与杜西模式原型地区远程医疗服务接受度相关的因素。
Factors associated with the acceptance of telemedicine services in Dusit model prototype area.
作者信息
Benjakul Nontawat, Wongsin Utoomporn, Siri Sukhontha, Prutipinyo Chardsumon
机构信息
M.Sc. (Public Health Administration), Faculty of Public Health, Mahidol University, Bangkok, 10400, Thailand.
Department of Anatomical Pathology, Faculty of Medicine Vajira Hospital, Navamindradhiraj University, Bangkok, 10300, Thailand.
出版信息
Sci Rep. 2025 Jul 13;15(1):25311. doi: 10.1038/s41598-025-11193-6.
The Dusit Model, a prototype area in Bangkok, Thailand, integrates telemedicine into primary and tertiary care to reduce overcrowding and promote equitable access. This study aimed to identify factors influencing telemedicine acceptance using an extended Technology Acceptance Model (TAM). A cross-sectional study was conducted among 389 participants using Vajira@Home. Variables included demographics, the extended Technology Acceptance Model (TAM), and telemedicine acceptance. Descriptive statistics, chi-square tests, and multivariate binary logistic regression were employed. Telemedicine acceptance was significantly correlated with generation, perceived ease of use (adjusted OR = 3.95, p = 0.047), and facilitating conditions (adjusted OR = 5.78, p = 0.013). Compared to Generation Z, Baby Boomers and Generation X had lower odds of acceptance (OR = 0.01 and 0.22, respectively). Model fit was confirmed (AUC = 0.79; Hosmer-Lemeshow p > 0.05). Generation, usability perceptions, and infrastructure support critically influence telemedicine acceptance. Policy should prioritize digital literacy for older adults, improve user experience, and invest in infrastructure to enhance equitable adoption.
泰国曼谷的一个样板地区——杜西模型，将远程医疗整合到初级和三级医疗中，以减少过度拥挤并促进公平就医。本研究旨在使用扩展技术接受模型（TAM）确定影响远程医疗接受度的因素。对389名使用Vajira@Home的参与者进行了一项横断面研究。变量包括人口统计学、扩展技术接受模型（TAM）和远程医疗接受度。采用描述性统计、卡方检验和多变量二元逻辑回归分析。远程医疗接受度与代际、感知易用性（调整后的OR = 3.95，p = 0.047）和促进条件（调整后的OR = 5.78，p = 0.013）显著相关。与Z世代相比，婴儿潮一代和X世代接受远程医疗的几率较低（OR分别为0.01和0.22）。模型拟合得到确认（AUC = 0.79；Hosmer-Lemeshow p > 0.05）。代际、可用性认知和基础设施支持对远程医疗接受度有至关重要的影响。政策应优先考虑提高老年人的数字素养，改善用户体验，并投资于基础设施建设，以促进公平采用。
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