Predictive modeling for healthcare needs in the aging U.S. population: A conceptual exploration

The aging population in the United States poses significant challenges for healthcare systems, necessitating advanced strategies to anticipate and meet their healthcare needs. This review paper explores the potential of predictive modeling to address these challenges, offering a conceptual framework that integrates diverse data sources, including electronic health records (EHRs) and social determinants of health (SDOH). Key predictive modeling techniques, such as machine learning and statistical methods, are examined for their application in predicting patient outcomes, disease prevalence, and resource allocation. The paper also highlights the challenges of data privacy, model accuracy, and ethical considerations in the deployment of predictive models. Recommendations for future research emphasize the need for advanced modeling techniques, improved integration of SDOH, and the development of ethical and regulatory frameworks. By leveraging predictive modeling, healthcare systems can enhance their capacity to manage the complex health needs of an aging population, ultimately improving patient outcomes and optimizing resource allocation.