Performance Improvement of the Yellow Zone in Emergency Department using Discrete Event Simulation Approach


  • Ireen Munira Ibrahim
  • Choong-Yeun Liong
  • Sakhinah Abu Bakar
  • Ahmad Farid Najmudd





Discrete Event Simulation, Emergency Department, LOS, Waiting Time, Utilization Rate.


Overcrowding is a major concern for the Emergency Department (ED) management at the public hospital under study. Although the number of patients in the Yellow Zone (YZ) of the department represents only 30% of the total visiting patient per day, the Key Performance Indicator (KPI) of the zone’s patients’ LOS (LOS) as well as waiting time are not achievable due to the resources constraints. Therefore, this paper discusses the application of Discrete Event Simulation (DES) approach on modeling the YZ’s daily operations. The model was developed using Arena software to assist the ED management to better understand their system behavior and causes for the high patients’ LOS and waiting time. The simulation outputs show that the bottleneck of the system is waiting for an available resource. A few scenarios were designed based on the discussion made with the ED management for possible improvement. The results show a significant reduction of 25% and 35% in the total average of patients’ LOS for the patients of the observation unit and the intensive unit respectively. Meanwhile, for the total average patients’ waiting time, the results show a reduction of 34% for the observation unit and 29% reduction for the intensive unit.





[1] Baesler FF, Jahnsen HE & DaCosta M (2003), The use of simulation and design of experiments for estimating maximum capacity in an emergency room. Proceedings of the Winter Simulation Conference, pp. 1903-1906.

[2] Gunal MM & Pidd M (2006), Understanding accident and emergency department performance using simulation. Proceedings of the Winter Simulation Conference, pp. 446-452.

[3] Brailsford SC & Harpar PR (2009), An analysis of the academic literature on simulation and modeling in health care. Journal of Simulation 2, 130-140.

[4] Raunak M, Osterweil LAW, Clarke L & Henneman P (2009), Simulating patient flow through an emergency department using process-driven discrete event simulation. Proceedings of the ICSE Workshop on Software Engineering in Health Care, pp. 73-83.

[5] Wiler JL, Griffey RT & Olsen T (2011), Review of modeling approaches for emergency department patient flow and crowding research. Journal of Academic Emergency Medicine 18, 1371 – 1379.

[6] Duguay C & Chetouane F (2007), Modeling and improving emergency department systems using discrete event simulation. Journal of Simulation 83, 311-320.

[7] Carson II, John S (2005), Introduction to Modeling and Simulation. Proceedings of the Winter Simulation Conference, pp. 16-23.

[8] Kelton WD, Sadowski RP & Zupick NB (2015), Simulation with Arena. McGraw-Hill.

[9] Law AM & McComas MG (2001), How to build valid and credible simulation models. Proceedings of the Winter Simulation Conference, pp. 24-33.

[10] Carson I.I. & John, S. (2002), Verification validation: model verification and validation. Proceedings of the Winter Simulation Conference, pp. 52-58.

[11] Zulkifli MR, Annamalai M, Isahak K & Ahmad R (2016), Estimating the right allocation of doctors in emergency department. Proceedings of the Knowledge Management International Conference, pp. 446 – 452.

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