Continuous tracking of patient’s health data through electronic health records (EHRs) has created an opportunity to predict healthcare policies’ long-term impacts. Despite the advances in EHRs, data may be missing or sparsely collected. In this article, we use EHR data to develop a simulation model to test multiple treatment guidelines for cardiovascular disease (CVD) prevention. We use our model to estimate treatment benefits in terms of CVD risk reduction and treatment harms due to side effects, based on when and how much medication the patients are exposed to over time. Our methodology consists of using the EM algorithm to fit sparse health data and a discrete-time Monte-Carlo simulation model to test guidelines for different patient demographics. Our results suggest that, among published guidelines, those that focus on reducing CVD risk are able to reduce treatment without increasing the risk of severe health outcomes.