Ambient AI Scribes and Emergency Department Documentation Burden: Retrospective Cohort Study.

Authors

Carl Preiksaitis, Al'ai Alvarez, Maia Winkel, Mia Karamatsu, Ian Brown, Neetha Sama, Luke Morris, Jae-Yeon Lee, Allie Gubbels, Eileen Wahl, Anna Frye, Austin Schoeffler, Laleh Gharahbaghian, Christian Rose

Published in

JMIR AI. Volume 5. Pages e92193. Jul 02, 2026. Epub Jul 02, 2026.

Abstract

Clinician burnout has reached crisis levels in emergency medicine, with clinical documentation burden identified as a central contributing factor. Ambient artificial intelligence (AI) scribes offer a promising approach to reduce this burden, but objective evidence in the emergency department (ED) setting remains limited, and prior reports have been constrained by short observation windows and low adoption.
This study aimed to evaluate the association between ambient AI scribe use and on-shift documentation time during a 13-month staged rollout in a busy ED, accounting for physician- and patient-level factors.
We conducted a retrospective cohort study at a tertiary academic ED from February 2025 to March 2026. The analytic cohort comprised 10,344 encounters managed by 100 attending physicians across 4 ED care settings. We restricted analysis to encounters managed by a single attending physician and excluded those with human scribes. The comparison group comprised encounters in which the ambient AI scribe was not used; use was determined entirely at attending physician discretion on an encounter-by-encounter basis. The primary outcome was on-shift documentation time derived from electronic health record audit logs. We used mixed-effects linear models with physician random intercepts to adjust for patient and encounter characteristics.
Ambient AI scribe use was associated with a 72.6-second reduction in on-shift documentation time per encounter (95% CI 63.8-81.4; P<.001). The effect was similar in magnitude for high-use physicians (use rates of ≥18.2%, which was the cohort mean; -71.6 seconds) and low or moderate users (-64.2 seconds), with no statistically significant difference (P=.51). Note character count decreased by 690 characters (95% CI 273-1107; P=.001); after-shift documentation time increased modestly by 9.1 seconds (95% CI 2.9-15.3; P=.004). Negative control outcomes were largely null, and a within-clinician placebo permutation test yielded a distribution centered at 0 (mean -0.8 seconds), inconsistent with the observed effect arising from confounding alone.
In this single-center analysis, ambient AI scribe use was associated with a statistically significant reduction in on-shift documentation time (P<.001), equivalent to approximately 24 minutes per 8-hour shift if used across 20 encounters. These findings extend prior descriptive work with adjusted inferential evidence and support the clinical relevance of ambient AI scribes for ED documentation burden, although the magnitude of benefit varies by physician, patient, and workflow factors.

PMID:
42391625
Bibliographic data and abstract were imported from PubMed on 03 Jul 2026.

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