Data-Driven Decision Making in Healthcare

Abstract

The increasing availability of healthcare data has provided a great opportunity for the development of data-driven models to guide health policy and medical practice. The objective of this dissertation is to present new methods that use these data to make better healthcare decisions at a population and patient level. We first model the supply, demand, and allocation of organs for transplantation using data from the Organ Procurement and Transplantation Network and the US Census Bureau. Then, we introduce personalized treatment plans and genetic testing strategies for the management of cardiovascular diseases. We evaluate the clinical and policy implications of the treatment and testing strategies at a population level using data from the National Health and Nutrition Examination Survey. Lastly, we propose a modeling framework to consider physicians' judgment and patients' preferences in the implementation of treatment protocols. To illustrate how this method can be implemented in medical practice, we find ranges of near-optimal antihypertensive treatment choices for 16.72 million adults in the US. This research has the potential to improve healthcare practice by giving flexible and achievable guidelines to policymakers and medical professionals based on patient and population-level data.