Integrating discrete event simulation and data envelopment analysis for performance measurement and efficency evaluation of resource configurations in an outpatient clinic

• Main Author: Mohamad Amirrul Muqmin, Mohamad Isa
• Format: Thesis
• Language: English; English; English
• Published: 2023
• Subjects: HM Sociology.
• Online Access: https://etd.uum.edu.my/11011/1/depositpermission-826822.pdf; https://etd.uum.edu.my/11011/2/s826822_01.pdf; https://etd.uum.edu.my/11011/3/s826822_02.pdf; https://etd.uum.edu.my/11011/

Health care facilities, especially outpatient clinics, are the main source of medical care and treatment for the population. To provide quality services, facilities must perform their work optimally. System performance, such as patient wait time, patient cycle time, and throughput, can be measured using discrete event simulation (DES). DES replicates facility structures and behaviour and provides a platform for measuring the impact of operational alternatives (resource configurations) on system performance. However, current tools from DES do not identify the best resource configurations for future implementation. Therefore, this research develops a DES model for an outpatient clinic and evaluates the effectiveness of a range of possible resource configurations to improve system performance. The Kesihatan Changlun clinic was considered as a case study. Relevant data, such as patient arrival time, resource service time, and patient flow were observed and collected. The information was then fed into the DES model, which was designed and developed using Arena software. The model was used to measure clinic performance and verify that the clinic was operating efficiently. Actual performance was then determined by testing the various resource configurations. Their relative efficiency was evaluated using Data Envelopment Analysis (DEA). To rank the efficient configurations, cross-efficiency analysis DEA was used. The analysis of the results showed that the Kesihatan Changlun clinic is efficient. However, performance can be further improved by allocating an appropriate number of resources to relevant processes. This research helps decision makers to effectively manage their healthcare facilities by finding the best resource configuration.