Advancing Emergency Department Efficiency, Infectious Disease Management at Mass Gatherings, and Self-Efficacy Through Data Science and Dynamic Modeling
Date
2024-02-27
Authors
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Publisher
Virginia Polytechnic Institute and State University
Abstract
This dissertation employs management systems engineering principles, data science, and industrial
systems engineering techniques to address pressing challenges in emergency department (ED)
efficiency, infectious disease management at mass gatherings, and student self-efficacy. It is
structured into three essays, each contributing to a distinct domain of research, and utilizes
industrial and systems engineering approaches to provide data-driven insights and recommend
solutions.
The first essay used data analytics and regression analysis to understand how patient length of stay
(LOS) in EDs could be influenced by multi-level variables integrating patient, service, and
organizational factors. The findings suggested that specific demographic variables, the complexity
of service provided, and staff-related variables significantly impacted LOS, offering guidance for
operational improvements and better resource allocation. The second essay utilized system
dynamics simulations to develop a modified SEIR model for modeling infectious diseases during
mass gatherings and assessing the effectiveness of commonly implemented policies. The results
demonstrated the significant collective impact of interventions such as visitor limits, vaccination
mandates, and mask wearing, emphasizing their role in preventing health crises. The third essay
applied machine learning methods to predict student self-efficacy in Muslim societies, revealing
the importance of socio-emotional traits, cognitive abilities, and regulatory competencies. It
provided a basis for identifying students with varying levels of self-efficacy and developing
tailored strategies to enhance their academic and personal success.
Collectively, these essays underscore the value of data-driven and evidence-based decision-
making. The dissertation’s broader impact lies in its contribution to optimizing healthcare operations, informing public health policy, and shaping educational strategies to be more culturally sensitive and psychologically informed. It provides a roadmap for future research and practical
applications across the healthcare, public health, and education sectors, fostering advancements
that could significantly benefit society.
Description
Keywords
Emergency Department, Overcrowding, Patient Flow, Regression Analysis, Mass Gathering, Epidemic Modeling, COVID-19, System Dynamics, Self-Efficacy, Prediction, Machine Learning