Special Topic on Burnout: Clinical Implementation of Artificial Intelligence Scribes in Healthcare: A Systematic Review

Background: Artificial intelligence (AI) scribes use advanced speech recognition and natural language processing to automate clinical documentation and ease administrative burden. However, little is known about the impact of AI scribes on clinicians, patients and organizations. Objectives: (1) To propose an evaluation framework to guide future AI scribe implementations, (2) to describe the impact of AI scribes along the domains proposed in the developed evaluation framework, and (3) to identify gaps in the AI scribe implementation literature to be evaluated in future studies. Methods: Databases including Embase, Embase Classic and Ovid Medline were searched, and a manual review was conducted of the New England Journal of Medicine AI. Studies published after 2021 that reported on the implementation of AI scribes in healthcare were included. Descriptive analysis was undertaken. Quality assessment was undertaken using the Newcastle-Ottawa Scale. The nominal group technique was used to develop an evaluation framework. Results: Eleven studies met the inclusion criteria, with ten published in 2024. The most frequently used AI scribe was Dragon Ambient eXperience (DAX) (n=7, 64%). While clinicians often reported improved documentation quality, AI scribe accuracy varied, frequently requiring manual edits and raising occasional concerns about errors. Ten studies reported improvements in at least one efficiency metric, and ten studies highlighted positive impacts on clinician wellness and burnout. Patient experience was assessed in three studies, all reporting favorable outcomes. Conclusions: AI scribes represent a promising tool for improving clinical efficiency and alleviating documentation burden. This systematic review highlights the potential benefits of AI scribes, including reduced documentation time and enhanced clinician satisfaction, while also identifying critical challenges such as variable adoption, performance limitations, and gaps in evaluation.

Publikationsverlauf

Eingereicht: 18. Februar 2025

Angenommen nach Revision: 29. April 2025

Accepted Manuscript online:
30. April 2025