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Central Line-Associated Bloodstream Infections (CLABSI) pose significant challenges in healthcare systems globally, contributing to increased morbidity, mortality, and healthcare costs. As healthcare organizations strive to improve patient safety and quality of care, Artificial Intelligence (AI) presents considerable promise in the prevention, detection, and management of CLABSI. This paper proposes a conceptual framework that integrates AI within healthcare systems, aligning technological innovations with human workflows, system design, and risk management strategies. By taking a systems approach, the framework supports the implementation of AI tools in ways that are compatible with the complexity of healthcare delivery. The paper explores the potential and significance of AI in enhancing healthcare through the prevention, early detection, and management of patient safety concerns, including CLABSI. It highlights how AI applications can predict infection risks, support timely interventions, and operate in tandem with standard infection control protocols to reduce the incidence of CLABSI. This integrated approach aims to promote safer, more efficient, and patient-centered care.
Saad et al. (Mon,) studied this question.