Randomized Trial Protocol: Epic Generative AI Chart Summarization Tool to Reduce Ambulatory Provider Cognitive Task Load
Analyses will use clinician-level survey responses and aggregated EHR metrics; no patient-level protected health information will be included in the analytic dataset. Results will be disseminated via preprint and peer-reviewed publication.
Authors
- John N. Mafi (ORCID: https://orcid.org/0000-0002-0322-7636)
- Paul J. Lukac (ORCID: https://orcid.org/0000-0002-3315-6613)
- Aaron Chin (ORCID: https://orcid.org/0000-0001-9490-6580)
- Lauren E. Wisk (ORCID: https://orcid.org/0000-0003-2932-4140)
- Artem Romanov (ORCID: https://orcid.org/0009-0002-3308-1073)
- Hawkin E. Woo
- Maxwell Weng (ORCID: https://orcid.org/0009-0008-5348-6928)
- Nina Zhu
- Thomas C. Kingsley
- Sitaram Vangala
- Yan Phipps
- Pallavi Mynampati
Institutions
- RAND Corporation (US)
- University of California, Los Angeles (US)
- UCLA Health (US)
Publication Details
- Journal
- medRxiv
- Published
- 2026-02-22
- DOI
- https://doi.org/10.64898/2026.02.20.26346503
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health
- National Institute on Aging