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 Essays on Health Care Satisfaction, Health Insurance, and Cancer Screening Among Veterans(2021) Frost, Sydney; Chen, Jie; Public and Community Health; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Objective: to determine differences in satisfaction of health care services among veterans based on insurance type/coverage and the impact of service utilization of cancer screening services among female veterans who have received health care services within past 12 months. Methods: IPUMS National Health Interview Survey (NHIS) for the years 2013-2018 were used and logistic regressions applied. Results: veterans with VA-only coverage are significantly more dissatisfied with the services they receive compared to veterans who have any-private coverage. Conclusion: there are differences between satisfaction of care among veterans based on insurance type, but differences do not impact cancer screening utilization among female veterans who utilized health care services within the past 12 months. Future work: findings could be utilized to determine ways to increase satisfaction of care received among veterans within the VA, or drive policy creation to allow veterans to access health care services at non-VA facilities.Item STATISTICAL LEARNING WITH APPLICATIONS IN HIGH DIMENSIONAL DATA AND HEALTH CARE ANALYTICS(2017) Fan, Yimei; Ryzhov, Ilya; Mathematics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Statistical learning has been applied in business and health care analytics. Predictive models are fit using hierarchically structured data: common characteristics of products and customers are represented as categorical variables, and each category can be split up into multiple subcategories at a lower level of the hierarchy. Hundreds of thousands of binary variables may be required to model the hierarchy, necessitating the use of variable selection to screen out large numbers of irrelevant or insignificant features. We propose a new dynamic screening method, based on the distance correlation criterion, designed for hierarchical binary data. Our method can screen out large parts of the hierarchy at the higher levels, avoiding the need to explore many lower-level features and greatly reducing the computational cost of screening. The practical potential of the method is demonstrated in a case application involving a large volume of B2B transaction data. While statistical inference has been widely used for decision and policy making in health care, we particularly focused on how providers get paid for some common procedures. We explored a few rich datasets and discovered large variations among providers for how much payers/insurers have paid, aka allowed payment. Then we proposed to incorporate available providers' attributes with regression model to explain the possible reasons for those payment variations.Item Form And Function Glucometer Evaluation For Specialized Populations(2014) Santos, Luis Samai; Vaughn-Cooke, Monifa; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Patient self-management technologies (glucometer, blood pressure monitor, etc.) are a critical component of chronic disease care. Although these technologies are intended to support patient activities, low device usability can produce design imped-iments that may negatively impact patient adherence and hence treatment outcomes. In particular, patients with disabilities, who are the majority of the chronic disease population, are typically excluded from medical device usability studies required for FDA approval. This study aims to develop a usability method to: 1) evaluate patient self-management technology and 2) inform design decision making for disabled pa-tients. The study will focus on handheld device use (glucometers) for diabetic patients with mobility and vision impairment. An initial expert usability analysis was per-formed for 13 glucometers to determine the design features that are most problematic for disabled users. The usability analysis informed the design of an experiment to test disabled user performance and satisfaction for several meter interaction tasks. Com-mon diabetes disabilities were simulated in healthy subjects through the use of glasses (retinopathy, glaucoma) and gloves (arthritis, neuropathy) to evaluate the experimental protocol prior to future testing in the actual disease population. Results suggested a preference of participants for large text, large protruding buttons, and contrast color between case and buttons to facilitate locating buttons. Future studies will integrate the disabled diabetic population in the data collection and integration of these results in the design of a new glucometer. This work can inform regulatory guidelines for usability testing with disabled patients and the patient-centric design practices of medical device manufacturers.