Trivedi Ritu, Laranjo Liliana, Marschner Simone, Thiagalingam Aravinda, Thomas Stuart, Kumar Saurabh, Shaw Tim, Chow Clara K
Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Level 5, Block K, Westmead Hospital, Hawkesbury Road, Westmead, 2145, Australia, 61 2 8890 3125.
Cardiology Department, Westmead Hospital, Westmead, Australia.
JMIR Cardio. 2025 Aug 19;9:e64326. doi: 10.2196/64326.
Patient education and self-management support are critical for atrial fibrillation (AF) management. Conversational artificial intelligence (AI) has the potential to provide interactive and personalized support, but has not been evaluated in patients with AF.
This study aimed to evaluate the feasibility of a conversational AI intervention to support patients with AF postdischarge.
This was a single-blinded, 4:1-parallel-randomized controlled trial with process evaluation of feasibility and engagement. The primary outcome was the change in Atrial Fibrillation Effect on Quality-of-Life (AFEQT) questionnaire total score between groups. Patients with AF (18 y and older) were recruited postdischarge from Westmead Hospital cardiology services and randomized to receive either the intervention or usual care. The 6-month intervention consisted of fully automated conversational AI phone calls (with speech recognition and natural language processing) that regularly assessed patient health and symptoms and provided self-management support and education. These phone calls were supplemented with an online survey (sent via text message or email) containing replicated call content when participants could not be reached after 3 call attempts. If participant responses were concerning (eg, poor overall health, low medication confidence, and high symptom burden), they would be followed up with an ad hoc phone call and directed to clinical care if required. A semipersonalized education website was also available as part of the intervention, and participants were encouraged weekly (nudges delivered via text messages or emails) to visit it.
A total of 103 patients (mean age, 63.7 y, SD 11.2 y; n=72, 70% male) were randomized (82 to the intervention); the target sample size was 385. The difference in the AFEQT total score was nonsignificant (adjusted mean difference 2.08, 95% CI -7.79 to 11.96; P=.46). An exploratory prepost comparison revealed an improvement in total AFEQT score in the intervention group only (baseline: 69.9, 95% CI 64.4 to 75.5; 6 months: 79.9, 95% CI 74.9 to 84.8; P=.01). Participants completed 4 of 7 outreaches on average, and 88.4% (304/344) of completed outreaches were reported as useful.
This proof-of-concept study demonstrates the feasibility of conversational AI in supporting patients with chronic conditions postdischarge. Intervention participants had improvement in their atrial fibrillation quality of life, though the forced shortening of the evaluation was unable to demonstrate a significant difference between groups.
患者教育和自我管理支持对于房颤（AF）管理至关重要。对话式人工智能（AI）有潜力提供交互式和个性化支持，但尚未在房颤患者中进行评估。
本研究旨在评估一种对话式AI干预措施对房颤患者出院后提供支持的可行性。
这是一项单盲、4:1平行随机对照试验，对可行性和参与度进行过程评估。主要结局是两组之间房颤对生活质量影响（AFEQT）问卷总分的变化。房颤患者（18岁及以上）从韦斯特米德医院心脏病科出院后被招募，并随机分为接受干预或常规护理。为期6个月的干预包括全自动对话式AI电话（具备语音识别和自然语言处理功能），定期评估患者健康状况和症状，并提供自我管理支持和教育。当经过3次电话尝试仍无法联系到参与者时，这些电话会辅以包含重复通话内容的在线调查（通过短信或电子邮件发送）。如果参与者的回答令人担忧（例如，整体健康状况不佳、用药信心低和症状负担重），将通过临时电话进行跟进，并在需要时引导至临床护理。作为干预措施的一部分，还提供了一个半个性化教育网站，鼓励参与者每周（通过短信或电子邮件推送）访问该网站。
共有103名患者（平均年龄63.7岁，标准差11.2岁；n = 72，70%为男性）被随机分组（82人接受干预）；目标样本量为385。AFEQT总分差异无统计学意义（调整后平均差异2.08，95%置信区间-7.79至11.96；P = 0.46）。一项探索性的前后比较显示，仅干预组的AFEQT总分有所改善（基线：69.9，95%置信区间64.4至75.5；6个月：79.9，95%置信区间74.9至84.8；P = 0.01）。参与者平均完成了7次 outreach中的4次，88.4%（304/344）完成的outreach被报告为有用。
这项概念验证研究证明了对话式AI在支持慢性病患者出院后的可行性。干预参与者的房颤生活质量有所改善，尽管评估被迫缩短，未能显示出组间的显著差异。
