Date of Award

10-1-2021

Publication Type

Thesis

Degree Name

M.A.Sc.

Department

Industrial and Manufacturing Systems Engineering

Keywords

Patient wait times, Computed tomography, Magnetic resonance imaging, COVID-19 impact

Supervisor

G. Zhang

Supervisor

A. Snowdon

Rights

info:eu-repo/semantics/embargoedAccess

Abstract

Due to world-wide increasing population and life expectancy, the volume of chronic illnesses and number of patients admitted into hospitals is growing. Hospital services can be extremely important and expensive—particularly for Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) diagnostic imaging. Ontario hospitals follow a public healthcare system with four patient priority levels and wait time targets for each of the four priority levels. Along with increasing needs for diagnostic imaging services, COVID-19 was declared a pandemic virus on March 11th, 2020, by the World Health Organization (WHO), which was followed by the Premier of Ontario declaring a state of emergency. Through this time, hospitals were required to decrease services which were considered non-essential or non-life threatening to reserve volume for pandemic-affected patients and minimize patient flow through hospitals. The impact of COVID-19 took a toll on hospitals and their scheduling methods. This thesis focuses on completing a data analysis of an Ontario hospital’s diagnostic imaging for CT and MRI scans to develop a simulation model to determine potential scheduling formats and show how current and prospective future states of scheduling can be implemented through the impact of COVID-19. There are a small number of available simulation models which provide information on hospital-specific patient scheduling implementation for diagnostic imaging. The data analysis is completed by using hospital data, publicly available data from Health Quality Ontario (HQO), and the county’s local health unit. Results of the models will provide insight to improvements in the system to predict outpatient wait times through the COVID-19 pandemic. Wait time predictions are needed to help provide better outcome data for patients and the length of time they should be expected to wait.

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