Artificial Intelligence in Hospital-Based Clinical Decision-Making: A Qualitative Study on Physicians And Nurses Experiences with Clinical Decision Support Systems
The study concludes that successful implementation of AI-based CDSS requires not only technical accuracy but also transparency, adequate training, workflow integration, and clear accountability structures.
AI-CDSS should be regarded as tools that support, rather than replace, nursing clinical judgment, and safe and equitable implementation requires robust digital infrastructure, nurse education, local evaluation of system performance, multidisciplinary governance, and clear safeguards for privacy, transparency, accountab...
I. M, Bamigboye T. O., Oniyide A. A.· International journal of re...· 0 citations
This article shows how the Technology Acceptance Model and Technology Trust are combined in the Unified Theory of Acceptance and Use of Technology (UTAUT) and how the Perceived Empathy Model (PEM) and Perceived Clarity of Accountability Model (PCA) are extensions of these models.
This review examines the main factors that shape patient trust in AI-assisted diagnosis and triage and argues that trust in medical AI should be understood as calibrated confidence rather than simple approval or rejection.
Oliwer Műller, Jagoda Pałubska, Zuzanna Rafałowska et al.· International Journal of Inn...· 0 citations
Clinical practice has transformed due to a surge in the development of artificial intelligence tools to support decision-making. AI-Assisted Clinical Decision Support (AI-CDS) is one of the most promising technological tools in improving care outcomes. AI-CDS tools are digital systems relying on artificial intelligence...
Dinah Limiri· Premier Journal of Science· 0 citations
AI-enabled clinical decision support systems (CDSS) have the potential to support the detection of patient deterioration, but their value depends on outputs that fit clinical workflows, support reasoning, and minimise alert fatigue. Little is known about clinician perspectives on Multi-Output Early Warning Systems (M...
Monica Noselli, A. H. van der Vegt, Maxime Cordeil et al.· Journal of Technology in Beh...· 0 citations
Abstract Background Clinical decision support systems (CDSSs) are widely implemented in hospitals, yet their uptake in nursing practice remains inconsistent. While electronic health records continuously capture nursing decision data, these data are rarely used to support structured quality improvement. Decision mining...
M. Berkhout, J. Leenen, Koen Smit et al.· JMIR Formative Research· 0 citations
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