A Narrative-Driven Computational Framework for Clinician Burnout Surveillance

Clinician burnout threatens patient safety, care quality, and workforce sustainability, especially in high-acuity ICUs. Existing detection approaches rely on retrospective surveys or coarse EHR metadata, limiting their ability to capture the evolution of burnout-related stress. We analyze 10,000 ICU discharge summaries from the MIMIC-IV database and propose a narrative-driven, weakly supervised framework for provider-level surveillance of burnout risk. Our approach integrates BioBERT-based sentiment modeling, lexical stress cues, latent topic structure, structured workload proxies, and temporal dynamics. In the absence of survey ground truth, we use a quantile-based ordinal labeling strategy to distinguish low, medium, and high burnout risk. A logistic regression classifier achieves an F1 score of 0.84 for conservative high-risk screening, while temporal features enable trajectory-based monitoring without degrading point-in-time performance. Specialty-specific analysis reveals elevated narrative stress indicators among Radiology, Psychiatry, and Neurology providers. ICU clinical narratives encode actionable, longitudinal signals for scalable burnout surveillance beyond static sentiment or metadata-only approaches.