Evaluating ambient artificial intelligence documentation: effects on work efficiency, documentation burden, and patient-centered care

Yawen Guo; Jiayuan Wang; Di Hu; Steven Tam; Charles Gilman; Emilie Chow; Danielle Perret; Deepti Pandita; Kai Zheng

doi:10.1093/jamia/ocaf180

Evaluating ambient artificial intelligence documentation: effects on work efficiency, documentation burden, and patient-centered care

J Am Med Inform Assoc. 2025 Oct 16:ocaf180. doi: 10.1093/jamia/ocaf180. Online ahead of print.

Authors

Yawen Guo¹, Jiayuan Wang¹, Di Hu¹, Steven Tam², Charles Gilman³, Emilie Chow², Danielle Perret⁴, Deepti Pandita², Kai Zheng¹

Affiliations

¹ Department of Informatics, University of California, Irvine, Irvine, CA 92617, United States.
² Department of Medicine, University of California, Irvine, Irvine, CA 92617, United States.
³ UCI Health, University of California, Irvine, Orange, CA 92868, United States.
⁴ Department of Physical Medicine & Rehabilitation, University of California, Irvine, Irvine, CA 92697, United States.

PMID: 41100159
DOI: 10.1093/jamia/ocaf180

Abstract

Background and significance: Ambient listening tools powered by generative artificial intelligence (GenAI) offer real-time, scribe-like support that reduce documentation burden and may help alleviate burnout. This study assesses physician-perceived benefits and challenges of ambient AI implementation through surveys and evaluates its effectiveness in clinical workflows using automatically recorded electronic health record (EHR) time-efficiency metrics.

Method and materials: A quality improvement pilot has been underway at UCI Health since December 2023. Epic EHR Signal metrics were analyzed to assess changes in note length, documentation time, and same-day encounter closure rates. Matched pre- and post-implementation surveys evaluated physician-perceived changes in documentation burden, clinical efficiency, and care quality. We also examined open-ended survey responses using thematic analysis to supplement quantitative findings.

Results: Analysis on EHR usage data from 167 physicians showed significant reductions in note-writing time, despite an increase in note length. Survey responses (n = 65) also indicated statistically significant improvements across multiple domains. Physicians reported reduced cognitive demand (P = .031) and documentation effort (P = .014), alongside perceptions of enhanced clinical efficiency, patient-centered care, and EHR system usability. Thematic analysis confirmed these quantitative findings and identified opportunities for improvement, including specialty-specific customization and expanded AI functionality.

Discussion: Ambient AI tools demonstrated improved documentation efficiency, perceived care quality, and reduced cognitive workload. These benefits suggest potential to alleviate key burdens in clinical documentation.

Conclusion: Future development should prioritize customization for specialty-specific and individual physician needs, ensure the reliability and accuracy of AI-generated content, and integrate ethical and legal considerations to facilitate safe and scalable implementation in patient-centered care contexts.

Keywords: ambient listening; artificial intelligence; documentation; efficiency; electronic health records [E05.318.308.940.968.625.500]; survey.

© The Author(s) 2025. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.