Data-Driven Approaches to Optimize Long-Term Care for Aging Populations: A Review of Predictive Analytics

This review paper explores the role of predictive analytics in optimizing long-term care for aging populations, addressing the challenges posed by increasing demand and chronic disease management. Predictive analytics, powered by machine learning algorithms and data mining, enhances patient outcomes by enabling early intervention, personalized care plans, and efficient resource allocation. Despite its benefits, the integration of predictive tools into healthcare systems faces challenges related to data privacy, security, and ethical considerations, particularly around algorithmic decision-making. This paper provides a comprehensive overview of key predictive techniques used in long-term care, outlines the benefits they offer, and discusses the ethical and logistical challenges associated with their implementation. The conclusion presents recommendations for future research, focusing on reducing bias in predictive models and ensuring that policy developments prioritize data protection and ethical standards. By addressing these areas, predictive analytics has the potential to improve care delivery, reduce hospital readmissions, and make long-term care more sustainable.