Physician Perspectives on Ambient AI Scribes
In this qualitative study, ambient AI scribes were found to positively impact physician workload, work-life integration, and patient engagement. Key facilitators and barriers to adoption were identified, along with specific suggestions for tool improvement. These findings suggest the potential for ambient AI scribes to reduce clinician burden, with user-centered recommendations offering practical guidance on ways to improve future iterations and improve adoption.
Authors
- Steven Lin (ORCID: https://orcid.org/0000-0001-8108-053X)
- Yejin Jeong (ORCID: https://orcid.org/0009-0003-4310-3472)
- Anna Devon-Sand (ORCID: https://orcid.org/0000-0002-8151-3048)
- Christopher Sharp (ORCID: https://orcid.org/0000-0001-7169-6904)
- P. Stephen (ORCID: https://orcid.org/0000-0003-3738-9569)
- Tait D. Shanafelt (ORCID: https://orcid.org/0000-0002-7106-5202)
- Michael A. Pfeffer (ORCID: https://orcid.org/0000-0002-8958-0843)
- Shreya Shah (ORCID: https://orcid.org/0000-0002-8242-7889)
- April S. Liang (ORCID: https://orcid.org/0009-0002-3890-4104)
- Patricia García (ORCID: https://orcid.org/0000-0002-9058-9933)
- Margaret Smith (ORCID: https://orcid.org/0009-0002-3874-4542)
- Trevor Crowell
- Betsy Yang
- Clarissa Delahaie
- Caroline Hsia
Institutions
- Stanford Health Care (US)
- Geriatric Research Education and Clinical Center (US)
- Stanford Medicine (US)
- Stanford University (US)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2025-03-24
- DOI
- https://doi.org/10.1001/jamanetworkopen.2025.1904
- Citations
- 78
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 51.04