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Ambient artificial intelligence scribes: utilization and impact on documentation time.
J Am Med Inform Assoc. 2025 Feb 01; 32(2):381-385.JAMIA

Abstract

OBJECTIVES

To quantify utilization and impact on documentation time of a large language model-powered ambient artificial intelligence (AI) scribe.

MATERIALS AND METHODS

This prospective quality improvement study was conducted at a large academic medical center with 45 physicians from 8 ambulatory disciplines over 3 months. Utilization and documentation times were derived from electronic health record (EHR) use measures.

RESULTS

The ambient AI scribe was utilized in 9629 of 17 428 encounters (55.25%) with significant interuser heterogeneity. Compared to baseline, median time per note reduced significantly by 0.57 minutes. Median daily documentation, afterhours, and total EHR time also decreased significantly by 6.89, 5.17, and 19.95 minutes/day, respectively.

DISCUSSION

An early pilot of an ambient AI scribe demonstrated robust utilization and reduced time spent on documentation and in the EHR. There was notable individual-level heterogeneity.

CONCLUSION

Large language model-powered ambient AI scribes may reduce documentation burden. Further studies are needed to identify which users benefit most from current technology and how future iterations can support a broader audience.

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Authors+Show Affiliations

Ma SP
Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States.
Liang AS
Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States.
Shah SJ
Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States. Stanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA 94305, United States.
Smith M
Stanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA 94305, United States.
Jeong Y
Stanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA 94305, United States.
Devon-Sand A
Stanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA 94305, United States.
Crowell T
Stanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA 94305, United States.
Delahaie C
Technology and Digital Solutions, Stanford Medicine, Stanford, CA 94305, United States.
Hsia C
Technology and Digital Solutions, Stanford Medicine, Stanford, CA 94305, United States.
Lin S
Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States. Stanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA 94305, United States.
Shanafelt T
Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States. WellMD Center, Stanford University School of Medicine, Stanford, CA 94305, United States.
Pfeffer MA
Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States. Technology and Digital Solutions, Stanford Medicine, Stanford, CA 94305, United States.
Sharp C
Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States.
Garcia P
Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States.

MeSH

Electronic Health RecordsArtificial IntelligenceDocumentationHumansProspective StudiesTime FactorsQuality ImprovementAcademic Medical Centers

Pub Type(s)

Journal Article

Language

eng

PubMed ID

39688515

Citation

Ma, Stephen P., et al. "Ambient Artificial Intelligence Scribes: Utilization and Impact On Documentation Time." Journal of the American Medical Informatics Association : JAMIA, vol. 32, no. 2, 2025, pp. 381-385.
Ma SP, Liang AS, Shah SJ, et al. Ambient artificial intelligence scribes: utilization and impact on documentation time. J Am Med Inform Assoc. 2025;32(2):381-385.
Ma, S. P., Liang, A. S., Shah, S. J., Smith, M., Jeong, Y., Devon-Sand, A., Crowell, T., Delahaie, C., Hsia, C., Lin, S., Shanafelt, T., Pfeffer, M. A., Sharp, C., & Garcia, P. (2025). Ambient artificial intelligence scribes: utilization and impact on documentation time. Journal of the American Medical Informatics Association : JAMIA, 32(2), 381-385. https://doi.org/10.1093/jamia/ocae304
Ma SP, et al. Ambient Artificial Intelligence Scribes: Utilization and Impact On Documentation Time. J Am Med Inform Assoc. 2025 Feb 1;32(2):381-385. PubMed PMID: 39688515.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Ambient artificial intelligence scribes: utilization and impact on documentation time. AU - Ma,Stephen P, AU - Liang,April S, AU - Shah,Shreya J, AU - Smith,Margaret, AU - Jeong,Yejin, AU - Devon-Sand,Anna, AU - Crowell,Trevor, AU - Delahaie,Clarissa, AU - Hsia,Caroline, AU - Lin,Steven, AU - Shanafelt,Tait, AU - Pfeffer,Michael A, AU - Sharp,Christopher, AU - Garcia,Patricia, PY - 2024/8/20/received PY - 2024/10/29/revised PY - 2024/11/27/accepted PY - 2025/1/22/medline PY - 2024/12/17/pubmed PY - 2024/12/17/entrez PY - 2025/12/17/pmc-release KW - ambient intelligence KW - ambient scribes KW - artificial intelligence KW - documentation KW - informatics SP - 381 EP - 385 JF - Journal of the American Medical Informatics Association : JAMIA JO - J Am Med Inform Assoc VL - 32 IS - 2 N2 - OBJECTIVES: To quantify utilization and impact on documentation time of a large language model-powered ambient artificial intelligence (AI) scribe. MATERIALS AND METHODS: This prospective quality improvement study was conducted at a large academic medical center with 45 physicians from 8 ambulatory disciplines over 3 months. Utilization and documentation times were derived from electronic health record (EHR) use measures. RESULTS: The ambient AI scribe was utilized in 9629 of 17 428 encounters (55.25%) with significant interuser heterogeneity. Compared to baseline, median time per note reduced significantly by 0.57 minutes. Median daily documentation, afterhours, and total EHR time also decreased significantly by 6.89, 5.17, and 19.95 minutes/day, respectively. DISCUSSION: An early pilot of an ambient AI scribe demonstrated robust utilization and reduced time spent on documentation and in the EHR. There was notable individual-level heterogeneity. CONCLUSION: Large language model-powered ambient AI scribes may reduce documentation burden. Further studies are needed to identify which users benefit most from current technology and how future iterations can support a broader audience. SN - 1527-974X UR - https://www.unboundmedicine.com/medline/citation/39688515/Ambient_artificial_intelligence_scribes:_utilization_and_impact_on_documentation_time DB - PRIME DP - Unbound Medicine ER -
Grapherence [↓37 ↑19]

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