ArticleJournal of the American Medical Informatics Association : JAMIA2024
Leveraging large language models for generating responses to patient messages-a subjective analysis.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 4 of them syntheses that pooled it.
What it found
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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.
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Who cites it
65 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review.BMC primary care · 2025Pooled it
- Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review.Journal of medical Internet research · 2025Pooled it
- Large Language Models and Empathy: Systematic Review.Journal of medical Internet research · 2024Pooled it
- 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
- From Innovation to Impact: The CREATE Framework as a Blueprint for Large Language Model Adoption in Opioid Treatment Programs.Journal of medical Internet research · 2026Article
- Do ChatGPT and Gemini's Recommendations Align With Established Guidelines for Hand and Upper Extremity Surgery?Hand (New York, N.Y.) · 2026Article
- Embracing Large Language Models for Medical Applications, Part II: Building a Framework for Clinical Stewardship.Cureus · 2026Article
- A Secure User Interface for Preclinical Evaluation of AI in Patient Portal Message Management: Tutorial.JMIR medical informatics · 2026Article
- Generating Question Prompt Lists From Electronic Health Record Data Using Large Language Models: Iterative Evaluation Study.Journal of medical Internet research · 2026Article
- Applications, Challenges, and Future Directions of Large Language Models in Health Care Communication: Scoping Review.Journal of medical Internet research · 2026Article
- Exploring the potential of artificial intelligence and machine learning in orthopaedic surgery.Bulletin of the Hospital for Joint Disease (2013) · 2026Review
- A scoping review of studies on secure messaging through patient portals: persistent challenges and potential solutions.npj health systems · 2026Article
- Using Large Language Models to Generate Dietary Feedback Similar to Human Experts in Weight Management: Experiments on Real-World Scenario Data.Bioengineering (Basel, Switzerland) · 2026Article
- Optimization of University Counseling Consent Forms With Large Language Models: Multidimensional Comparative Evaluation.Journal of medical Internet research · 2026Article
- Enhancing quality of antimicrobial prescribing through 'Ask Eolas' (language model): a user-testing and simulation evaluation.npj antimicrobials and resistance · 2026Article
- From Pilot to Practice: A Sociotechnical Perspective for Sustainable Adoption of Patient Engagement Technologies.Applied clinical informatics · 2026Article
- A Dataset for Addressing Patient's Information Needs related to Clinical Course of Hospitalization.Scientific data · 2026Article
- Gaps in artificial intelligence research for rural health in the United States: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Large Language Models in Surgery: Promise, Pitfalls, and Practical Use.Journal of abdominal wall surgery : JAWS · 2026Review
- Large Language Models and Primary Care: A Scoping Review.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
- Update of
Authors and funding
9 authors.
Funding
Abstract
objectiveThis study aimed to develop and assess the performance of fine-tuned large language models for generating responses to patient messages sent via an electronic health record patient portal. MATERIALS AND
methodsUtilizing a dataset of messages and responses extracted from the patient portal at a large academic medical center, we developed a model (CLAIR-Short) based on a pre-trained large language model (LLaMA-65B). In addition, we used the OpenAI API to update physician responses from an open-source dataset into a format with informative paragraphs that offered patient education while emphasizing empathy and professionalism. By combining with this dataset, we further fine-tuned our model (CLAIR-Long). To evaluate fine-tuned models, we used 10 representative patient portal questions in primary care to generate responses. We asked primary care physicians to review generated responses from our models and ChatGPT and rated them for empathy, responsiveness, accuracy, and usefulness.
resultsThe dataset consisted of 499 794 pairs of patient messages and corresponding responses from the patient portal, with 5000 patient messages and ChatGPT-updated responses from an online platform. Four primary care physicians participated in the survey. CLAIR-Short exhibited the ability to generate concise responses similar to provider's responses. CLAIR-Long responses provided increased patient educational content compared to CLAIR-Short and were rated similarly to ChatGPT's responses, receiving positive evaluations for responsiveness, empathy, and accuracy, while receiving a neutral rating for usefulness.
conclusionThis subjective analysis suggests that leveraging large language models to generate responses to patient messages demonstrates significant potential in facilitating communication between patients and healthcare providers.
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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.