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

TL;DR

This study investigates patient safety risks associated with AI scribes in healthcare, highlighting errors in transcription that could impact medication and treatment safety, based on user feedback analysis.

Contribution

It provides the first mixed-methods analysis of real-world patient safety issues linked to AI scribes in a large hospital system.

Findings

AI scribes may induce patient safety risks due to transcription errors

Errors are most significant in medication and treatment documentation

Further research needed to quantify the absolute risk levels

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.