Detection of Gastrointestinal Bleeding with Large Language Models to Aid Quality Improvement and Appropriate Reimbursement

Early identification and accurate characterization of overt gastrointestinal bleeding (GIB) enables opportunities to optimize patient management and ensures appropriately risk-adjusted coding for claims-based quality measures and reimbursement. Recent advancements in generative artificial intelligence, particularly large language models (LLMs), create opportunities to support accurate identification of clinical conditions. In this study, we present the first LLM-based pipeline for identification of overt GIB in the electronic health record (EHR). We demonstrate two clinically relevant applications: the automated detection of recurrent bleeding and appropriate reimbursement coding for patients with GIB.