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Sociotechnical Misalignments in Hospital AI System Implementation: Qualitative Case Study
Adrian Yeow1, Jennifer Cleland2,3, Christina Soh4
1School of Business, Singapore University of Social Sciences, 463 Clementi Road, Singapore, Singapore, 65 62480221.
Journal of Medical Internet Research
|August 10, 2026
Summary
Sociotechnical misalignments hindered AI chatbot adoption in a hospital, leading to its discontinuation. Addressing these challenges is crucial for successful AI implementation in healthcare settings.
Area of Science:
- Healthcare Informatics
- Artificial Intelligence in Medicine
- Sociotechnical Systems Theory
Background:
- Implementing artificial intelligence (AI) in healthcare presents significant challenges, particularly in integrating AI tools into existing clinical knowledge, practices, and procedures.
- Understanding the contextual embedding of AI is critical for successful implementation, resource planning, and maximizing the impact of AI tools.
Purpose of the Study:
- To examine the contextual challenges of implementing an AI chatbot (ChatAI) in a tertiary government hospital.
- To analyze the impact of sociotechnical factors on the sustained use and adoption of the AI chatbot.
Main Methods:
- An instrumental case study approach was employed, utilizing interviews and meeting minutes.
- 16 semistructured interviews were conducted with the implementation team and hospital staff.
- Sociotechnical Systems (STS) theory, specifically Davis et al.'s (2014) framework, was adopted to analyze the implementation and use of ChatAI.
Main Results:
- Misalignments in sociotechnical elements (goals, people, processes, technology, infrastructure) limited user adoption and sustainability of ChatAI.
- Contextual changes, including new regulatory mandates and evolving stakeholder practices, introduced further misalignments, leading to the chatbot's discontinuation.
- Initial attempts by the innovation center team to address misalignments were insufficient against ongoing contextual shifts.
Conclusions:
- Sociotechnical misalignments can significantly undermine the sustained use and success of large-scale AI implementations in healthcare.
- Future AI implementations require careful alignment of sociotechnical dimensions: goals, people, processes, technology, and infrastructure.
- Aligning evolving infrastructure and regulatory requirements presents a particularly challenging aspect for AI sustainability in healthcare settings.
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