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Understanding Clinician Edits to Ambient AI Draft Notes: A Feasibility Analysis Using Large Language Models
2026-03-02
health informatics
Title + abstract only
View on medRxiv
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Ambient AI documentation tools generate draft notes that clinicians can review and edit before signing off in electronic health records. Scalable computational approaches to characterize how clinicians modify drafts remain limited, yet are essential for evaluating and improving AI effectiveness. We examined the feasibility of a few-shot prompted large language model (LLM) for categorizing sentence-level edits between AI drafts and final documentation. We developed five label-specific binary mode...
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