difficulties with statistical analysis summarized herein, investigators ask about and explicitly report practice location (inpatient vs outpatient vs both) and report mean MBI subset data and standard deviations. Such information about US hospitalists would allow comparison with a robust (if heterogeneous) international literature on burnout.

Acknowledgments

The authors gratefully acknowledge all of the study authors who contributed clarification and guidance for this project, particularly the following authors who provided unpublished data for further analysis: Olaf Aasland, MD; Szilvia dm, PhD; Annalisa Bargellini, PhD; Cinzia Bressi, MD, PhD; Darrell Campbell Jr, MD; Ennio Cocco, MD; Russell Deighton, PhD; Senem Demirci Alanyali, MD; Biagio Di Iorio, MD, PhD; David Dunwoodie, MBBS; Sharon Einav, MD; Madeleine Estryn‐Behar, PhD; Bernardo Gandini, MD; Keiki Hinami, MD; Antonio Lasalvia, MD, PhD; Joseph Lee, MD; Guido Maccacaro, MD; Swati Marner, EdD; Chris McManus, MD, PhD; Carmelle Peisah, MBBS, MD; Katarina Putnik, MSc; Alfredo Rodrguez‐Muoz, PhD; Yahya Shehabi, MD; Evelyn Sosa Oberlin, MD; Jean Karl Soler, MD, MSc; Knut Srgaard, PhD; Cath Taylor; Viva Thorsen, MPH; Mascha Twellaar, MD; Edgar Voltmer, MD; Colin West, MD, PhD; and Deborah Whippen. The authors also thank the following colleagues for their help with translation: Dusanka Anastasijevic (Norwegian); Joyce Cheung‐Flynn, PhD (simplified Chinese); Ales Hlubocky, MD (Czech); Lena Jungheim, RN (Swedish); Erez Kessler (Hebrew); Kanae Mukai, MD (Japanese); Eliane Purchase (French); Aaron Shmookler, MD (Russian); Jan Stepanek, MD (German); Fernando Tondato, MD (Portuguese); Laszlo Vaszar, MD (Hungarian); and Joseph Verheidje, PhD (Dutch). Finally, the authors thank Cynthia Heltne and Diana Rogers for their expert and tireless library assistance, Bonnie Schimek for her help with figures, and Cindy Laureano and Elizabeth Jones for their help with author contact.

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  39. Furukawa TA, Guyatt GH, Griffith LE. Can we individualize the “number needed to treat”? An empirical study of summary effect measures in meta‐analyses. Int J Epidemiol. 2002;31(1):7276.
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  45. Curiel‐Garcia JA, Rodriguez‐Moran M, Guerrero‐Romero F. Burnout syndrome among health staff [in Spanish]. Rev Med Inst Mex Seguro Soc. 2006;44(3):221226.
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  47. Miedema B, Easley J, Fortin P, Hamilton R, Tatemichi S. Crossing boundaries: family physicians' struggles to protect their private lives. Can Fam Physician. 2009;55(3):286287.e5.
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  52. Ramirez AJ, Graham J, Richards MA, Cull A, Gregory WM. Mental health of hospital consultants: the effects of stress and satisfaction at work. Lancet. 1996 Mar 16;347(9003):7248.
  53. Mukherjee S, Beresford B, Glaser A, Sloper P. Burnout, psychiatric morbidity, and work‐related sources of stress in paediatric oncology staff: a review of the literature. Psycho‐Oncology. 2009 Oct;18(10):101928.
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  55. Lasalvia A, Bonetto C, Bertani M, et al. Influence of perceived organisational factors on job burnout: survey of community mental health staff. Br J Psychiatry. 2009;195(6):537544.
  56. Sosa Oberlin EN. Frecuencia de los sintomas del syndrome de burnout en profesionales medicos. Rev Med Rosario. 2007;73:1220.
  57. Voltmer E, Kieschke U, Spahn C. Work‐related behaviour and experience patterns of physicians compared to other professions. Swiss Med Wkly. 2007;137(31‐32):448453.
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  60. Yaman H, Soler JK. The job related burnout questionnaire: a multinational pilot study. Aust Fam Physician. 2002;31(11):10551056.
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  63. Goehring C, Bouvier Gallacchi M, Kunzi B, Bovier P. Psychosocial and professional characteristics of burnout in Swiss primary care practitioners: a cross‐sectional survey. Swiss Med Wkly. 2005;135(7‐8):101108.
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  65. Gandini BJ, Paulini SS, Marcos IJ, Jorge S, Luis F. The professional wearing down or syndrome of welfare labor stress (“burnout”) among health professionals in the city of Cordoba [in Spanish]. Rev Fac Cien Med Univ Nac Cordoba. 2006;63(1):1825.
  66. Ozyurt A, Hayran O, Sur H. Predictors of burnout and job satisfaction among Turkish physicians. QJM. 2006;99(3):161169.
  67. Deighton RM, Gurris N, Traue H. Factors affecting burnout and compassion fatigue in psychotherapists treating torture survivors: is the therapist's attitude to working through trauma relevant? J Trauma Stress. 2007;20(1):6375.
  68. Dunwoodie DA, Auret K. Psychological morbidity and burnout in palliative care doctors in Western Australia. Intern Med J. 2007;37(10): 693698.
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  71. Iorio B, Cillo N, Cucciniello E, Bellizzi V. Burn‐out in the dialysis unit. J Nephrol. 2008;21(suppl 13):S158S162.
  72. Truchot D. Career orientation and burnout in French general practitioners. Psychol Rep. 2008;103(3):875881.
  73. Twellaar M, Winants Y, Houkes I. How healthy are Dutch general practitioners? Self‐reported (mental) health among Dutch general practitioners. Eur J Gen Pract. 2008;14(1):49.
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Journal of Hospital Medicine - 8(11)
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Journal of Hospital Medicine - 8(11)
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653-664
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653-664
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Burnout in inpatient‐based versus outpatient‐based physicians: A systematic review and meta‐analysis
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Burnout in inpatient‐based versus outpatient‐based physicians: A systematic review and meta‐analysis
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Address for correspondence and reprint requests: Daniel L. Roberts, MD, Division of Hospital Internal Medicine, Mayo Clinic Hospital, 5777 E. Mayo Blvd, Phoenix, AZ 85054; Telephone: (480) 342‐1387; FAX: (480) 342‐1388; E‐mail: roberts.daniel@mayo.edu
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