Large Language Model-Based Responses to Patients' In-Basket Messages

• Published in: JAMA network open Vol. 7; no. 7; p. e2422399
• Main Authors: Small, William R, Wiesenfeld, Batia, Brandfield-Harvey, Beatrix, Jonassen, Zoe, Mandal, Soumik, Stevens, Elizabeth R, Major, Vincent J, Lostraglio, Erin, Szerencsy, Adam, Jones, Simon, Aphinyanaphongs, Yindalon, Johnson, Stephen B, Nov, Oded, Mann, Devin
• Format: Journal Article
• Language: English
• Published: United States American Medical Association 01.07.2024
• Subjects: Adult; Artificial Intelligence; Attitude of Health Personnel; Communication; Cross-Sectional Studies; Electronic Health Records; Empathy; Female; Health Informatics; Humans; Language; Male; Middle Aged; Online Only; Original Investigation; Physician-Patient Relations; Physicians, Primary Care - psychology; Quality Improvement
• ISSN: 2574-3805; 2574-3805
• DOI: 10.1001/jamanetworkopen.2024.22399

Virtual patient-physician communications have increased since 2020 and negatively impacted primary care physician (PCP) well-being. Generative artificial intelligence (GenAI) drafts of patient messages could potentially reduce health care professional (HCP) workload and improve communication quality, but only if the drafts are considered useful. To assess PCPs' perceptions of GenAI drafts and to examine linguistic characteristics associated with equity and perceived empathy. This cross-sectional quality improvement study tested the hypothesis that PCPs' ratings of GenAI drafts (created using the electronic health record [EHR] standard prompts) would be equivalent to HCP-generated responses on 3 dimensions. The study was conducted at NYU Langone Health using private patient-HCP communications at 3 internal medicine practices piloting GenAI. Randomly assigned patient messages coupled with either an HCP message or the draft GenAI response. PCPs rated responses' information content quality (eg, relevance), using a Likert scale, communication quality (eg, verbosity), using a Likert scale, and whether they would use the draft or start anew (usable vs unusable). Branching logic further probed for empathy, personalization, and professionalism of responses. Computational linguistics methods assessed content differences in HCP vs GenAI responses, focusing on equity and empathy. A total of 16 PCPs (8 [50.0%] female) reviewed 344 messages (175 GenAI drafted; 169 HCP drafted). Both GenAI and HCP responses were rated favorably. GenAI responses were rated higher for communication style than HCP responses (mean [SD], 3.70 [1.15] vs 3.38 [1.20]; P = .01, U = 12 568.5) but were similar to HCPs on information content (mean [SD], 3.53 [1.26] vs 3.41 [1.27]; P = .37; U = 13 981.0) and usable draft proportion (mean [SD], 0.69 [0.48] vs 0.65 [0.47], P = .49, t = -0.6842). Usable GenAI responses were considered more empathetic than usable HCP responses (32 of 86 [37.2%] vs 13 of 79 [16.5%]; difference, 125.5%), possibly attributable to more subjective (mean [SD], 0.54 [0.16] vs 0.31 [0.23]; P < .001; difference, 74.2%) and positive (mean [SD] polarity, 0.21 [0.14] vs 0.13 [0.25]; P = .02; difference, 61.5%) language; they were also numerically longer (mean [SD] word count, 90.5 [32.0] vs 65.4 [62.6]; difference, 38.4%), but the difference was not statistically significant (P = .07) and more linguistically complex (mean [SD] score, 125.2 [47.8] vs 95.4 [58.8]; P = .002; difference, 31.2%). In this cross-sectional study of PCP perceptions of an EHR-integrated GenAI chatbot, GenAI was found to communicate information better and with more empathy than HCPs, highlighting its potential to enhance patient-HCP communication. However, GenAI drafts were less readable than HCPs', a significant concern for patients with low health or English literacy.