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

Abstract

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.

The ambiguity of “we”: Perceptions of teaming in dynamic environments and their implications

“…Our second general hypothesis posits that teaming expansiveness exhibits a curvilinear relationship with job satisfaction, in a concave (inverted U) shape, such that greater expansiveness is initially beneficial but becomes harmful at higher levels. Some expansiveness in perception is likely to be beneficial if it helps people distribute burdensome workload or develop fulfilling relationships; for instance, research using electronic health records has found that greater physician well-being is associated with having a higher number of medical assistants within the close support team and a higher share of messages related to ambiguous diagnoses that presumably require more teamwork to solve (Escribe et al, 2022). Yet organizational scholars have also cautioned that large teams tend to bring higher demands and costs for coordinating (Hackman, 1987).…”

HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records

“…Purely relying on self-reported measures, they are subject to inaccuracy of workload measurement and unable to provide unobtrusive burnout prediction [14]. Recently, the quantification of clinician workload based on EHR use has enabled studies to track EHR-based workload measures [5,31], and apply off-the-shelf machine learning to predict burnout based on delicately design summary statistics of clinical activities as features [12,22].…”

“…The availability of clinician activity logs, the digital footprint of physicians' EHR-based activities, has enabled studies for tracking EHR-based workload measures [5,31], offering new opportunities for assessing its associations with burnout. More recently, such activity logs have been used to predict burnout using off-the-shelf machine learning models [12,22]. However, as these models are unable to directly process unstructured data, they rely exclusively on feature engineering, using hand-crafted summary statistics of activity logs as features.…”

Implementing Generative AI in U.S. Hospital Systems

“…In fact, past attempts to disseminate new technologies into healthcare systems have often resulted in unintended consequences, such as an increase in the administrative burden on physicians and clinical teams. [5] Moreover, despite several successful proof of concepts and prototype pilots, the number of large-scale field implementations of AI-enabled software within healthcare systems is still relatively low. [6], [7] As hospitals begin to experiment with generative AI (GAI) tools in clinical settings, it is important to identify known challenges that might stand in the way of achieving better patient care and lower administrative burdens -and, where possible, document practices that can mitigate those challenges.…”