359P Validating large language model-assisted data extraction from clinical notes in head and neck oncology

Abstract

The administrative burden in healthcare is substantial, with clinicians spending nearly twice as much time on documentation as on direct patient care (Sinsky et al., 2016). A growing patient population, combined with increasingly complex and multimorbid conditions, necessitates more efficient workflows. Large Language Models (LLMs) offer a promising solution to reduce administrative burden and enhance data reuse by extracting structured information from unstructured clinical notes. However, validation is essential to ensure safe and effective integration into clinical practice (Bedi et al.

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