Monitoring policy in the context of preventive treatment of cardiovascular disease

Preventing chronic diseases is an essential aspect of medical care. To prevent chronic diseases, physicians focus on monitoring their risk factors and prescribing the necessary medication. The optimal monitoring policy depends on the patient's risk factors and demographics. Monitoring too frequently may be unnecessary and costly; on the other hand, monitoring the patient infrequently means the patient may forgo needed treatment and experience adverse events related to the disease. We propose a finite horizon and finite-state Markov decision process to define monitoring policies. To build our Markov decision process, we estimate stochastic models based on longitudinal observational data from electronic health records for a large cohort of patients seen in the national U.S. Veterans Affairs health system. We use our model to study policies for whether or when to assess the need for cholesterol-lowering medications. We further use our model to investigate the role of gender and race on optimal monitoring policies.

Automated summary

Preventing chronic diseases is crucial in medical care. Physicians prevent chronic diseases by monitoring risk factors and prescribing necessary medication. The optimal monitoring policy depends on the patient's risk factors and demographics. This study proposes a finite horizon and finite-state Markov decision process to define monitoring policies, using stochastic models based on longitudinal observational data from electronic health records. The research investigates policies for cholesterol-lowering medication and the influence of gender and race on optimal monitoring policies.