ORCID Identifier(s)

0000-0003-3630-0585

Graduation Semester and Year

2022

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Industrial Engineering

Department

Industrial and Manufacturing Systems Engineering

First Advisor

Yuan Zhou

Second Advisor

Victoria Chen

Abstract

The purpose of this research is to examine methods for minimizing the influence of boarding on emergency department (ED) crowding outcomes. To accomplish this purpose, this research uses a hybrid systems model framework by combining agent-based simulation, predictive and optimization models to improve ED outcomes such as length-of-stay and left-without-being-seen rates. For the research, different types of simulation models were examined (discrete event and agent-based/discrete event combination) to identify the most parsimonious for studying ED boarding. Predictive models using simulation output were developed to understand the factors that influence future boarding levels as well as generate predictions. Research has previously highlighted how valuable bed assignment/management strategies can be in ensuring minimal length-of-stays in healthcare systems. Such research is limited for EDs specifically, however. This research contributes by directly leveraging the predictions of future boarding levels to develop a bed assignment strategy that can optimize fast track bed capacity to ensure improved ED outcomes.

Keywords

Emergency department, Hybrid systems modeling, Simulation, Boarding, Forecasting, Operations research

Disciplines

Engineering | Operations Research, Systems Engineering and Industrial Engineering

Comments

Degree granted by The University of Texas at Arlington

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