Physicians seek to prevent chronic diseases by tracking and nudging patients’ healthcare behavior despite the limited time, resources, and diverse needs of a heterogeneous panel of patients. Therefore, there is a need to schedule patients optimally. If programmed incorrectly, patients may forgo needed treatment and suffer disease-related adverse events. Moreover, patients may not adhere to medication and follow-up recommendations despite the physician’s efforts. We develop a guided set of solutions to improve patients’ health. First, we propose a finite horizon and finite-state Markov decision process to define monitoring policies. Second, we develop a discrete Monte-Carlo simulation model built using electronic health records to test policies. Third, we build a dynamic logistic regression model that identifies low-adherent patients. Fourth, we will discuss a preliminary version of a multi-armed bandit model to prioritize high-risk patients within a limited budget scenario. We test and validate using the Veterans Affairs health system longitudinal data for cardiovascular diseases. Finally, we include race and gender effects, given the one-size-fits-all nature of the current national cholesterol guidelines.