A Narrative-Driven Computational Framework for Clinician Burnout Surveillance
This addresses clinician burnout, a critical issue for patient safety in ICUs, by leveraging narrative data for proactive monitoring, though it is incremental as it builds on existing methods with new data and tailored components.
The study tackled clinician burnout in ICUs by analyzing 10,000 ICU discharge summaries using a hybrid pipeline combining BioBERT sentiment embeddings, a lexical stress lexicon, and LDA with workload proxies, achieving an F1 score of 0.84 and identifying high-risk specialties like Radiology, Psychiatry, and Neurology.
Clinician burnout poses a substantial threat to patient safety, particularly in high-acuity intensive care units (ICUs). Existing research predominantly relies on retrospective survey tools or broad electronic health record (EHR) metadata, often overlooking the valuable narrative information embedded in clinical notes. In this study, we analyze 10,000 ICU discharge summaries from MIMIC-IV, a publicly available database derived from the electronic health records of Beth Israel Deaconess Medical Center. The dataset encompasses diverse patient data, including vital signs, medical orders, diagnoses, procedures, treatments, and deidentified free-text clinical notes. We introduce a hybrid pipeline that combines BioBERT sentiment embeddings fine-tuned for clinical narratives, a lexical stress lexicon tailored for clinician burnout surveillance, and five-topic latent Dirichlet allocation (LDA) with workload proxies. A provider-level logistic regression classifier achieves a precision of 0.80, a recall of 0.89, and an F1 score of 0.84 on a stratified hold-out set, surpassing metadata-only baselines by greater than or equal to 0.17 F1 score. Specialty-specific analysis indicates elevated burnout risk among providers in Radiology, Psychiatry, and Neurology. Our findings demonstrate that ICU clinical narratives contain actionable signals for proactive well-being monitoring.