Forecasting Physician Burnout Risk: The Role of Electronic Health Record (EHR) and Operational Data in Predictive Models of Burnout

Physician burnout is a persistent global concern, driven by workload pressures, administrative demands, and emotional strain. It undermines both physician well-being and patient care, making early detection and intervention critical. While numerous reviews have examined the prevalence and drivers of burnout, less attention has been given to predictive approaches using electronic health records (EHR) and operational data. This review addresses that gap by evaluating how such data have been incorporated into predictive models, highlighting both their utility and limitations. A narrative review was conducted using PubMed and Google Scholar to identify peer-reviewed studies published between 2014 and June 2025. Ten studies met the inclusion criteria. Findings show that EHR and operational data can capture important predictors of burnout—such as documentation time, after-hours charting, and administrative burden—and are valuable for identifying high-risk clinics. However, current models struggle to achieve accuracy at the individual level because they rely heavily on quantitative workload metrics while neglecting organizational culture, leadership support, and psychosocial factors. This review underscores the need for next-generation predictive models that integrate qualitative and contextual variables with EHR-based measures. By articulating this gap, our contribution lies in reframing EHR and operational data not as standalone predictors but as components of multi-faceted, context-aware models. For healthcare leaders and policymakers, this means investing in tools that combine clinical, organizational, and personal dimensions to better forecast burnout and inform targeted interventions.