Saad Ahmed Alaaeldin, Hassan Abduraouf, Alali Ahmad, Alkhatib Fathy, Tolba Mohammed F, Simsekler Mecit Can Emre
Khalifa University of Science & Technology, Department of Management Science & Engineering, Abu Dhabi, 127788, United Arab Emirates.
Khalifa University of Science & Technology, Department of Computer and Information Engineering, Abu Dhabi, 127788, United Arab Emirates.
Risk Manag Healthc Policy. 2025 Sep 3;18:2887-2898. doi: 10.2147/RMHP.S520035. eCollection 2025.
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.
中心静脉导管相关血流感染（CLABSI）在全球医疗系统中构成了重大挑战，导致发病率、死亡率上升以及医疗成本增加。随着医疗机构努力提高患者安全和护理质量，人工智能（AI）在CLABSI的预防、检测和管理方面展现出了巨大潜力。本文提出了一个将AI整合到医疗系统中的概念框架，使技术创新与人员工作流程、系统设计和风险管理策略保持一致。通过采用系统方法，该框架支持以与医疗服务复杂性相兼容的方式实施AI工具。本文探讨了AI通过预防、早期检测和管理患者安全问题（包括CLABSI）来提升医疗服务的潜力和重要性。它强调了AI应用如何能够预测感染风险、支持及时干预，并与标准感染控制协议协同运作以降低CLABSI的发生率。这种综合方法旨在促进更安全、更高效且以患者为中心的护理。
