POPULATION HEALTH MANAGEMENT THROUGH PREDICTIVE ANALYTICS
Keywords:
Predictive Analytics, Population Health Management, Machine Learning, Risk Stratification, Healthcare Data, Hospital Readmissions, Healthcare Costs, Ethical Considerations, Data Privacy, Neural NetworksAbstract
Predictive analytics has become a transformative tool in Population Health Management (PHM), allowing healthcare systems to anticipate health risks and improve outcomes through data-driven interventions. This paper explores the historical context and evolution of predictive analytics in healthcare, with a focus on its applications in PHM. Through the analysis of various models—including logistic regression, decision trees, random forests, and neural networks—this study highlights the effectiveness of predictive analytics in identifying high-risk patients, reducing hospital readmissions, and optimizing resource allocation. While the benefits of predictive analytics are clear, challenges such as data integration, model bias, and ethical considerations regarding data privacy are discussed. This paper concludes with recommendations for future research and improvements in predictive model transparency and fairness to fully realize the potential of predictive analytics in improving population health outcomes
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