Cardiovascular Diseases

Access to electronic health records creates an opportunity to build stochastic models that support healthcare providers’ decisions to prevent chronic diseases. As the patient’s health conditions vary, decision-makers must apply optimal medical policies that learn from patients’ health behaviors and consider their needs. In this dissertation, we present new models that address the following key challenges: (1) understanding how the patient demographics influence the disease progression, (2) developing sequential decision-making models under uncertainty that pursue the best health outcomes for individual patients, and (3) developing sequential decision-making models with limited resources to prevent chronic diseases for a population. We propose operations research methods to develop policies to prevent cardiovascular diseases. We applied our models to longitudinal data for cardiovascular diseases in a large cohort of patients seen in the national Veterans Affairs health system. The contributions of this work include: (1) Developing an EM algorithm to model patient’s health progression, (2) creating a simulation framework to test and analyze different treatment guidelines, (3) developing a sequential decision-making model to define cholesterol monitoring policies that maximize societal benefits, and (4) developing an algorithm for identifying and selecting high-risk patients into adherence-improving interventions. Finally, our modeling framework establishes the analytical and theoretical foundation to build stochastic models that address multiple healthcare opportunities for improvement.