Hybrid NLP and LLM system extracts deprescribing recommendations from electronic health records for older hospitalized patients.

Introduction: Polypharmacy in older adults is associated with increased risks of adverse drug events and functional decline. Discharge summaries often contain deprescribing recommendations, but these are frequently overlooked due to documentation complexity. Objective: To develop and validate a two-stage hybrid system combining rule-based natural language processing (NLP) and large language model (LLM) for automated extraction of deprescribing recommendations from discharge summaries. Methods: This retrospective cohort study included 850 discharge summaries from patients aged [≥]65 years with hospitalisation [≥]48 hours across six public hospitals in New South Wales, Australia. Model 1 (rule-based NLP) extracted discharge medications and candidate sentences containing pre-defined deprescribing keywords. Model 2 (open-source LLM) classified candidate sentences into five categories. Data were split into training (80%) and test (20%) sets. Gold standard classifications were established by independent reviews, followed by adjudication of discrepancies. Results: Model 1 extracted 9,631 discharge medications (median 11 per patient) and 1,061 candidate sentences from 850 patients (median age 82.8 years). Model 2 achieved an F1 score of 0.91 and accuracy of 0.90 on the test set. Inter-rater reliability showed substantial agreement (Cohen's kappa = 0.70). The most frequently identified medications recommended for deprescribing were antibiotics and opioids. The most common misclassification was incorrectly identifying actions completed during hospitalisation as post-discharge recommendations. The combined processing time averaged 12.6 seconds per discharge summary. Conclusions: A two-stage hybrid approach combining rule-based NLP and open-source LLM can accurately extract deprescribing recommendations from discharge summaries, enabling cost-efficient, privacy-compliant local deployment.