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Evaluating Large Language Models for Drafting Emergency Department Discharge Summaries.

Christopher Y K WilliamsJaskaran BainsTianyu TangKishan PatelAlexa N LucasFiona ChenBrenda Y MiaoAtul J ButteAaron E Kornblith
Published in: medRxiv : the preprint server for health sciences (2024)
In this cross-sectional study of 100 ED encounters, we found that LLMs could generate accurate discharge summaries, but were liable to hallucination and omission of clinically relevant information. A comprehensive understanding of the location and type of errors found in GPT-generated clinical text is important to facilitate clinician review of such content and prevent patient harm.
Keyphrases
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