Abstract
Purpose: This study aims to explore the role of healthcare data analytics and predictive modeling in enhancing healthcare outcomes, specifically in resource allocation, disease forecasting, and identifying high-risk populations. Methodology: The research employs a comprehensive approach, utilizing various sources of healthcare data such as electronic health records (EHRs) and public health databases. Advanced analytical techniques, including machine learning, artificial intelligence, and big data analytics, are applied to derive actionable insights. Findings: The study reveals that predictive modeling significantly enhances resource optimization, enables accurate disease prevalence forecasting, and improves the identification of high-risk populations. Case studies demonstrate how these technologies lead to more efficient healthcare delivery, cost reduction, and better patient care outcomes. Unique Contribution to Theory, Policy, and Practice: This research contributes to the theoretical understanding of healthcare data analytics by integrating advanced predictive modeling techniques with real-world healthcare applications. It offers valuable insights for policymakers on the importance of investing in data infrastructure and promoting data-driven decision-making. Practically, the study provides healthcare organizations with actionable strategies to implement predictive analytics for improved resource allocation and patient care.
Published Version
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