UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Data Analytics and Mathematical Models to Facilitate Disease Prevention in the U.S.(2020) Apergi, Lida Anna; Baras, John; Golden, Bruce; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)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.Item Prioritizing Patients: Stochastic Dynamic Programming for Surgery Scheduling and Mass Casualty Incident Triage(2011) Herring, William L.; Herrmann, Jeffrey W; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The research presented in this dissertation contributes to the growing literature on applications of operations research models to problems in healthcare through the development and analysis of mathematical models for two fundamental problems facing nearly all hospitals: the single-day surgery scheduling problem and planning for triage in the event of a mass casualty incident. Both of these problems can be understood as sequential decision-making processes aimed at prioritizing between different classes of patients under significant uncertainty and are modeled using stochastic dynamic programming. Our study of the single-day surgery scheduling problem represents the first model to capture the sequential nature of the operating room (OR) manager's decisions during the transition between the generality of cyclical block schedules (which allocate OR time to surgical specialties) and the specificity of schedules for a particular day (which assign individual patients to specific ORs). A case study of the scheduling system at the University of Maryland Medical Center highlights the importance of the decision to release unused blocks of OR time and use them to schedule cases from the surgical request queue (RQ). Our results indicate that high quality block release and RQ decisions can be made using threshold-based policies that preserve a specific amount of OR time for late-arriving demand from the specialties on the block schedule. The development of mass casualty incident (MCI) response plans has become a priority for hospitals, and especially emergency departments and trauma centers, in recent years. Central to all MCI response plans is the triage process, which sorts casualties into different categories in order to facilitate the identification and prioritization of those who should receive immediate treatment. Our research relates MCI triage to the problem of scheduling impatient jobs in a clearing system and extends earlier research by incorporating the important trauma principle that patients' long-term (post-treatment) survival probabilities deteriorate the longer they wait for treatment. Our results indicate that the consideration of deteriorating survival probabilities during MCI triage decisions, in addition to previously studied patient characteristics and overall patient volume, increases the total number of expected survivors.