Optimizing Data-Driven Healthcare Decision Making

Headshot of Wesley Marrero

Wesley Marrero discussed his research on data-driven approaches to optimize healthcare decision making at a recent CHDS seminar. Marrero, an assistant professor in the Thayer School of Engineering at Dartmouth College, uses operations research and statistics to improve medical decision making in four clinical areas: opioid use disorder (OUD), mental health, organ transplantation, and cardiovascular disease.

Marrero spoke about incorporating consideration of disparities into the allocation of funding for OUD medication delivery options. He projects the number of individuals with OUD nonfatal and fatal overdoses, and uses this information to inform recommendations about where and when to invest OUD funding. In his work on mental health, Marrero focuses on medical trainees, who experience high rates of anxiety, depression, and burnout. He uses smartwatches and fitness trackers to develop equitable predictions of anxiety, depression, and burnout to inform administrators and mental health professionals.

Marrero also highlighted his work on optimizing medical decision making for cardiovascular disease. He described evaluating whether genetic testing could improve cholesterol treatment plans. In a recent paper, he found that treatment plans that incorporate both clinical information and genetic testing improve health outcomes and save costs compared to treatment using only clinical information. Marrero’s future work in cardiovascular disease will focus on optimizing antihypertensive treatment plans to make treatment personalized and flexible.

Learn more: Read the publication, Optimal Cholesterol Treatment Plans and Genetic Testing Strategies for Cardiovascular Diseases
Learn more: Read the publication, A Machine Learning Approach for the Prediction of Overall Deceased Donor Organ Yield

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