ArticleJournal of medical Internet research2025
Investigating Clinicians' Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Determinants of Health Care Technology Adoption Using an Integrated Unified Theory of Acceptance and Use of Technology and Task Technology Fit Model: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Task-Technology Fit of Artificial Intelligence-based clinical decision support systems: a review of qualitative studies.BMC medical informatics and decision making · 2025Pooled it
- A Hierarchical Machine Learning-Based Framework for Clinical Decision Support in Foot Orthosis Prescription: Algorithm Development and Validation Study.JMIR medical informatics · 2026Observational
- Integrating Artificial Intelligence into Orthopedic Practice: Modeling Attitudes and Intentions to Use AI.Healthcare (Basel, Switzerland) · 2026Article
- AI in Clinical Decision Support Systems: Promising Applications and Strategies for Managing Data Challenges.Journal of medical Internet research · 2026Article
- Determinants of healthcare professionals' acceptance of DeepSeek-assisted clinical decision support applications: a cross-sectional study.BMC medical informatics and decision making · 2026Article
- Development and Application of a Unplanned Extubation Assessment and Clinical Decision Support Information System.Risk management and healthcare policy · 2026Article
- Disease Activity Assessment Frequency in Rheumatoid Arthritis: A Retrospective Observational Study of the Medical Support System for Rheumatoid Arthritis System Implementation.JMIR formative research · 2025Observational
- Identifying Measurement Dimensions of Users' Benefit-Risk Perceptions of AI in Healthcare: A Scoping Review.Inquiry : a journal of medical care organization, provision and financingArticle
- Factors shaping the adoption of large language models among hospital administrative staff: A cross-sectional survey study.Digital healthArticle
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Authors and funding
7 authors.
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Abstract
backgroundAn intelligence-enabled clinical decision support system (CDSS) is a computerized system that integrates medical knowledge, patient data, and clinical guidelines to assist health care providers make clinical decisions. Research studies have shown that CDSS utilization rates have not met expectations. Clinicians' intentions and their attitudes determine the use and promotion of CDSS in clinical practice.
objectiveThe aim of this study was to enhance the successful utilization of CDSS by analyzing the pivotal factors that influence clinicians' intentions to adopt it and by putting forward targeted management recommendations.
methodsThis study proposed a research model grounded in the task-technology fit model and the technology acceptance model, which was then tested through a cross-sectional survey. The measurement instrument comprised demographic characteristics, multi-item scales, and an open-ended query regarding areas where clinicians perceived the system required improvement. We leveraged structural equation modeling to assess the direct and indirect effects of "task-technology fit" and "perceived ease of use" on clinicians' intentions to use the CDSS when mediated by "performance expectation" and "perceived risk." We collated and analyzed the responses to the open-ended question.
resultsWe collected a total of 247 questionnaires. The model explained 65.8% of the variance in use intention. Performance expectations (β=0.228; P<.001) and perceived risk (β=-0.579; P<.001) were both significant predictors of use intention. Task-technology fit (β=-0.281; P<.001) and perceived ease of use (β=-0.377; P<.001) negatively affected perceived risk. Perceived risk (β=-0.308; P<.001) negatively affected performance expectations. Task-technology fit positively affected perceived ease of use (β=0.692; P<.001) and performance expectations (β=0.508; P<.001). Task characteristics (β=0.168; P<.001) and technology characteristics (β=0.749; P<.001) positively affected task-technology fit. Contrary to expectations, perceived ease of use (β=0.108; P=.07) did not have a significant impact on use intention. From the open-ended question, 3 main themes emerged regarding clinicians' perceived deficiencies in CDSS: system security risks, personalized interaction, seamless integration.
conclusionsPerceived risk and performance expectations were direct determinants of clinicians' adoption of CDSS, significantly influenced by task-technology fit and perceived ease of use. In the future, increasing transparency within CDSS and fostering trust between clinicians and technology should be prioritized. Furthermore, focusing on personalized interactions and ensuring seamless integration into clinical workflows are crucial steps moving forward.
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