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Objective: To determine whether natural language processing (NLP) can effectively detect adverse events defined in the New York Patient Occurrence Reporting and Tracking System (NYPORTS) using discharge summaries.

Design: An adverse event detection system for discharge summaries using the NLP system MedLEE was constructed to identify 45 NYPORTS event types. The system was first applied to a random sample of 1,000 manually reviewed charts. The system then processed all inpatient cases with electronic discharge summaries for two years. All system-identified events were reviewed, and performance was compared with traditional reporting.

Measurements: System sensitivity, specificity, and predictive value, with manual review serving as the gold standard.

Results: The system correctly identified 16 of 65 events in 1,000 charts. Of 57,452 total electronic …