Clinician Burnout Associated With Sex, Clinician Type, Work Culture, and Use of Electronic Health Records

McPeek-Hinz, Eugenia R; Boazak, Mina; Sexton, J. Bryan; Adair, Kathryn C.; West, Vivian; Goldstein, Benjamin A.; Alphin, Robert S; Idris, Sherif; Hammond, W. Ed; Hwang, Shelley; Bae, Jonathan

doi:10.1001/jamanetworkopen.2021.5686

Cited by 57 publications

(56 citation statements)

References 42 publications

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“…Some important issues are caused by power and subordination relationships that exist in the professional sector, such as moral and gender harassment, wage inequality, and discrimination regarding work capacity; such issues may affect women’s QOL. Notably, advances in labor legislation have not been able to reverse these gender inequalities [ 45 , 46 , 47 ].…”

Section: Discussionmentioning

confidence: 99%

Gender Differences in the Quality of Life of Formal Workers

Louzado

Cortes

Oliveira

et al. 2021

IJERPH

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Background: This study aimed to assess the quality of life associated with gender inequalities in formal workers and to determine the effect of sociodemographic, clinical, and behavioral factors on the quality of life (QOL). Methods: This cross-sectional study involved 1270 workers. Quality of life was measured using the EUROHIS-QOL 8-Item and assessed in terms of psychological, environmental, social, and physical domains, while demographic, socioeconomic, behavioral, and clinical variables served as explanatory variables. Analyses were performed using an ordinal logistic regression model whose significance level was 5%. Results: Of the participants, 80.2% were men, and 19.8% were women; the mean age was 34 (standard deviation: ±10) and 32 (±9) years, respectively. In all prediction scenarios, men were more likely to have a higher quality of life, especially in the physical (odds ratio: 2.16; 95% confidence interval: 1.60–2.93) and psychological (odds ratio: 2.09; 95% confidence interval: 1.51–2.91) domains. Conclusions: Men and women had significantly different levels of quality of life, and sociodemographic, clinical, and behavioral variables partially clarified these differences, which were possibly established by a socio-historical process of construction of the work role determined by gender issues.

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Section: Discussionmentioning

confidence: 99%

Gender Differences in the Quality of Life of Formal Workers

Louzado

Cortes

Oliveira

et al. 2021

IJERPH

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“…This finding is consistent with a survey study of 1310 ambulatory clinicians, which found that work culture explained more variation in burnout than the EHR ( R 2 , 17.6% vs 1.3%; P < .001). 33 Similarly, physician-perceived EHR usability explained only 5.8% of burnout. 17 Although 2 recent physician surveys reported that excessive EHR time was the most prevalent stressor associated with burnout (OR, 1.99; 95% CI, 1.21-3.27) 34 and that physicians who were frustrated with their EHR had 2.4 times higher odds of burnout (95% CI, 1.6-3.7; P < .001), 35 the findings from the present analysis suggest either a smaller contribution of the EHR to physician turnover or a more complex relationship between EHR use, burnout, and attrition.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Electronic Health Record Use and Clinical Productivity and Their Association With Physician Turnover

et al. 2021

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IMPORTANCEPhysician turnover takes a heavy toll on patients, physicians, and health care organizations. Survey research has established associations of electronic health record (EHR) use with professional burnout and reduction in professional effort, but these findings are subject to response fatigue and bias. OBJECTIVE To evaluate the association of physician productivity and EHR use patterns, as determined by vendor-derived EHR use data platforms, with physician turnover. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study was conducted among nonteaching ambulatory physicians at a large ambulatory practice network based in New England. Data were collected from March 2018 to February 2020. MAIN OUTCOMES AND MEASURES Physician departure from the practice network; 4 time-based core measures of EHR use, normalized to 8 hours of scheduled clinical time; teamwork, percentage of a physician's orders that are placed by other members of the care team; and productivity measures of patient volume, intensity, and demand. RESULTSAmong 335 physicians assessed for eligibility, 314 unique physicians (89.2%) were included in the analysis (123 [39%] women; 100 [32%] aged 45-54 years), with 5663 physicianmonths of data. The turnover rate was 5.1%/year (32 of 314 physicians). Physicians completed a mean 2.6 appointments/hour (95% CI, 2.5-2.6 appointments/hour) and 206 appointments/month (95% CI, 197-215 appointments/month) with 5.5 hours (95% CI, 5.3-5.8 hours) of EHR time for every 8 hours of scheduled patient time. After controlling for gender, medical specialty, and time, the following variables were associated with turnover: inbox time (odds ratio [OR], 0.70; 95% CI, 0.61-0.82; P < .001), teamwork (OR, 0.68; 95% CI, 0.52-0.87; P = .003), demand (ie, proportion of available appointments filled: OR, 0.49; 95% CI, 0.35-0.70; P < .001), and age 45 to 54 years vs 25 to 34 years (OR, 0.19; 95% CI, 0.04-0.93; P = .04). CONCLUSIONS AND RELEVANCEIn this study, physician productivity and EHR use metrics were associated with physician departure. Prospectively tracking these metrics could identify physicians at high risk of departure who would benefit from early, team-based, targeted interventions. The counterintuitive finding that less time spent on the EHR (in particular inbox management) was associated with physician departure warrants further investigation.

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“…Numerous studies showed that female physicians had a higher prevalence of burnout from countries including America, China, Sweden, Nigeria, etc. ( Norlund et al, 2010 ; Dyrbye et al, 2011 ; Aguwa et al, 2014 ; Granek et al, 2016 ; Huang et al, 2019 ; Eden et al, 2020 ; Gold et al, 2021 ; Lee et al, 2021 ; McPeek-Hinz et al, 2021 ). A few other studies reported only marginal gender differences in physician burnout prevalence ( Linzer et al, 2002 ; te Brake et al, 2003 ; Śliwiński et al, 2014 ; El Ghaziri et al, 2019 ; Doraiswamy et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…For example, previous studies have used the Shirom Melamed Burnout Questionnaire ( Norlund et al, 2010 ), Freudenberger Burnout Scale ( Aguwa et al, 2014 ), Mini-Z burnout scale ( Gold et al, 2021 ), Utrechtse Burnout Schaal ( te Brake et al, 2003 ), or Burnout Scale Inventory ( Śliwiński et al, 2014 ). Others used one or two items to measure burnout ( Granek et al, 2016 ; El Ghaziri et al, 2019 ; Eden et al, 2020 ; McPeek-Hinz et al, 2021 ). Huang et al used a 7-point Likert scale for MBI-HSS, ranging from 1 to 7, instead of 0 to 6 ( Ma et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…In healthcare settings, several studies showed significant gender differences in physician burnout prevalence and associated factors. A few studies found that female health workers had a higher risk of burning out ( Aguwa et al, 2014 ; Granek et al, 2016 ; Eden et al, 2020 ; Loscalzo et al, 2021 ; McPeek-Hinz et al, 2021 ), and some suggested that educational level, socioeconomic status and working hours were associated with burnout in female physicians ( Norlund et al, 2010 ). On the contrary, a few studies found no gender differences in physician burnout ( Linzer et al, 2002 ; te Brake et al, 2003 ; Doraiswamy et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Gender Differences in Burnout Among Endocrinologists in China

et al. 2022

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ObjectiveTo survey the prevalence of burnout in a national sample of endocrinologists in China and to examine its correlates, with a special focus on gender differences.MethodsAn anonymous online survey was conducted among endocrinologists in 31 provincial government-owned “People’s Hospitals” of each province in mainland China. Demographic and work-related factors were collected from participants. The Maslach Burnout Inventory-Human Services Survey (MBI-HSS) was used to assess burnout, including emotional exhaustion (EE), depersonalization (DP), and reduced personal accomplishment (PA).ResultsA total of 711 endocrinologists (72.1% were female and mean age was 39.63 ± 8.51 years old) completed the survey. Burnout was reported by 32.8% of the participants. There were no significant gender differences in the overall prevalence of burnout or EE, DP, and PA (all p > 0.05). A multi-level linear regression revealed: (1) In male participants, PA was significantly associated with age (β = 0.03, p = 0.003), DP was inversely associated with age (β = −0.06, p = 0.005), EE was significantly associated with shorter sleep duration (β = −0.25, p = 0.006), and longer work hours (β = 0.01, p = 0.016). (2) In females, PA was significantly associated with age (β = 0.01, p = 0.038), EE and DP were both significantly associated with shorter sleep duration (β = −0.19, p = 0.001; and β = −0.15, p = 0.011, respectively). EE and DP were also associated with work hours (β = 0.02, p < 0.001; and β = 0.01, p < 0.001, respectively).ConclusionNearly one-third of endocrinologists in China experienced burnout. Although there were no significant gender differences in the prevalence of overall burnout or EE, DP, and PA scores, male and female participants differed in factors associated with EE, DP, and PA. Interventions need to be tailored to target different aspects in male and female endocrinologists and target different subgroups.

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Clinician Burnout Associated With Sex, Clinician Type, Work Culture, and Use of Electronic Health Records

Cited by 57 publications

References 42 publications

Gender Differences in the Quality of Life of Formal Workers

Gender Differences in the Quality of Life of Formal Workers

Analysis of Electronic Health Record Use and Clinical Productivity and Their Association With Physician Turnover

Gender Differences in Burnout Among Endocrinologists in China

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