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.
Daniel F. Otero‐Leon is a Ph.D. candidate in the department of Industrial and Operations Engineering (IOE) at the University of Michigan and is co-advised by Dr. Brian Denton and Dr. Mariel Lavieri of IOE. His research interests are generally in operations research and, more specifically, in stochastic models and stochastic dynamic programming with applications to service systems, including health systems and revenue management. His dissertation research is in the area of data-driven models for improving decision-making in the context of cardiovascular disease, with the help of clinical collaborators at the U.S. Department of Veteran Affairs. His work seeks to develop new frameworks that aim for health equity by considering patients' health disparities in disease prevention policies.

The seminar series “Providing Better Healthcare through Systems Engineering” is presented by the U-M Center for Healthcare Engineering and Patient Safety (CHEPS): Our mission is to improve the safety and quality of healthcare delivery through a multi-disciplinary, systems-engineering approach.

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