Patient Safety Risks from AI Scribes: Signals from End-User Feedback

Jessica Dai; Anwen Huang; Catherine Nasrallah; Rhiannon Croci; Hossein Soleimani; Sarah J. Pollet; Julia Adler-Milstein; Sara G. Murray; Jinoos Yazdany; Irene Y. Chen

arXiv · 2512.04118

Patient Safety Risks from AI Scribes: Signals from End-User Feedback

Jessica Dai·Anwen Huang·Catherine Nasrallah·Rhiannon Croci·Hossein Soleimani·Sarah J. Pollet·Julia Adler-Milstein·Sara G. Murray·Jinoos Yazdany·Irene Y. Chen

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Abstract

AI scribes are transforming clinical documentation at scale. However, their real-world performance remains understudied, especially regarding their impacts on patient safety. To this end, we initiate a mixed-methods study of patient safety issues raised in feedback submitted by AI scribe users (healthcare providers) in a large U.S. hospital system. Both quantitative and qualitative analysis suggest that AI scribes may induce various patient safety risks due to errors in transcription, most significantly regarding medication and treatment; however, further study is needed to contextualize the absolute degree of risk.

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Jessica Dai, Anwen Huang, Catherine Nasrallah, Rhiannon Croci, Hossein Soleimani, Sarah J. Pollet, Julia Adler-Milstein, Sara G. Murray, Jinoos Yazdany, Irene Y. Chen. 2025-12-01. Patient Safety Risks from AI Scribes: Signals from End-User Feedback. https://arxiv.org/abs/2512.04118

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