Investigating the Confounding Role of Artificial Intelligence on the Relationship between Patient Safety and Infection Control Practices in Enhancing Patient Satisfaction in Healthcare

Vollständiger Abstract

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This study aimed to investigate how AI mediates the relationship between infection control, patient safety, and patient satisfaction. The study used an exploratory sequential research design with two phases. In the first phase, 25 qualitative studies were analyzed using PRISMA (2009–2024), which were supplemented by quantitative analysis of 384 samples. A cross-sectional survey was conducted among physicians, nurses, and paramedics. The scales were found to be reliable and valid in PLS-SEM measurement models. The structural model was run to investigate the direct and indirect effects. The result suggests the significant direct effect of AI and infection control on satisfaction. However, patient safety and satisfaction were found to be insignificant. This implies full mediation by AI between Patient safety and satisfaction. AI's mediating role was confirmed between safety, infection control, and satisfaction. The study concludes that themes emerged from the review of the studies, and the findings of the quantitative data were consistent. Hence, this study validates the results of previous studies in the context of the Middle East. Through triangulation, the diverse sources used in this study have reduced bias, reinforced internal validity, and provided a deep insight into the mediating effect of AI in enhancing patient safety, infection control, and patient satisfaction. The significant role of experience and staff categories emphasizes the need for targeted interventions and training to foster a culture of safety and technological adoption across healthcare roles. Healthcare policymakers should consider these factors when implementing new technologies and designing training programs to ensure the benefits of AI, infection control, and patient safety.