Accepted for/Published in: JMIR AI
Date Submitted: Apr 29, 2025
Open Peer Review Period: May 21, 2025 - Jul 16, 2025
Date Accepted: Sep 18, 2025
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Real World Evidence-Synthesis of Digital Scribes using Ambient Listening and Generative Artificial Intelligence for Clinician Documentation Workflows: A Rapid Review
ABSTRACT
Background:
Digital scribes using ambient listening and generative AI have the potential to streamline clinical documentation and enhance workflow efficiency. However, despite growing interest, real-world evidence on their effects on clinician efficiency, satisfaction, quality, and integration remains limited.
Objective:
To synthesize evidence on clinician efficiency, user satisfaction, quality, and practical barriers associated with the use of digital scribes employing ambient listening and generative artificial intelligence (AI) in real-world clinical settings.
Methods:
A rapid review was conducted to evaluate the real-world evidence of digital scribes using ambient listening and generative AI in clinical practice from 2014 to 2024. Data was collected from Ovid MEDLINE, Embase, Web of Science - Core Collection, Cochrane CENTRAL and Reviews, and PubMed Central. Predefined eligibility criteria focused on studies addressing clinical implementation, excluding those centered solely on technical development or model validation. The findings of each study were synthesized and analyzed through the QUEST human evaluation framework for quality and safety and the SEIPS 3.0 Model to assess integration into clinician’s workflows and experience.
Results:
Of the 1,450 studies identified, six met inclusion criteria. These studies included an observational study, a case report, a peer-matched cohort study, and survey-based assessments conducted across academic health systems, community settings, and outpatient practices. The major themes noted were: (1) They decreased self-reported documentation times, with associated increased length of notes, (2) Physician burnout measured using standardized scales was unaffected, but physician engagement improved, (3) Physician productivity, assessed via billing metrics, was unchanged, (4) the studies fell short when compared to standardized frameworks.
Conclusions:
Digital scribes show promise in reducing documentation burden and enhancing clinician satisfaction, thereby supporting workflow efficiency. However, the current available evidence is sparse. Future real-world, multifaceted studies are needed before AI scribes can be recommended unequivocally. Clinical Trial: N/A
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