ArticleJournal of the American Medical Informatics Association : JAMIA2024
Using large language model to guide patients to create efficient and comprehensive clinical care message.
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 18 papers.
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Who cites it
18 citing papers in PubMed.
- Billing E-Visits and Patient Medical Advice Requests in a Large Academic Health Center.JAMA network open · 2026Article
- Evidence, use cases, and implementation safeguards of large language models in primary care.Communications medicine · 2026Review
- Generating Question Prompt Lists From Electronic Health Record Data Using Large Language Models: Iterative Evaluation Study.Journal of medical Internet research · 2026Article
- A scoping review of studies on secure messaging through patient portals: persistent challenges and potential solutions.npj health systems · 2026Article
- Evaluation of causal reasoning for large language models in contextualized clinical scenarios of laboratory test interpretation.NPJ digital medicine · 2026Article
- Fact-Checking Large Language Model Responses to a Health Care Prompt: Comparative Study.JMIR formative research · 2026Article
- Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.Burns & trauma · 2026Review
- The use of artificial intelligence in healthcare as perceived by the citizens and patients: a narrative review of the literature.European journal of public health · 2025Review
- Artificial intelligence for autoimmune diseases.NPJ digital medicine · 2025Article
- The evaluation illusion of large language models in medicine.NPJ digital medicine · 2025Article
- Primary care physicians' experiences with inbox triage.JAMIA open · 2025Article
- Prompt Engineering in Clinical Practice: Tutorial for Clinicians.Journal of medical Internet research · 2025Article
- A systematic review of early evidence on generative AI for drafting responses to patient messages.npj health systems · 2025Article
- Primary care staff members' experiences with managing electronic health record inbox messages.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Detecting emergencies in patient portal messages using large language models and knowledge graph-based retrieval-augmented generation.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Combating cardiovascular disease disparities: The potential role of artificial intelligence.American journal of preventive cardiology · 2025Article
- Article
- Exploring large language models for summarizing and interpreting an online brain tumor support forum.Digital healthArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
objectiveThis study aims to investigate the feasibility of using Large Language Models (LLMs) to engage with patients at the time they are drafting a question to their healthcare providers, and generate pertinent follow-up questions that the patient can answer before sending their message, with the goal of ensuring that their healthcare provider receives all the information they need to safely and accurately answer the patient's question, eliminating back-and-forth messaging, and the associated delays and frustrations.
methodsWe collected a dataset of patient messages sent between January 1, 2022 to March 7, 2023 at Vanderbilt University Medical Center. Two internal medicine physicians identified 7 common scenarios. We used 3 LLMs to generate follow-up questions: (1) Comprehensive LLM Artificial Intelligence Responder (CLAIR): a locally fine-tuned LLM, (2) GPT4 with a simple prompt, and (3) GPT4 with a complex prompt. Five physicians rated them with the actual follow-ups written by healthcare providers on clarity, completeness, conciseness, and utility.
resultsFor five scenarios, our CLAIR model had the best performance. The GPT4 model received higher scores for utility and completeness but lower scores for clarity and conciseness. CLAIR generated follow-up questions with similar clarity and conciseness as the actual follow-ups written by healthcare providers, with higher utility than healthcare providers and GPT4, and lower completeness than GPT4, but better than healthcare providers.
conclusionLLMs can generate follow-up patient messages designed to clarify a medical question that compares favorably to those generated by 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.