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Quality of Clinical Notes Created by Ambient Listening Generative AI: Pragmatic Prospective Pilot Study
Sandra L Taylor1,2, Melissa Jost3, Scott MacDonald3
1Department of Public Health Sciences, School of Medicine, University of California, Davis, 4480 2nd Avenue, Suite 4152, Sacramento, CA, 95817, United States, 1 916-734-4800.
JMIR Medical Informatics
|April 17, 2026
Summary
Ambient listening artificial intelligence (AI) scribes reduce physician documentation burden but require careful review. A pilot study found most AI-generated notes had minor errors, but vigilance is needed to prevent patient harm.
Area of Science:
- Medical Informatics
- Clinical Quality Improvement
- Artificial Intelligence in Healthcare
Background:
- Physicians face significant documentation burden from clinic visit notes.
- Ambient listening artificial intelligence (AI) scribes are emerging tools to alleviate this burden.
- Ensuring the safety of AI-generated clinical notes is crucial to prevent potential patient harm.
Purpose of the Study:
- To develop and pilot a standardized, efficient, and scalable method for evaluating AI-generated clinical notes for safety concerns.
- To assess the types and severity of errors in AI-generated notes within ambulatory care settings.
Main Methods:
- A 2-month pilot involving 31 physicians across multiple specialties using an AI scribe for 7545 clinic notes.
- Development of a novel survey instrument to assess note quality, focusing on inclusions, omissions, hallucinations, and bias.
- Physician evaluation of 356 AI-generated notes, rating error severity and analyzing vendor-reported editing metrics.
Main Results:
- Accidental omissions (18%) and hallucinations (11.5%) were the most common errors in AI-generated notes.
- The majority of errors (83.8%) were mild to moderate in severity; only 5.3% posed a serious risk.
- Physician editing varied widely, with a median of 9.0% of AI-generated words changed, and 14.9% of notes left unedited.
Conclusions:
- AI-generated clinical notes are generally high quality, with 94.7% free from significant errors.
- Despite high quality, the potential for serious harm from uncorrected errors necessitates continued clinician review.
- Health systems should pilot AI scribe technology and implement robust review processes to ensure safe adoption.
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