Evaluating ambient artificial intelligence documentation: effects on work efficiency, documentation burden, and patient-centered care
Open accessAbstract
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.
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AI is rapidly reshaping healthcare and the competencies expected of graduating medical students.
“Artificial intelligence (AI) is rapidly reshaping healthcare and the competencies expected of graduating medical students”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [8]
The Background and significance section (page unavailable) describes recent advances in ambient AI documentation, and the Conclusion (page unavailable) reports effects on clinical workflow and patient care. These passages support AI changing an aspect of healthcare, but the paper does not address the competencies expected of graduating medical students; a search for medical-student competencies and curriculum found no relevant passage. Its evidence concerns ambient AI in one health system, not a healthcare-wide transformation. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAI has enabled novel digital solutions in diagnostics, imaging, and clinical decision support and patient management.
“AI has enabled novel digital solutions in diagnostics1–4, imaging5,6, and clinical decision support and patient management7–9.”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [8]
For this source, the relevant part of the citing sentence is clinical decision support and patient management, not diagnostics or imaging. The Results (Patient safety and care experience; page unavailable) report improved physician perceptions of patient-centered care after ambient AI documentation was introduced. But the Discussion describes clinical decision-making as a desired future expansion, not an existing capability demonstrated by the study; it does not establish AI-enabled clinical decision support or patient management. (LLM, conf=0.83)
Experimental — accuracy not yet measured
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Artificial intelligence: Physicians and patients’ communication
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- Authors
- Guo, Y., Wang, J., Hu, D., Tam, S., Gilman, C., Chow, E., Perret, D., Pandita, D., & Zheng, K.
- Affiliations
- University of California, Irvine (US)
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2025-10-16
- Volume
- 33
- Issue
- 2
- Pages
- 273-282
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC12844569
- OpenAlex
- W4415260223
- Cited by
- 43
- References
- 33
- Citations by year
- 2026: 42 · 2025: 1
- FWCI
- 33.82
- Type
- journal article
- Language
- en
- Open access
- green
- pmc.ncbi.nlm.nih.gov
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (UM1TR004927)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Keywords
- generative AI, clinical documentation burden, electronic health records, physician burnout, EHR usability, patient-centered care, clinical workflow efficiency, physician perceptions
- MeSH terms
- Artificial Intelligence, Burnout, Professional, Documentation, Humans, Physicians, Pilot Projects, Surveys and Questionnaires, Efficiency, Organizational, Patient-Centered Care, Workflow, Electronic Health Records, Quality Improvement
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