Data Analytics and Mathematical Models to Facilitate Disease Prevention in the U.S.

Abstract

The U.S. is leading in healthcare expenditures worldwide, but health outcomes in the U.S. are not reflective of the level of spending. Prevention plays a crucial role in improving the health of individuals in the U.S., since it helps people live longer and healthier lives. Preventive services include actions that prevent diseases from ever occurring, detect diseases at an early stage, and manage diseases that have already been diagnosed. In this dissertation, we use data analytics and mathematical modeling techniques to better understand factors that influence disease prevention and help provide efficient solutions. In the first part of this dissertation, we study two problems of disease prevention at the public health level. First, we investigate the impact of state-level vaccination exemption policy and of the highly publicized Disneyland measles outbreak on MMR vaccination rates of young children. At the same time, we highlight the impact that the choice of socioeconomic factors can have on measurement results. We estimate the impact of these policies using multiple linear regression. Furthermore, we study the sensitivity of the results by examining a number of different approaches for the selection of socioeconomic control variables. Second, we utilize big data to estimate the additive cost of chronic diseases and study their cost patterns. We model the cost based on a cost hierarchy; that is, the cost of each condition is modeled as a function of the number of other more expensive chronic conditions the individual has. Using large scale claims data, we identify members that suffer from one or more chronic conditions and estimate their healthcare expenditures. Through our analysis, we categorize the chronic conditions into different expenditure groups based on the characteristics of their cost profiles. In the second part of this dissertation, we study two problems of disease prevention at the healthcare provider level, focusing in the area of cardiology. First, we study the adoption of conversational agent technology by patients with heart failure. Conversational agents can help patients with heart failure to manage their condition and provide frequent feedback to their healthcare providers. We analyze data from two studies, with each study focusing on a different type of conversational agent. We compare the two types of conversational agent technologies in terms of patient engagement, and investigate which patient characteristics are important in determining the patient engagement. Second, we tackle the problem of outpatient scheduling in the cardiology department of a large medical center. The outpatients have to go through a number of diagnostic tests and treatments before they can complete the final procedure. We develop an integer programming model to schedule appointments that are convenient for the outpatients by minimizing the number of visits that the patients have to make to the hospital and the time they spend waiting in the hospital. Furthermore, we investigate whether scheduling outpatients in groups can lead to better schedules for the patients.