Trial reportJAMA network open2024
AI-Generated Draft Replies Integrated Into Health Records and Physicians' Electronic Communication.
Trial report in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
71 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Digital compassion fatigue and digital empathy in healthcare: mapping the evidence and informing a human-centered clinical framework.Frontiers in psychology · 2026Pooled it
- The TRIPOD-LLM reporting guideline for studies using large language models: a Korean translation.Ewha medical journal · 2025Guideline
- Computational Use of Patient-Provider Secure Messaging Data to Achieve Better Clinical Efficiency and Quality of Communication: A Systematic Review.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Pooled it
- AI-Based EMG Reporting: A Randomized Controlled Trial.Journal of neurology · 2025Trial
- GPT-4 assistance for improvement of physician performance on patient care tasks: a randomized controlled trial.Nature medicine · 2025Trial
- From patient notes to prognostication: The revolutionary potential of event-based foundation models in orthopaedics.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Article
- Program cost and return on investment analysis of remote patient monitoring for hypertension management in the cardiology department of a large healthcare system.Journal of telemedicine and telecare · 2026Article
- Large Language Models as a New Tool for Therapists in Internet-Based Cognitive Behavioral Therapy: Blinded Clinician Rating Pilot Experiment.JMIR mental health · 2026Article
- Physician-revised AI-generated drafts are associated with higher ratings of written explanations in end-of-life care in the intensive care unit: a scenario-based single-center cross-sectional study.Journal of intensive care · 2026Article
- Care Team Response to Patient Portal Message Content and Writing Style.JAMA network open · 2026Article
- Large reasoning models as thinking machines for medicine.Nature biomedical engineering · 2026Review
- Article
- Clinical trials for continuously monitored and updated AI systems.Nature medicine · 2026Article
- Applying artificial intelligence to ensure high quality and equitable lung cancer screening.Translational lung cancer research · 2026Review
- A scoping review of studies on secure messaging through patient portals: persistent challenges and potential solutions.npj health systems · 2026Article
- Applications of Generative Artificial Intelligence for Strabismus Surgery Video-Based Education.Ophthalmology science · 2026Article
- Tracing the Pen: Electronic Health Records Amid the Rise of Generative AI.NPJ digital medicine · 2026Review
- Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication.Patient education and counseling · 2026Article
- The potential of artificial intelligence in clinical trials.European journal of clinical investigation · 2026Review
- Artificial intelligence in primary care: innovation at a crossroads.The Lancet. Primary care · 2026Review
11 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.