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AI-Generated Draft Replies Integrated Into Health Records and Physicians' Electronic Communication.
Tai-Seale, Ming; Baxter, Sally L; Vaida, Florin; Walker, Amanda; Sitapati, Amy M; Osborne, Chad; Diaz, Joseph; Desai, Nimit; Webb, Sophie; Polston, Gregory; Helsten, Teresa; Gross, Erin; Thackaberry, Jessica; Mandvi, Ammar; Lillie, Dustin; Li, Steve; Gin, Geneen; Achar, Suraj; Hofflich, Heather; Sharp, Christopher; Millen, Marlene; Longhurst, Christopher A.
Afiliación
  • Tai-Seale M; Department of Family Medicine, University of California San Diego School of Medicine, La Jolla.
  • Baxter SL; Department of Medicine, University of California San Diego School of Medicine, La Jolla.
  • Vaida F; Department of Medicine, University of California San Diego School of Medicine, La Jolla.
  • Walker A; Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego School of Medicine, La Jolla.
  • Sitapati AM; Division of Biostatistics, University of California San Diego Herbert Wertheim School of Public Health and Human Longevity Science, La Jolla.
  • Osborne C; Department of Family Medicine, University of California San Diego School of Medicine, La Jolla.
  • Diaz J; Department of Medicine, University of California San Diego School of Medicine, La Jolla.
  • Desai N; Department of Family Medicine, University of California San Diego School of Medicine, La Jolla.
  • Webb S; Department of Medicine, University of California San Diego School of Medicine, La Jolla.
  • Polston G; University of California San Diego School of Medicine, La Jolla.
  • Helsten T; Department of Family Medicine, University of California San Diego School of Medicine, La Jolla.
  • Gross E; Department of Anesthesiology, University of California San Diego School of Medicine, La Jolla.
  • Thackaberry J; Department of Medicine, University of California San Diego School of Medicine, La Jolla.
  • Mandvi A; Department of Obstetrics and Gynecology, University of California San Diego School of Medicine, La Jolla.
  • Lillie D; Department of Psychiatry, University of California San Diego School of Medicine, La Jolla.
  • Li S; Department of Family Medicine, University of California San Diego School of Medicine, La Jolla.
  • Gin G; Department of Family Medicine, University of California San Diego School of Medicine, La Jolla.
  • Achar S; Department of Medicine, University of California San Diego School of Medicine, La Jolla.
  • Hofflich H; Department of Family Medicine, University of California San Diego School of Medicine, La Jolla.
  • Sharp C; Department of Family Medicine, University of California San Diego School of Medicine, La Jolla.
  • Millen M; Department of Medicine, University of California San Diego School of Medicine, La Jolla.
  • Longhurst CA; Department of Medicine, Stanford School of Medicine, Stanford, California.
JAMA Netw Open ; 7(4): e246565, 2024 Apr 01.
Article en En | MEDLINE | ID: mdl-38619840
ABSTRACT
Importance Timely tests are warranted to assess the association between generative artificial intelligence (GenAI) use and physicians' work efforts.

Objective:

To investigate the association between GenAI-drafted replies for patient messages and physician time spent on answering messages and the length of replies. Design, Setting, and

Participants:

Randomized waiting list quality improvement (QI) study from June to August 2023 in an academic health system. Primary care physicians were randomized to an immediate activation group and a delayed activation group. Data were analyzed from August to November 2023. Exposure Access to GenAI-drafted replies for patient messages. Main Outcomes and

Measures:

Time spent (1) reading messages, (2) replying to messages, (3) length of replies, and (4) physician likelihood to recommend GenAI drafts. The a priori hypothesis was that GenAI drafts would be associated with less physician time spent reading and replying to messages. A mixed-effects model was used.

Results:

Fifty-two physicians participated in this QI study, with 25 randomized to the immediate activation group and 27 randomized to the delayed activation group. A contemporary control group included 70 physicians. There were 18 female participants (72.0%) in the immediate group and 17 female participants (63.0%) in the delayed group; the median age range was 35-44 years in the immediate group and 45-54 years in the delayed group. The median (IQR) time spent reading messages in the immediate group was 26 (11-69) seconds at baseline, 31 (15-70) seconds 3 weeks after entry to the intervention, and 31 (14-70) seconds 6 weeks after entry. The delayed group's median (IQR) read time was 25 (10-67) seconds at baseline, 29 (11-77) seconds during the 3-week waiting period, and 32 (15-72) seconds 3 weeks after entry to the intervention. The contemporary control group's median (IQR) read times were 21 (9-54), 22 (9-63), and 23 (9-60) seconds in corresponding periods. The estimated association of GenAI was a 21.8% increase in read time (95% CI, 5.2% to 41.0%; P = .008), a -5.9% change in reply time (95% CI, -16.6% to 6.2%; P = .33), and a 17.9% increase in reply length (95% CI, 10.1% to 26.2%; P < .001). Participants recognized GenAI's value and suggested areas for improvement. Conclusions and Relevance In this QI study, GenAI-drafted replies were associated with significantly increased read time, no change in reply time, significantly increased reply length, and some perceived benefits. Rigorous empirical tests are necessary to further examine GenAI's performance. Future studies should examine patient experience and compare multiple GenAIs, including those with medical training.
Asunto(s)

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Médicos / Inteligencia Artificial Límite: Adult / Female / Humans / Male / Middle aged Idioma: En Revista: JAMA Netw Open Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Médicos / Inteligencia Artificial Límite: Adult / Female / Humans / Male / Middle aged Idioma: En Revista: JAMA Netw Open Año: 2024 Tipo del documento: Article