ArticleBMJ health & care informatics2025
Using technology acceptance model to explore physicians' perspectives of clinical decision support system alerts.
Article in BMJ health & care informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Patterns of CDSS adoption in primary care: a cluster analysis and predictive modelling study from a stepped wedge trial.Implementation science : IS · 2026Trial
- Integrating Artificial Intelligence into Orthopedic Practice: Modeling Attitudes and Intentions to Use AI.Healthcare (Basel, Switzerland) · 2026Article
- Mapping Practice-Based Signals of Generative AI in Psychiatric Care: Qualitative Study of Korean Psychiatrists' Experiences, Interpretations, and Implementation Priorities.Journal of medical Internet research · 2026Article
- Research on the implementation path of digital-intelligent healthcare based on the TAM model from the perspective of high-quality development.BMC health services research · 2026Article
- Experiences of young adults with cardiovascular disease in using digital health: a protocol for a qualitative systematic review and meta-synthesis from the perspective of media affordances.Frontiers in cardiovascular medicine · 2026Article
- Factors Affecting the Receptiveness of Chinese Internists and Surgeons Toward Artificial Intelligence-Driven Drug Prescription: Protocol for a Systematic Survey Study.JMIR research protocols · 2025Article
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7 authors.
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Abstract
objectiveTo examine factors influencing physicians' perspectives of clinical decision support system (CDSS) alerts using the Technology Acceptance Model (TAM), focusing on perceived ease of use (PEOU), perceived usefulness (PU), attitude towards usage (AT), user satisfaction (US) and behavioural intention to use (BI).
methodsThis study was conducted in the outpatient departments of a single academic medical centre in northern Taiwan, involving 72 physicians who completed a structured TAM-based questionnaire. Seven physician's characteristics (age, clinical experience, CDSS operating status, patient volume, consultation frequency, gender and specialty) were analysed for their influence on PEOU and PU. Multiple regression analysis assessed relationships among TAM constructs and external factors.
resultsPatient volume and age negatively affected PU and PEOU (eg, age vs PU: β=-2.38, p<0.05; patient volume vs PEOU: β=-2.64, p<0.01), while clinical experience positively influenced them (PEOU: β=2.11, p<0.05). TAM construct analysis revealed that PEOU positively influenced PU (β=0.67, p<0.001), AT (β=0.31, p<0.01), and US (β=0.35, p<0.001). No significant correlation was found between US and BI (p=0.96). DISCUSSION: Findings suggest that PEOU significantly affects physicians' behavioural intention to use alerts, with high patient volume and older age lowering acceptance due to alert fatigue. Adaptive, context-aware CDSS alerts can improve usability and align better with clinical workflows, enhancing efficiency in high-demand environments.
conclusionThis study highlights the need for context-aware, frequency-optimised alert designs to enhance CDSS acceptance, improve user experience and streamline clinical workflows.
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