The Effect of Ambient AI Documentation on Clinician Workload, Efficiency, and Patient Experience in a Multisite Emergency Department Network.

Authors

Markos G Kashiouris, Andrew Miner, Sameh Saleh, Matthrew Scripps, Lindsey Stanton, Golda Samuel, Brent Dibble

Published in

Applied clinical informatics. Aug 21, 2026. Epub Aug 21, 2026.

Abstract

Evaluation of ambient AI on patient experience, documentation efficiency, clinician workload, and clinical throughput across a large emergency department (ED) network.
Retrospective, observational cohort study of ambient AI rollout across 14 EDs (May 2024-June 2025). Clinicians applied the tool on a voluntary basis and contributed both AI-assisted and conventional notes. Outcomes included patient-reported experience, active editing time, disposition-to-completion time, copied-forward content, and on-time note completion. Clinician workload was assessed with pre- and post-implementation surveys including the NASA Task Load Index (TLX).
Ambient AI was used in 8.6% of 315,242 ED clinical notes. AI use was associated with higher top-box ratings for clinician listening (82.0% vs. 76.6%; OR 1.39; p = 0.003) but not likelihood to recommend (77.6% vs. 75.0%; OR 1.15; p = 0.185). Within-clinician paired analysis showed no difference in active editing time between AI and conventional notes (median difference +0.17 min; 95% CI, -1.00 to +1.55; p = 0.349), and clinicians typed 722 fewer characters per AI-assisted note. Documentation finalized 3.4 hours earlier for admitted and 6.0 hours earlier for discharged patients; the disposition-to-completion difference was +5.06 hours (p = 0.337), reflecting attenuation of early-adopter's advantage. AI-assisted notes were half as likely to include copied content (9.4% vs. 18.3%; OR 0.46; p < 0.001). NASA-TLX workload decreased by 40.2 points (95% CI 30.4-50.1).
Ambient AI documentation was associated with improved patient-reported listening, earlier note completion, reduced copied-forward content, and lower clinician-perceived cognitive burden, without altering active editing time per note. These findings suggest a scalable tool for reducing clerical burden in high-throughput emergency care, while introducing new responsibilities for clinicians to review, edit, and sign machine-generated notes.

PMID:
42628943
Bibliographic data and abstract were imported from PubMed on 22 Aug 2026.

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