Using Theory-Based Frameworks to Identify Barriers, Facilitators, Expectations, and Willingness to Pay for Online...

Online medical consultation (OMC) services have gained considerable attention as an integral component of telemedicine. Recently, AI has been increasingly integrated into OMC platforms, facilitating more efficient consultations and clinical decision-making. AI-driven OMC services can provide preliminary triage, medication guidance, and diagnostics for multiple medical conditions. Despite the availability and potential benefits of AI-driven OMC services, public acceptance and willingness to pay (WTP) for these services remain low.

This study aimed to identify perceived barriers, facilitators, expectations, and factors shaping public acceptance of and stated WTP for AI-driven OMC services.

We conducted semistructured qualitative interviews with patients, caregivers, and health care professionals to explore barriers, facilitators, expectations, and factors shaping public acceptance of and stated WTP for AI-driven OMC services. The study was informed by the theories of perceived risk and perceived benefit, which guided the development of the interview guide. All interviews were audio-recorded and transcribed verbatim. Data were analyzed using NVivo (version 15; Lumivero) with deductive thematic analysis guided by these theories. Coding was conducted independently and cross-checked by 2 researchers to ensure credibility and consistency.

Thematic analysis of 20 in-depth interviews identified 2 main themes and 11 subthemes. Perceived risks and perceived benefits emerged as 2 key perspectives influencing participants' acceptance and WTP. Psychological, governance, social, functional, health, and financial risks reduced acceptance, whereas convenience, diversity, reliability, efficiency, and educational benefits promoted it. Participants' self-reported WTP ranged from RMB 0 to RMB 200 (US $0-$27.28; RMB 1=US $0.1364 as of January 15, 2025), with participants who had prior experience with OMC generally reporting higher values than those without prior OMC experience.

This study identified facilitators and barriers influencing public acceptance of and WTP for AI-driven OMC services using theoretical constructs. Our findings offer valuable insights into the development and refinement of AI-driven OMC services, enabling more targeted pricing strategies and tailored services that address public preferences and concerns, as well as supporting the development of standardized regulatory governance for digital medical consultation platforms.