JMIR Publications

ABSTRACT

Background:

Myocardial infarction (MI) remains a leading cause of morbidity and mortality worldwide. Although post-surgical 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, large language model artificial intelligence (LLM AI) tools have emerged as promising interventions to deliver personalized health information to post-MI patients.

Objective:

To explore the user experiences and perceptions of an LLM AI tool (iflyhealth) with integrated Personal Health Record (PHR) 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 non-users) who underwent MI surgery within the previous six months was recruited between July and August 2024. Data were collected through semi-structured, 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 non-adoption of the iflyhealth app. The interviews were audio-recorded, transcribed verbatim, and analyzed using Colaizzi’s 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) non-users and those who discontinued use primarily cited complexity of the interface and perceived limited relevance of the advice as major deterrents. The user group was slightly younger (mean age 49.3 ± 12.2 years) compared to non-users (mean age 53.7 ± 12.5 years), with users more likely to have higher education levels and employment.

Conclusions:

LLM AI tools combined with PHR functionality show significant potential in assisting post-MI patients. The main benefits reported by users of iflyhealth 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 these barriers can further enhance the use of such AI tools, which can play a crucial role in patient-centered, personalized post-MI management as well as in the management of other chronic diseases.