{"doi":"10.1093/jamiaopen/ooae028","title":"Generative artificial intelligence responses to patient messages in the electronic health record: early lessons learned","abstract":"Background: Electronic health record (EHR)-based patient messages can contribute to burnout. Messages with a negative tone are particularly challenging to address. In this perspective, we describe our initial evaluation of large language model (LLM)-generated responses to negative EHR patient messages and contend that using LLMs to generate initial drafts may be feasible, although refinement will be needed. Methods: = 50) of negative patient messages was extracted from a health system EHR, de-identified, and inputted into an LLM (ChatGPT). Qualitative analyses were conducted to compare LLM responses to actual care team responses. Results: Some LLM-generated draft responses varied from human responses in relational connection, informational content, and recommendations for next steps. Occasionally, the LLM draft responses could have potentially escalated emotionally charged conversations. Conclusion: Further work is needed to optimize the use of LLMs for responding to negative patient messages in the EHR.","journal":"JAMIA Open","year":2024,"id":418795,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":46,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9565,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":74541,"name":"Chris Longhurst","orcid":"0000-0003-4908-6856","position":1,"is_corresponding":false},{"id":227850,"name":"Marlene Millen","orcid":"0000-0002-2671-5755","position":2,"is_corresponding":false},{"id":513498,"name":"Amy M. Sitapati","orcid":"0000-0002-5737-5925","position":3,"is_corresponding":false},{"id":432340,"name":"Ming Tai‐Seale","orcid":"0000-0001-9272-066X","position":4,"is_corresponding":false},{"id":349921,"name":"Sally L. Baxter","orcid":"0000-0002-5271-7690","position":0,"is_corresponding":true}],"reference_count":22,"raw_metadata":null,"created_at":"2026-07-19T01:57:09.067930Z","pmid":"38601475","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}