October 4, 2023
Khodakaram Salimifard

Khodakaram Salimifard

Academic Rank: Associate professor
Address:
Degree: Ph.D in Operations Research
Phone: 07731222118
Faculty: School of Business and Economics

Research

Title
Applying a Mixture of Discrete Event Simulation and Data Mining in Investigating hospital Laboratory Performance
Type Thesis
Keywords
شبيه سازي گسسته پيشامد، داده كاوي، عملكرد، آزمايشگاه بيمارستان، بهداشت و درمان
Researchers Khodakaram Salimifard (Primary advisor) , Ahmad Ghorbanpour (Advisor)

Abstract

Background: In recent decades, healthcare organizations have faced increasing pressures to provide quality services even under normal conditions, especially laboratories that are at the forefront of diagnosing people's diseases, while with increasing costs, reimbursement low prices and new regulatory demands and most importantly the waiting time. In special and critical situations such as Corona, the role of laboratories in the diagnosis of new diseases such as Corona became much more prominent than in normal conditions. Therefore, examining the workload of the laboratory has become particularly important in recent studies. Aim: The purpose of this research is to investigate the workload of the laboratory, and then, based on that, measure the ability of this system to respond to events such as the corona virus. Methodology: In this research, using simulation and data mining, the functioning of the laboratory in the event of a disease epidemic has been investigated. A mixed approach of data mining simulation was used. The performance of current laboratory conditions as well as critical conditions are modeled using discrete event simulation. Its input data was clustered. Findings: The implementation of the Covid-19 scenarios showed that the laboratory will be able to respond only in the best case scenario, and in other scenarios, if special measures are not taken into account, it will not respond to test requests. Conclusions: This study showed that this approach to check the performance of the laboratory during Bajran is helpful to decision makers. By improving the number of employees in each laboratory unit, the patient's waiting time can be significantly reduced in order to improve the performance of the laboratory.