Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework
Open accessAbstract
Objectives: This study used the Technology-Organization-Environment (TOE) framework to identify the factors involved in the decisions made by integrated medical and healthcare organizations to adopt artificial intelligence (AI) elderly care service resources. Method: This study identified the Decision-making Trial and Evaluation Laboratory-Interpretive Structural Modeling (DEMATEL-ISM) method was used to construct a multilayer recursive structural model and to analyze the interrelationships between the levels. A MICMAC quadrant diagram was used for a cluster analysis. Results: The ISM recursive structural model was divided into a total of seven layers. The bottom layer contained the four factors of High risk of data leakage (T1), Lack of awareness of the value and benefits of AI healthcare technology (T5), Lack of management leadership support (O1), and Government policies (E1). Having a low dependency but high driving force, these factors are the root causes of adoption by healthcare organizations. The topmost layer contained the most direct factors, which had a high dependency but the low driving force, influencing adoption: Competitive pressures (E2), Lack of patient trust (E5), and Lack of excellent partnerships (E7). Healthcare organizations are more concerned with technology and their environments when deciding to adopt intelligent healthcare resources. Conclusion: The combination of the three methods of DEMATEL-ISM-MICMAC construction models provides new ideas for smart healthcare services for hospitals. The DEMATEL method favors the construction dimension of the micro-model, while the ISM method favors the construction dimension of the macro-model. Combining these two methods may reduce the loss of information within the system, simplify the matrix calculation workload, and improve the efficiency of operations while decomposing the complex problems into several sub-problems in a more comprehensive and detailed way. Conducting cluster analysis of the adoption determinants utilizing MICMAC quadrant diagrams may provide strong methodological guidance and decision-making recommendations for government departments, senior decision-makers in healthcare organizations, and policy-makers in associations in the senior care industry.
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Data security was identified as a major contributor toward hesitancy, with concerns around cybersecurity relating to both training and testing data as well as fear of trackers and spyware obtaining unsolicited data.
“Data security was identified as a major contributor toward hesitancy, with concerns around cybersecurity relating to both training and testing data as well as fear of trackers and spyware obtaining unsolicited data [39,41,42,45,48,77].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [77]
The Methodology’s ‘Multilayer recursive structural analysis’ section identifies data-leakage risk, including theft of sensitive medical data by hackers, as an important factor in medical institutions’ adoption decisions (page not available). It does not discuss cybersecurity of training or testing data, or trackers and spyware obtaining unsolicited data. (LLM, conf=0.87)
Experimental — accuracy not yet measuredLack of regulation and policies from the government, including uncertainty around legal direction or law, is a barrier to the application of AI technologies.
“A lack of regulation and policies from the government, including uncertainty around legal direction or law, was presented as a barrier to the application of AI technologies [37,45,57,60,77,78].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [77]
The Methodology’s “Multilayer recursive structural analysis” section says that poorly implemented government policies hinder healthcare institutions’ acquisition of AI medical service resources. It does not identify uncertainty about legal direction or law as that barrier; its discussion of legal disputes concerns unclear ownership in joint ventures. Page numbers are not available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe TOE framework emphasizes identifying facilitators and barriers to the adoption of a specific technology rather than providing guidance on governing the broader range of technologies implemented within organizations.
“The framework emphasizes identifying facilitators and barriers to the adoption of a specific technology, rather than providing guidance on governing the broader range of technologies implemented within organizations23–26.”
From Establishing organizational AI governance in healthcare: a case study in Canada (2025) · cites this paper as [23]
The Introduction describes using TOE to identify factors influencing the adoption of AI healthcare resources, supporting the claim about adoption facilitators and barriers. It does not say that TOE lacks guidance for governing a broader range of technologies; the limitations it names concern analyzing relationships among adoption factors. No page numbers are available in our copy. (LLM, conf=0.84)
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References 78 · Cited by 81
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- Authors
- Yang, J., Luo, B., Zhao, C., & Zhang, H.
- Affiliations
- Hefei University of Technology (CN)
- School of Transportation Technology, Fujian University, Fuzhou, People’s Republic of China
- Anhui University of Technology (CN)
- Corresponding author
- Luo, Biao
- Venue
- DIGITAL HEALTH
- Publisher
- SAGE Publications
- Published
- 2022-01
- Volume
- 8
- Pages
- 205520762211260
- ISSN
- 2055-2076 (ISSN-L)
- PMC
- PMC9537501
- OpenAlex
- W4304116294
- Cited by
- 81
- References
- 78
- Citations by year
- 2026: 21 · 2025: 24 · 2024: 23 · 2023: 13
- FWCI
- 12.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Digital Health (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (72188101)
- the Influence Mechanism of Social Network Structures on the Diffusion of Green Consumption Behavior (21YJA630066)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Keywords
- artificial intelligence adoption, healthcare organizations, elderly care services, technology-organization-environment framework, adoption factors, data leakage risk, public policy, patient trust, competitive pressure, health partnerships, smart healthcare
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