Willingness to Use and Pay for Digital Health Care Services According to 4 Scenarios: Results from a National Survey

• Published in: JMIR mHealth and uHealth Vol. 11; p. e40834
• Main Authors: Lee, Junbok, Oh, Yumi, Kim, Meelim, Cho, Belong, Shin, Jaeyong
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 29.03.2023
• Subjects: Chronic illnesses; Delivery of Health Care; FDA approval; Health care; Health Facilities; Health Services; Humans; Intervention; Original Paper; Personal health; Surveys and Questionnaires; South Korea; United Kingdom > UK; United States > US; Germany; willingness to pay; digital health; health technology; digital health intervention; willingness to use; service experience
• ISSN: 2291-5222; 2291-5222
• DOI: 10.2196/40834

Smartphones and their associated technology have evolved to an extent where these devices can be used to provide digital health interventions. However, few studies have been conducted on the willingness to use (WTU) and willingness to pay (WTP) for digital health interventions. The purpose of this study was to investigate how previous service experience, the content of the services, and individuals' health status affect WTU and WTP. We conducted a nationwide web-based survey in 3 groups: nonusers (n=506), public service users (n=368), and private service users (n=266). Participants read scenarios about an imagined health status (such as having a chronic illness) and the use of digital health intervention models (self-management, expert management, and medical management). They were then asked to respond to questions on WTU and WTP. Public service users had a greater intention to use digital health intervention services than nonusers and private service users: scenario A (health-risk situation and self-management), nonusers=odd ratio [OR] .239 (SE .076; P<.001) and private service users=OR .138 (SE .044; P<.001); scenario B (health-risk situation and expert management), nonusers=OR .175 (SE .040; P<.001) and private service users=OR .219 (SE .053; P<.001); scenario C (chronic disease situation and expert management), nonusers=OR .413 (SE .094; P<.001) and private service users=OR .401 (SE .098; P<.001); and scenario D (chronic disease situation and medical management), nonusers=OR .480 (SE .120; P=.003) and private service users=OR .345 (SE .089; P<.001). In terms of WTP, in scenarios A and B, those who used the public and private services had a higher WTP than those who did not (scenario A: β=-.397, SE .091; P<.001; scenario B: β=-.486, SE .098; P<.001). In scenario C, private service users had greater WTP than public service users (β=.264, SE .114; P=.02), whereas public service users had greater WTP than nonusers (β=-.336, SE .096; P<.001). In scenario D, private service users were more WTP for the service than nonusers (β=-.286, SE .092; P=.002). We confirmed that the WTU and WTP for digital health interventions differed based on individuals' prior experience with health care services, health status, and demographics. Recently, many discussions have been made to expand digital health care beyond the early adapters and fully into people's daily lives. Thus, more understanding of people's awareness and acceptance of digital health care is needed.