A sociotechnical systems framework for the application of artificial intelligence in health care delivery
Open accessAbstract
In the coming years, artificial intelligence (AI) will pervade almost every aspect of the health care delivery system. AI has the potential to improve patient safety (e.g., diagnostic accuracy) as well as reduce the burden on clinicians (e.g., documentation-related workload); however, these benefits are yet to be realized. AI is only one element of a larger sociotechnical system that needs to be considered for effective AI application. In this paper, we describe the current challenges of integrating AI into clinical care and propose a sociotechnical systems (STS) approach for AI design and implementation. We demonstrate the importance of an STS approach through a case study on the design and implementation of a clinical decision support (CDS). In order for AI to reach its potential, the entire work system as well as clinical workflow must be systematically considered throughout the design of AI technology.
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The framework treats the clinician-AI diagnostic interaction as a system in which computational tools, institutional workflows, regulatory requirements, and human reasoning are mutually constitutive.
“At the sociotechnical level, the framework treats the clinician-AI diagnostic interaction as a system in which computational tools, institutional workflows, regulatory requirements, and human reasoning are mutually constitutive”
From A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification (2026) · cites this paper as Salwei and Carayon, 2022
The ‘Sociotechnical systems and the work system model’ section (page unavailable) says social and technical systems are interrelated and cannot be decoupled; the ‘AI as a part of the broader work system’ section describes interactions among tools, people, workflow, and organizational context. The paper mentions oversight and regulation as challenges in its Introduction (page unavailable), but does not identify regulatory requirements as a mutually interacting element of its framework. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIntegrating AI in health care settings requires a nuanced approach that considers both technical performance and sociotechnical factors, including the social aspects of its application alongside technical considerations.
“The integration of artificial intelligence (AI) in health care settings demands a nuanced approach that considers both technical performance and sociotechnical factors.”
From Checklist Approach to Developing and Implementing AI in Clinical Settings: Instrument Development Study (2025) · cites this paper as [1]
An AI can only integrate into clinical workflows if it fits within the context, or work system, in which it is implemented.
“Their model demonstrates that an AI can only integrate into clinical workflows if it fits within the context, or the work system, in which it is implemented.”
From Checklist Approach to Developing and Implementing AI in Clinical Settings: Instrument Development Study (2025) · cites this paper as [1]
Salwei and Carayon developed a sociotechnical systems framework for AI that acknowledges the social and technical aspects of work related to successful design and implementation of AI.
“Salwei and Carayon [1] developed a sociotechnical systems framework for AI that acknowledges the social and technical aspects of work that relate to the successful design and implementation of AI.”
From Checklist Approach to Developing and Implementing AI in Clinical Settings: Instrument Development Study (2025) · cites this paper as [1]
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A Theoretical Perspective on AI's Transformative Role in the Health Insurance Sector
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Operationalizing Responsible AI in Health Systems: Delphi Based Governance Metrics for Indonesia
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Human-AI Collaboration in Healthcare: A Review of User Perspectives and Challenges
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Lessons for Approaching Implementation of AI Systems in Clinical Settings
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Self-citationBridging the Gap: Consensus-Based Considerations for AI Usefulness in Healthcare
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“At the sociotechnical level, the framework treats the clinician-AI diagnostic interaction as a system in which computational tools, institutional workflows, regulatory requirements, and human reasoning are mutually constitutive (Salwei and Carayon, 2022).”
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- Authors
- Salwei, M. E., & Carayon, P.
- Affiliations
- Vanderbilt University Medical Center (US)
- University of Wisconsin–Madison (US)
- Corresponding author
- Salwei, Megan E.
- Venue
- Journal of Cognitive Engineering and Decision Making
- Publisher
- SAGE Publications
- Published
- 2022-05-11
- Volume
- 16
- Issue
- 4
- Pages
- 194-206
- ISSN
- 1555-3434 (ISSN-L)
- PMC
- PMC9873227
- OpenAlex
- W4280579451
- Cited by
- 112
- References
- 56
- Citations by year
- 2026: 58 · 2025: 29 · 2024: 18 · 2023: 5 · 2022: 2
- FWCI
- 4.07
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Cognitive Engineering and Decision Making (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Center for Advancing Translational Sciences (1UL1TR002373)
- Agency for Healthcare Research and Quality (K12HS026395, R01HS022086)
- U.S. National Library of Medicine (T15LM007450-19)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
- Keywords
- artificial intelligence in healthcare, socio-technical systems, clinical decision support, clinical workflow, patient safety, clinician workload, work system design, AI implementation
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