Understanding Physician Work and Well-being Through Social Network Modeling Using Electronic Health Record Data: a Cohort Study

BACKGROUND: Understanding association between factors related to clinical work environment and well-being can inform strategies to improve physicians’ work experience. OBJECTIVE: To model and quantify what drivers of work composition, team structure, and dynamics are associated with well-being. DESIGN: Utilizing social network modeling, this cohort study of physicians in an academic health center examined inbasket messaging data from 2018 to 2019 to identify work composition, team structure, and dynamics features. Indicators from a survey in 2019 were used as dependent variables to identify factors predictive of well-being. PARTICIPANTS: EHR data available for 188 physicians and their care teams from 18 primary care practices; survey data available for 163/188 physicians. MAIN MEASURES: Area under the receiver operating characteristic curve (AUC) of logistic regression models to predict well-being dependent variables was assessed out-of-sample. KEY RESULTS: The mean AUC of the model for the dependent variables of emotional exhaustion, vigor, and professional fulfillment was, respectively, 0.665 (SD 0.085), 0.700 (SD 0.082), and 0.669 (SD 0.082). Predictors associated with decreased well-being included physician centrality within support team (OR 3.90, 95% CI 1.28–11.97, P=0.01) and share of messages related to scheduling (OR 1.10, 95% CI 1.03–1.17, P=0.003). Predictors associated with increased well-being included higher number of medical assistants within close support team (OR 0.91, 95% CI 0.83–0.99, P=0.05), nurse-centered message writing practices (OR 0.89, 95% CI 0.83–0.95, P=0.001), and share of messages related to ambiguous diagnosis (OR 0.92, 95% CI 0.87–0.98, P=0.01). CONCLUSIONS: Through integration of EHR data with social network modeling, the analysis highlights new characteristics of care team structure and dynamics that are associated with physician well-being. This quantitative methodology can be utilized to assess in a refined data-driven way the impact of organizational changes to improve well-being through optimizing team dynamics and work composition. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11606-021-07351-x.