Computer Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2756
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Item A Model-Based Systems Engineering Simulation: Analysis and Design of Hospital Bed Maintenance in Critical Health Care Systems(2018) Dávila Andino, Arturo; Fu, Michael C.; Wood, Kenneth E.; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis summarizes the results of various methodologies integrated to solve a staffing problem when cleaning and maintaining hospital beds. First, a simplified systems engineering design model was developed to translate the need for reducing the total turnaround time of maintaining hospital beds into a performance requirement of the average time a hospital bed waits for service. The tools that were used were queueing analysis, discrete-event simulation modeling, and optimization via simulation. Finally, this work presents the derived staffing requirements from the pertinent measure of effectiveness, the average waiting time.Item Health Care Management System for Diabetes Mellitus: A Model-based Systems Engineering Framework(2015) Katsipis, Iakovos; Baras, John S.; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The present thesis develops a framework for Health Care Management Systems using modern Model-Based Systems Engineering methodologies and applies it to Diabetes Mellitus. The desired architecture of such systems is described. Tests and interventions, including Health Care IT, used for Diabetes 2 diagnosis and treatment, are described and modeled. A Controlled Markov Chain model for the progression of Diabetes Mellitus with three states, three diagnostic tests, ten interventions, three patient types, is developed. Evaluation metrics for healthcare quality and associated costs are developed. Using these metrics and disease models, two methods for tradeoff analysis between healthcare quality and costs are developed and analyzed. One is an exhaustive Monte Carlo simulation and the other utilizes multi-criteria optimization with full state information. The latter obtains similar results as the former at a fraction of the time. Practical examples illustrate the powerful capabilities of the framework. Future research directions and extensions are described.