Abstract

The early detection of patient safety risks remains a persistent challenge across healthcare systems, particularly in high-demand, resource-constrained settings. Health Information Analytics (HIA)—the systematic analysis of health-related data—has emerged as a transformative approach in identifying adverse trends, predicting safety events, and enabling proactive interventions. This paper proposes a framework for leveraging HIA to support early detection and prevention of patient safety risks. Drawing from a comprehensive review of existing literature, the framework integrates clinical decision support systems, real-time electronic health record (EHR) monitoring, natural language processing, and machine learning algorithms to detect anomalies in patient outcomes and workflows. Emphasis is placed on risk stratification, predictive modeling, and cross-functional feedback mechanisms to enhance clinical governance and data-driven decision-making. By aligning informatics tools with patient safety strategies, the proposed model serves as a guide for health institutions seeking to harness analytics for risk prevention. This work is grounded solely on secondary research and aims to contribute actionable insights into safer, analytics-enabled care delivery.