Abstract
AI-powered chatbots hold promise for enhancing chronic disease self-management in primary care; however, their real-world implementation often reveals hidden socio technical complexities. This paper presents a reflective case study of a chatbot designed to support diabetes self-care, deployed in a community-based clinic in rural Taiwan. Despite initial enthusiasm from stakeholders during co-design, the two-month field implementation yielded minimal patient engagement. Drawing on the Technology–Organization–Environment (TOE) framework, we identify critical barriers across three domains. Technologically, the lack of robust digital infrastructure required manual data entry, undermining the scal ability of personalization. Organization ally, integration with existing clinical workflows imposed additional burdens on healthcare staff. Environmentally, high patient-provider trust and readily accessible in-person care reduced the chatbot’s perceived value. These findings underscore the importance of aligning AI health tools with infra structural readiness, workflow dynamics, and contextual needs. Our work contributes design and implementation insights for human- centered AI in healthcare, emphasizing that success depends not only on technological efficacy but also on socio technical fit.
Keywords
AI Chatbots; Sociotechnical Implementation; TOE framework; Diabetes Self-Management
DOI
https://doi.org/10.21606/iasdr.2025.320
Citation
Wang, C., University, T.H.,and Tseng, Y.(2025) When Clinics Meet Chatbots: Sociotechnical Reflections on AI Implementation in Primary Care, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.320
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 4 - Human-Centered AI
When Clinics Meet Chatbots: Sociotechnical Reflections on AI Implementation in Primary Care
AI-powered chatbots hold promise for enhancing chronic disease self-management in primary care; however, their real-world implementation often reveals hidden socio technical complexities. This paper presents a reflective case study of a chatbot designed to support diabetes self-care, deployed in a community-based clinic in rural Taiwan. Despite initial enthusiasm from stakeholders during co-design, the two-month field implementation yielded minimal patient engagement. Drawing on the Technology–Organization–Environment (TOE) framework, we identify critical barriers across three domains. Technologically, the lack of robust digital infrastructure required manual data entry, undermining the scal ability of personalization. Organization ally, integration with existing clinical workflows imposed additional burdens on healthcare staff. Environmentally, high patient-provider trust and readily accessible in-person care reduced the chatbot’s perceived value. These findings underscore the importance of aligning AI health tools with infra structural readiness, workflow dynamics, and contextual needs. Our work contributes design and implementation insights for human- centered AI in healthcare, emphasizing that success depends not only on technological efficacy but also on socio technical fit.