Currently submitted to: JMIR Human Factors
Date Submitted: Mar 28, 2026
Open Peer Review Period: Apr 10, 2026 - Jun 5, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Using Theory-Based Frameworks to Identify Barriers and Facilitators of Willingness to Pay for Online Medication Consultation Services in China: A Qualitative Study
ABSTRACT
Background:
Online medical consultation (OMC) services have gained considerable attention as integral components of telemedicine. Recently, artificial intelligence (AI) has been increasingly integrated into OMC platforms, facilitating enhanced consultation efficiency and clinical decision-making capabilities. Despite the availability and potential benefits of AI-driven OMC services, the public acceptance and willingness to pay (WTP) for AI-driven OMC services remain low.
Objective:
This study aimed to explore public perception of the AI-driven OMC services and to further evaluate the WTP for the service.
Methods:
We conducted semi-structured qualitative interviews with patients, caregivers, and healthcare professionals to explore factors influencing public acceptance and 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 15 with deductive thematic analysis guided by these theories, and coding was independently conducted and cross-checked by two researchers to ensure credibility and consistency.
Results:
Thematic analysis of 20 in-depth interviews identified 2 main themes and 11 subthemes. Perceived risks and perceived benefits emerged as two key perspectives influencing participants’ acceptance and WTP. Psychological, privacy, social, functional, health, and financial risks reduced acceptance, whereas convenience, diversity, reliability, efficiency, and educational benefits promoted it. Reported WTP ranged from $0 to $30, with service-experienced participants generally reporting higher values than service-naive participants.
Conclusions:
This study identified the facilitators and barriers influencing public acceptance and WTP for AI-driven OMC services with theoretical constructs. Our findings offer valuable insights for the development and refinement of AI-driven OMC services, enabling more targeted pricing strategies and tailored services that can address the preferences and concerns of the public.
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Copyright
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