Tai-Seale AI draft replies

AI-Generated Draft Replies Integrated Into Health Records and Physicians' Electronic Communication

Patient / Population Intervention / Exposition Comparaison Critère de jugement

In 52 primary care physicians in an academic health system, alongside 70 contemporary controls, generative-AI drafted replies to patient messages did not reduce physician time spent reading and replying to messages compared with delayed (waiting-list) activation and contemporary controls.

N
52
Schéma
Randomised waiting-list quality-improvement study, single academic health system
Critère
Time spent reading and replying to messages, reply length, and likelihood to recommend
Pertinence
3
RésultatRead time +21.8% (95% CI 5.2% to 41.0%; P=.008); reply time −5.9% (95% CI −16.6% to 6.2%; P=.33); reply length +17.9% (95% CI 10.1% to 26.2%; P<.001). 25 physicians randomised to immediate and 27 to delayed activation.
Tai-Seale M, et al. JAMA Netw Open. 2024;7(4):e246565. 10.1001/jamanetworkopen.2024.6565
Discussion et critique

An early, honest test of the most widely deployed generative-AI feature in electronic health records: drafts made physicians read for longer and write longer replies without saving reply time, although participants saw value in them. A small quality-improvement study run from June to August 2023 with a waiting-list randomisation and a non-randomised contemporary control, and no patient-experience outcome — the authors themselves call for rigorous trials.