author(s)
Amarachi A Igwilo

abstract
Hospital-acquired infections (HAIs) remain a major cause of patient morbidity, mortality, and increased healthcare costs, with critical care units and surgical wards particularly vulnerable due to invasive procedures, compromised immunity, and prolonged hospital stays. Evidence-based nursing bundles structured, standardized sets of clinical interventions have consistently demonstrated effectiveness in reducing HAI incidence when applied systematically. This study examines the integration of such bundles with Internet of Things (IoT) devices to enhance compliance monitoring, improve real-time decision-making, and strengthen infection prevention strategies in high-risk hospital environments. The proposed framework incorporates standardized nursing bundles for central line-associated bloodstream infection (CLABSI) prevention, ventilator-associated pneumonia (VAP) protocols, and surgical site infection (SSI) reduction, combined with IoT-enabled sensors, wearable devices, and environmental monitoring systems. Real-time data from hand hygiene dispensers, patient vitals, and cleanliness audits are captured, securely transmitted to cloud-based platforms, and analyzed using machine learning algorithms to identify deviations from established care protocols. Embedding these IoT data streams into nursing workflow dashboards enables clinical teams to receive actionable alerts, track bundle compliance, and initiate timely interventions. Simulated pilot testing suggests that such integration can significantly reduce HAI rates, enable faster detection of protocol breaches, and optimize resource allocation for infection control efforts. This fusion of IoT capabilities with evidence-based nursing bundles not only enhances clinical accountability and supports precision nursing but also provides a foundation for data-driven quality improvement initiatives. Future research should address interoperability standards, cost-effectiveness analysis, and robust privacy safeguards to ensure scalable, sustainable adoption of IoT-assisted infection prevention systems across diverse healthcare settings.