Date of Award
2024-08-01
Degree Name
Master of Science
Department
Industrial Engineering
Advisor(s)
Md Fashiar Rahman
Abstract
Recently Hospital Systems faced a high invasion of patients generated by several events such as health crisis related epidemic (COVID, FLU) or seasonal flows. Hence, managing hospital bed availability and efficiency with proper care is obligatory for addressing the challenges associated with the overburden of patients. However, the Length of stay (LOS) is often increased due to the high patient influx and overcrowding problem occurs within the Hospital. It resolves these issues, it is essential for hospital authority to predict the Patients LOS which is the crucial indicator for the use of medical resources (allocation, utilization of providers and resource) and assessing the overcrowding within the hospital premises. Thus, accurate LOS and proper resource management is indispensable for hospital authority to ensure the maximum profit with optimized system utilization. This Study proposes a Machine Learning driven approach integrated with Simulation Software for the prediction of LOS and resource management within the hospital System. Artificial Neural Network is used for predicting the LOS in the simulation environment that learns the pertinent information from nonlinear and linear processes without prior assumption on data distribution and substantially boosts the prediction accuracy. Dynamic resources Planning is also integrated within this model that allows the management to make plans on the very first day for hospital resources based on Predicted LOS for the next few days as required. Most importantly, in this proposed Machine Learning Based Data-driven simulation method, Patients are generated randomly from a Dynamic simulation environment with different disease attributes and the simulation Predicts the LOS as per trained model in Brain. Based on this LOS, authorities can make decisions regarding the Bed allocation, Doctors requirements and Patients satisfactions for proper treatment in the hospital System. Hence this model significantly helps healthcare professionals and patients care to manage their resources and increases the patientâ??s satisfaction allowing early treatment and dynamic resource planning alongside with finance.
Language
en
Provenance
Received from ProQuest
Copyright Date
2024-08-01
File Size
72 p.
File Format
application/pdf
Rights Holder
S M Atikur Rahman
Recommended Citation
Rahman, S M Atikur, "Integrating Machine Learning And Simulation For Resource Planning Of Hospital Systems Based On Predicted Length Of Stay" (2024). Open Access Theses & Dissertations. 4202.
https://scholarworks.utep.edu/open_etd/4202