Ambient Artificial Intelligence Scribe Adoption and Documentation Time in the Emergency Department - 10/02/26

Doi : 10.1016/j.annemergmed.2025.12.017

Carl Preiksaitis, MD, MEd ^a, Al’ai Alvarez, MD ^a, Maia Winkel, MD ^a, Mia Karamatsu, MD ^a, Ian Brown, MD, MS ^a, Neetha Sama, MS ^b, Luke Morris, MSDA, MHBS ^a, Jae-yeon Lee, MD ^a, Allie Gubbels, MD ^a, Eileen Wahl, MS ^a, Anna Frye, MPH ^a, Christian Rose, MD ^a,^⁎

^a Department of Emergency Medicine, Stanford University School of Medicine, Stanford, CA

^b Technology and Digital Solutions, Stanford Health Care, Stanford, CA

^∗ Corresponding author.

Sous presse. Épreuves corrigées par l'auteur. Disponible en ligne depuis le Tuesday 10 February 2026

Abstract

Study objectives

To describe real-world adoption of an ambient artificial intelligence (AI) scribe in the emergency department (ED) and compare documentation time and note characteristics between ambient and standard encounters using electronic health record audit logs.

Methods

We performed a retrospective observational study of adult ED encounters at a tertiary academic medical center. Attending physicians could optionally use an ambient AI scribe to generate notes from patient–clinician conversations. We included single-attending encounters in core ED zones and excluded visits with human scribes. Electronic health record audit logs provided documentation of time during and after the shift, total electronic health record time, and note length. We summarized adoption by physician, zone, and acuity and compared medians between ambient and standard encounters.

Results

Among 8,740 eligible encounters, 976 (11.2%) used ambient AI. Thirty-five of 92 attendings (38%) used the tool, and a small group of high-frequency users accounted for most ambient encounters. Ambient use clustered in telemedicine and vertical-care zones (chair-based ambulatory care) and in lower-acuity patients, as well as those not requiring interpreters. Median on-shift documentation time was 2:45 min for ambient encounters versus 3:50 min for standard encounters (difference −1:05; −28%). Median total electronic health record time was 8:39 min versus 10:21 min (−16%), and ambient notes were shorter overall.

Conclusion

Early ED implementation of ambient AI scribes showed low but highly skewed adoption, with physicians favoring lower acuity, noninterpreted encounters. When used, ambient AI was associated with shorter on-shift documentation time, total electronic health record time, and note length.

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Keywords : Ambient artificial intelligence, Scribes, Documentation burden, AI, Workload

Plan

Introduction

Background

Importance

Goals of This Investigation

Methods

Study Design and Setting

Participants and Intervention

Data Sources and Measures

Analysis

Results

Cohort Characteristics and Adoption

Documentation Time and Note Characteristics

Limitations

Discussion

	Please see page XX for the Editor’s Capsule Summary of this article.
	Supervising editor: David L. Schriger, MD, MPH. Specific detailed information about possible conflict of interest for individual editors is available at editors .
	Author contributions: CP, AA, IB, AF, and CR conceived of the study and designed the research approach. CP and CR designed the statistical analysis plan and gathered clinical data. IB, LM, and NM acquired and assembled datasets from the Epic EHR infrastructure. CP performed all statistical analyses and data processing. AA, MW, MK, J-yL, AG, and EW contributed to data interpretation and clinical context. CP and CR drafted the manuscript, and all authors contributed substantially to its revision and approved the final version. CP takes responsibility for the paper as a whole.
	Data sharing statement: A deidentified dataset, data dictionary and analytic code for this investigation are available upon request, from the date of article publication by contacting Christian Rose, MD, at ccrose@stanford.edu , to investigators who provide an IRB letter of approval.
	All authors attest to meeting the four ICMJE.org authorship criteria: (1) Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND (2) Drafting the work or revising it critically for important intellectual content; AND (3) Final approval of the version to be published; AND (4) Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
	Funding and support : By Annals’ policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org/ ). Some of the work that lead to this manuscript was completed through the generosity of the American Medical Association's (AMA) SPO 345980. Addressing the BURDEN: Quantifying and Understanding Unaccounted EHR Work Among Emergency Physicians.