Zuber's Safety-III: Advancing AI-Driven Predictive Analytics for Patient Safety and High-Reliability Healthcare Systems

Abstract

Traditional patient safety models, such as Safety-I and Safety-II, have been instrumental in minimizing medical errors. However, modern healthcare requires a more proactive, predictive, and AI-driven approach. This research introduces Safety-III, an advanced framework that combines AI-driven predictive analytics, real-time adaptive risk management, and high-reliability healthcare systems (HROs). The study evaluates the effectiveness of Zuber's Safety-III using AI-driven patient safety data analysis, qualitative insights from healthcare leaders, and theoretical assessment of AI-based risk models. Findings show that AI interventions significantly reduce adverse events, enhance compliance, and foster self-sustaining safety cultures in hospitals. The research highlights the transformative potential of Safety-III in reshaping global patient safety frameworks.