Transforming Infection Control Through Automation, Digital Technologies, And Analytics (The Review of Diabetic Studies, 2024)
BackgroundTraditional infection control relies on manual surveillance, which suffers from underreporting (30-50%), delays, and resource limitations, contributing to 1.7 million annual HAIs in the US alone, with global costs exceeding $30 billion.​MethodsThis narrative review synthesizes peer-reviewed evidence from 2015-2026 on automation (e.g., UV robots), digital technologies (e.g., IoT sensors, EHR triggers), and analytics (e.g., AI/ML prediction) sourced from PubMed, Scopus, and Cochrane. RCTs, cohorts, and systematic reviews (n>50) were prioritized, excluding pre-2015 or non-healthcare studies.​ResultsTechnologies reduced HAIs by 20-40% in ICUs via AI surveillance and UV disinfection (96% microbial kill); IoT improved hand hygiene compliance to 72-85%; predictive ML achieved 90-95% accuracy in early detection. LMIC pilots, including Egypt, showed scalable mobile AI for outbreaks.​ConclusionsAutomation, digital tools, and analytics surpass manual methods, enabling proactive HAI prevention despite barriers like legacy systems. Phased adoption with training and policies is essential for equitable, sustainable implementation toward zero-HAI goals.​