Observational studyEmergency medicine Australasia : EMA2026
Evaluation of an AI Scribe Tool in the Emergency Department: A Single-Arm Observational Study.
Observational study in Emergency medicine Australasia : EMA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
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Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenerative artificial intelligence (AI) is reshaping the way clinicians record their clinical notes. AI-scribe systems leverage generative AI capabilities to transcribe clinical encounters into draft clinical notes. In this study, we assessed clinician uptake and estimated modelled documentation time savings for an AI-scribe system in an emergency department (ED).
methodED physicians and trainees were provided access to an AI-scribe for 5 weeks. Data from the first week were excluded. The transcript of each presentation, the initial AI-generated clinical note and the final EMR clinical notes were used to calculate time to finalise AI-assisted notes.
resultsForty ED consultants and 23 trainees accessed the AI-scribe. Over the study period, nine consultants (22.5%) and 11 registrars (48%) used the system. The AI-scribe was used for 248 ED presentations, including 185 (74.6%) by trainees and 63 (25.4%) by consultants. The system generated 44,489 words. Following clinician review, 18,140 words were added and 2274 were deleted prior to submitting the final clinical notes. For a clinician with an average typing speed, use of the AI-scribe resulted in a time saving of 7.1 h of documentation. This was reduced to 4.9 h for rapid typer clinicians. Overall, the initial AI-generated notes were modified on 1143 occasions. The most frequently revised section was the history of presenting illness (23.3%) followed by the management plan (22.9%).
conclusionUptake of AI-scribe was higher among trainees than consultants, and the platform achieved substantial time savings. Future studies are required to quantify real-time productivity gains over longer periods.
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