Systemwide adoption patterns of ambient AI scribe technology in an integrated academic health system
Ambient artificial intelligence (AI) scribes may reduce documentation burden, but little is known about their adoption during enterprise-scale ambulatory rollout. We conducted a retrospective observational cohort study of 2901 ambulatory clinicians at an integrated academic health system using linked electronic health record and vendor usage data from December 2024 through May 2025. Clinician adoption was defined as any use during the study period, and monthly visit-level uptake as the proportion of eligible ambulatory visits in which the ambient AI scribe was used. Adoption and uptake varied substantially across practice settings, specialties, and clinician types. Clinician-level adoption was highest in community internal medicine (67%), community general pediatrics (69%), and academic neurology (53%), and lowest in academic general surgery (16%), obstetrics and gynecology (18%), and psychiatry (19%). Community attending physicians and advanced practice providers showed marked increases in median visit-level uptake, reaching approximately 80%-90% in some groups by May 2025. Distinct longitudinal patterns included slow and steady adoption, early use followed by discontinuation, transient use, and late adoption. These findings suggest that implementation support and evaluation strategies should be tailored to local practice context rather than applied uniformly across a health system.
Authors
- Anmol Warman (ORCID: https://orcid.org/0000-0001-5564-6747)
- Sajida S. Chaudry (ORCID: https://orcid.org/0000-0002-9403-6885)
- Helen Hughes (ORCID: https://orcid.org/0000-0001-9359-2380)
- Tej Deepak Azad (ORCID: https://orcid.org/0000-0001-7823-4294)
- Manisha J. Loss (ORCID: https://orcid.org/0000-0001-6990-4209)
- Kimberly S. Peairs (ORCID: https://orcid.org/0000-0001-9475-2534)
- Carisa M M Cooney (ORCID: https://orcid.org/0000-0002-5475-206X)
- Derek J Baughman (ORCID: https://orcid.org/0000-0001-5549-6697)
- Brian W. Hasselfeld
- Deepti K. Tantry (ORCID: https://orcid.org/0009-0002-6884-4309)
- Ayşe P. Gürses (ORCID: https://orcid.org/0000-0001-7422-6852)
- Jennifer Ling Bagdasarian (ORCID: https://orcid.org/0000-0002-2426-4428)
- Anjalie Field
Institutions
- Johns Hopkins University (US)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2026-10-26
- DOI
- https://doi.org/10.1038/s41746-026-03326-6
- Primary Topic
- Electronic Health Records Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00