Key result
Conversational AI provides engaging AFib education and support but requires improved dialogue flexibility.
Why the study?
The acceptability, barriers, and enablers of conversational AI to support patients with AF were unknown.
What are the patient perspectives, acceptability, barriers, and enablers of using conversational AI for atrial fibrillation self-management?
What are the patient perspectives, acceptability, barriers, and enablers of using conversational AI for atrial fibrillation self-management?
Conversational AI is an engaging tool for atrial fibrillation self-management support, but requires improvements in natural dialogue flexibility and personalization to optimize the patient experience.
May support conversational AI for AF education; hypothesis-generating for dialogue refinements in larger trials.
BACKGROUND Conversational artificial intelligence (AI) allows for engaging interactions, however, its acceptability, barriers, and enablers to support patients with atrial fibrillation (AF) are unknown. OBJECTIVE This work stems from the Coordinating Health care with AI–supported Technology for patients with AF (CHAT-AF) trial and aims to explore patient perspectives on receiving support from a conversational AI support program. METHODS Patients with AF recruited for a randomized controlled trial who received the intervention were approached for semistructured interviews using purposive sampling. The 6-month intervention consisted of fully automated conversational AI phone calls (with speech recognition and natural language processing) that assessed patient health and provided self-management support and education. Interviews were recorded, transcribed, and thematically analyzed. RESULTS We conducted 30 interviews (mean age 65.4, SD 11.9 years; 21/30, 70% male). Four themes were identified: (1) interaction with a voice-based conversational AI program (human-like interactions, restriction to prespecified responses, trustworthiness of hospital-delivered conversational AI); (2) engagement is influenced by the personalization of content, delivery mode, and frequency (tailoring to own health context, interest in novel information regarding health, overwhelmed with large volumes of information, flexibility provided by multichannel delivery); (3) improving access to AF care and information (continuity in support, enhancing access to health-related information); (4) empowering patients to better self-manage their AF (encouraging healthy habits through frequent reminders, reassurance from rhythm-monitoring devices). CONCLUSIONS Although conversational AI was described as an engaging way to receive education and self-management support, improvements such as enhanced dialogue flexibility to allow for more naturally flowing conversations and tailoring to patient health context were also mentioned. CLINICALTRIAL Australian New Zealand Clinical Trials Registry ACTRN12621000174886; https://tinyurl.com/3nn7tk72 INTERNATIONAL REGISTERED REPORT RR2-10.2196/34470
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Trivedi et al. (2024) studied Atrial fibrillation (n=30). Conversational AI support program was evaluated on Patient perspectives and themes regarding the conversational AI support program. Among 30 interviewed patients with atrial fibrillation, conversational AI was perceived as an engaging way to receive education and self-management support, though dialogue flexibility needs improvement.
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