%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e68762
%T Harnessing an Artificial Intelligence–Based Large Language Model With Personal Health Record Capability for Personalized Information Support in Postsurgery Myocardial Infarction: Descriptive Qualitative Study
%A Yang,Ting-ting
%A Zheng,Hong-xia
%A Cao,Sha
%A Jing,Mei-ling
%A Hu,Ju
%A Zuo,Yan
%A Chen,Qing-yong
%A Zhang,Jian-jun
%+ , Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, #20 3rd Section, Renmin Nan Road, Chengdu, 610041, China, 86 13348886426, zhangjianjun-1983@163.com
%K myocardial infarction
%K post-surgery recovery
%K personalized health support
%K artificial intelligence
%K large language model
%K personal health record
%K digital health tools
%K health information accessibility
%K qualitative study
%K mobile phone
%D 2025
%7 30.4.2025
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: Myocardial infarction (MI) remains a leading cause of morbidity and mortality worldwide. Although postsurgical cardiac interventions have improved survival rates, effective management during recovery remains challenging. Traditional informational support systems often provide generic guidance that does not account for individualized medical histories or psychosocial factors. Recently, artificial intelligence (AI)–based large language models (LLM) tools have emerged as promising interventions to deliver personalized health information to post-MI patients. Objective: We aim to explore the user experiences and perceptions of an AI-based LLM tool (iflyhealth) with integrated personal health record functionality in post-MI care, assess how patients and their family members engaged with the tool during recovery, identify the perceived benefits and challenges of using the technology, and to understand the factors promoting or hindering continued use. Methods: A purposive sample of 20 participants (12 users and 8 nonusers) who underwent MI surgery within the previous 6 months was recruited between July and August 2024. Data were collected through semistructured, face-to-face interviews conducted in a private setting, using an interview guide to address participants’ first impressions, usage patterns, and reasons for adoption or nonadoption of the iflyhealth app. The interviews were audio-recorded, transcribed verbatim, and analyzed using Colaizzi method. Results: Four key themes revealed included: (1) participants’ experiences varied based on digital literacy, prior exposure to health technologies, and individual recovery needs; (2) users appreciated the app’s enhanced accessibility to professional health information, personalized advice tailored to their clinical conditions, and the tool’s responsiveness to health status changes; (3) challenges such as difficulties with digital literacy, usability concerns, and data privacy issues were significant barriers; and (4) nonusers and those who discontinued use primarily cited complexity of the interface and perceived limited relevance of the advice as major deterrents. Conclusions: iflyhealth, an LLM AI app with a built-in personal health record functionality, shows significant potential in assisting post-MI patients. The main benefits reported by iflyhealth users include improved access to personalized health information and an enhanced ability to respond to changing health conditions. However, challenges such as digital literacy, usability, and privacy and security concerns persist. Overcoming the barriers may further enhance the use of the iflyhealth app, which can play an important role in patient-centered, personalized post-MI management. 
%M 40305084
%R 10.2196/68762
%U https://www.jmir.org/2025/1/e68762
%U https://doi.org/10.2196/68762
%U http://www.ncbi.nlm.nih.gov/pubmed/40305084