Access Type

Open Access Dissertation

Date of Award

January 2021

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Industrial and Manufacturing Engineering

First Advisor

Leslie Monplaisir

Second Advisor

Celestine Aguwa

Abstract

Hospitals are faced with significant challenges during and after natural or human-caused disasters. Surge planning is a critical component of every healthcare facility’s emergency plan and response system. The process of managing and allocating scarce resources by tackling the vulnerability inherent to patients means that defining improvement priorities is one of the main challenges healthcare systems face when responding to a medical surge event (e.g., COVID-19). The consequences of these challenges include increased patient mortality, ambulance diversion, long wait times, and unavailability of beds. Previous efforts in hospital operations management have successfully applied operations research techniques in analyzing and optimizing emergency department (ED) operations during normal conditions. Limited research has been conducted for the current pandemic. This thesis aims to develop models that help answer these research questions: (a) what indicators influence the performance of EDs during a surge event and (b) how can we model for the constant level of hospital resources and the changing demand of medical care? We propose an intelligent-based framework to improve ED operations following a four-stage process. Stage one – developing a unique modified fuzzy Delphi (MOFD) method to identify the relevant indicators crucial in evaluating the ED’s performance during a surge. Stage two – building univariate and multivariate forecasting models to forecast daily ED patient arrivals, which will help hospital management efficiently plan and allocate limited ED resources. Stage three – investigating the current prolonged ED length of stay for COVID-19 patients, using a machine learning approach. Stage four – developing a multi-paradigm simulation-optimization framework to investigate how resource allocation affects the ED’s performance during a surge. The expected outcomes of the study are the multi-objective combination of indicators to optimize ED performance and studying the interactions between the different ED operations to improve service capacity. Our proposed activity will assist hospital administrators and clinicians in planning effectively to ramp up capacity in response to the current and future pandemic.

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