INTELLIGENT ANALYTICS IN HEALTHCARE: ENHANCING DIAGNOSIS, PREDICTION, AND EVIDENCE-BASED RESEARCH

The rapid digitalization of healthcare has led to unprecedented growth in clinical, operational, and biomedical data, creating both opportunities and challenges for healthcare systems. Intelligent analytics, encompassing artificial intelligence, machine learning, big data analytics, and advanced decision-support systems, has emerged as a critical enabler for improving diagnostic accuracy, predictive capability, and evidence-based clinical practice. This conceptual chapter develops an integrative framework that explains how intelligent analytics transforms healthcare decision-making across diagnostic, predictive, and research domains. Drawing on prior literature, the framework identifies key data sources, analytical capabilities, mediating mechanisms, and outcome dimensions. The model highlights how intelligent analytics supports early disease detection, personalized risk prediction, clinical workflow optimization, and continuous knowledge generation. It further emphasizes governance, explainability, data quality, and ethical safeguards as essential boundary conditions influencing the effectiveness of analytics-driven healthcare. By synthesizing fragmented research streams into a coherent conceptual structure, this paper advances theoretical understanding of intelligent healthcare analytics and provides a foundation for empirical testing. The proposed framework offers practical guidance for healthcare organizations, policymakers, and researchers seeking to leverage analytics for improved patient outcomes, operational efficiency, and scientifically grounded medical decision-making.