Beyond Billing Codes: An Agentic Large Language Model Pipeline for Extracting Glaucoma Procedural Data from Free-Text Clinical Notes

Purpose: To evaluate the accuracy of a large language model (LLM)-based agentic workflow to extract procedural data from free-text glaucoma clinical notes in the electronic health record (EHR). Design: Cross-sectional. Subjects: 1,250 subjects in the Bascom Palmer Ophthalmic Repository. Methods: Randomly sampled clinical notes from glaucoma encounters dated 2014 to 2024 were labeled by two glaucoma specialists (with an adjudicator as needed) for prior glaucoma-related procedures (name, date, details) and recommended future procedures (name, details). Notes were split into development (10%), va

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