Simulation Modeling of the Outpatient Healthcare System Using Incomplete System Data and Application to a Hospital in the UAE

Abstract

Nowadays, the most frequent complaint from patients is the prolonged wait for clinical treatment. Therefore, majority of previous investigations proposed to reduce the waiting time to enhance patients’ satisfaction using simulation. However, the usual simulation modeling assumes that the entire dataset is available for investigation. Unfortunately, in many cases, existing data is incomplete, especially in the healthcare sector. This study aims to tackle the issue of processing missing data in real-life system simulation, and proposes improvement conditions that would optimize and reduce the patients waiting time. The proposed methodology is to build the model depending on the hospital requirements, then overcome the issue of incomplete data. Based on the previous literature reviews, firstly, it can be assumed that the servers follows a lognormal distributions, after that, the Opt Quest methodology in Simio can be used to find the optimum parameters of these distributions. In the second phase, the model is validated and verified using historical data in the real-world. Lastly, a comparison between the optimized simulation model and the current one will be done to check if there’s a remarkable reduction in the patients waiting time. A positive outcome would indicate significant improvement in all the key performances of the current state process.

Date of Award	Dec 2022
Original language	American English
Supervisor	Mecit Simsekler (Supervisor)