Quality of Clinical Notes Created by Ambient Listening Generative AI: Pragmatic Prospective Pilot Study

Clinical Snapshot

Key Findings

• P Value: Not provided

• Effect Size: Error rates: 18% omissions, 11.5% hallucinations, 9.3% inclusions, 1.1% bias

• Primary Outcome: 94.7% (337/356) of AI-generated notes were free from significant errors

• Nnt Or Sensitivity: 5.3% of notes contained errors with serious harm potential (severity 4-5)

• Confidence Interval: Not provided

Clinical Application

Requires systematic quality review process and physician training on error recognition Relevant for Australian practices considering AI scribes, but would need evaluation against local documentation standards and medico-legal requirements Ambulatory care physicians using AI documentation tools

Abstract

BACKGROUND: Physicians routinely document specifics of patient encounters in clinic visit notes, a critical but potentially time-consuming task. Ambient listening artificial intelligence (AI) technology is being integrated into clinical workflows to reduce documentation burden by creating draft visit notes. While this technology is promising, it is not perfect, and the potential for patient harm needs to be understood and mitigated. We developed and piloted an efficient, standardized approach to evaluating AI-generated notes for safety concerns in ambulatory care visits. OBJECTIVE: The objective of this quality improvement project was to develop and pilot an efficient, standardized, and scalable approach to evaluating AI-generated notes for safety concerns in ambulatory care visits. METHODS: During a 2-month pilot (July to August 2024), 31 physicians across multiple specialties used an ambient listening AI scribe to assist with the creation of 7545 clinic notes. A novel survey instrument was developed to assess note quality, focusing on 4 error types: accidental inclusions, accidental omissions, hallucinations, and bias. Physicians evaluated 356 (4.7%) AI-generated notes. Where an error was present, physicians rated its severity based on its potential to cause patient harm if it was not corrected, on a 0 to 5 scale. Additionally, a vendor-reported metric on the percentage of note content edited by physicians was analyzed. RESULTS: Of the 356 evaluated notes, accidental omissions were the most frequent error (n=64, 18%), followed by hallucinations (n=41, 11.5%), and accidental inclusions (n=33, 9.3%). Bias was rare (n=4, 1.1%). Most (119/142, 83.8%) errors were rated as mild to moderate (severity 1-3), with only 19 (5.3%) notes containing errors rated as posing serious or imminent risk (severity 4-5). Editing metrics across all AI-created notes showed a median of 9.0% (IQR 2.5%-21.9%) of AI-generated words were changed, with 14.9% (143/960) of notes left entirely unedited. Physician editing practices varied widely, with average percentages of AI-generated words changed ranging from 1.9% to 69.3% (median 9.0%, IQR 2.5%-21.9%). CONCLUSIONS: AI-generated clinical notes were generally of high quality, with 94.7% (337/356) free from significant errors. However, because a small number contained errors that carried the risk of serious harm if not corrected, careful clinician review of notes remains imperative. Prior to deploying an AI scribe, organizations should pilot the technology and include an efficient review process to understand the nature and type of errors common at their organization. This pilot provides a scalable model for other health systems seeking to implement AI scribe technology responsibly.

References

1. 1.Taylor, S. L., Jost, M., MacDonald, S., Ren, Y., Hilton, S., Davenport, S., Aizenberg, D., Hall, B., Lyles, C. R., & Adams, J. Y. (2026). Quality of Clinical Notes Created by Ambient Listening Generative AI: Pragmatic Prospective Pilot Study. JMIR Medical Informatics, 14(1), e86474. https://doi.org/10.2196/86474