ArticleFrontiers in public health2025
Impact of an artificial intelligence-driven operational management system on operational efficiency in health care organization in Saudi Arabia: a mediating role of staff attitude.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Explainable and Trustworthy Artificial Intelligence in Cardiology: A Narrative Review of Clinical Applications, Operational Integration, and Future Directions.Journal of clinical medicine · 2026Review
- From broad strokes to fine details: qualitative research on the challenges of smart hospitals in Iran.Journal of health, population, and nutrition · 2026Article
- Artificial Intelligence perception and its influence on the Psychological Distress of Healthcare Professional.Pakistan journal of medical sciences · 2026Article
- Personalized AI for workplace health promotion: performance management and healthcare worker engagement through digital analytics.Frontiers in public health · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: In recent years, Artificial Intelligence (AI) is transforming healthcare systems globally and improved the operational efficiency in healthcare organizations. The authors examined how an artificial intelligence (AI)-driven operational management system (OMS) affected operational efficiency in health care units in the Kingdom of Saudi Arabia (KSA). They also investigated the mediating role of staff attitudes in the relationship between OMSs and operational efficiency. This research contributes to the field by applying the theory of planned behavior to examine health care professionals' perceptions of AI-based OMSs and their impact on operational efficiency. Methods: To achieve study objectives, a quantitative research design, with cross-sectional survey methodology, was used to gather data from 287 health care professionals across various hospitals in the KSA. The authors used a partial least squares structural equation modeling (PLS-SEM) approach to hypothesis testing. Results: The findings indicated that an AI-based OMS significantly improved operational efficiency and positively affected staff attitudes. Furthermore, staff attitudes mediated the relationship between an AI-based OMS and operational efficiency. Discussion: The study finding highlights the dual benefits of AI-based OMSs in enhancing both operational performance and employee satisfaction. The results suggest that health care organizations in the KSA should invest in AI technologies to optimize operational efficiency and improve staff attitudes. The findings also emphasize the need to address employee perceptions to fully capitalize on the benefits of AI implementations. They also introduce staff attitudes as a mediating factor, offering new insights into the interaction between technology and employee engagement.
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Registered trials
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