Emergency Department Simulation: Proposed Model and Optimization
(*) Corresponding author
DOI: https://doi.org/10.15866/irecos.v12i4.13816
Abstract
Emergency Department (ED), being a complex and critical entity of the healthcare system, is studied in this paper for several reasons. The main challenge faced by the ED is the growing number of patients who show up without any prior notice, the 24/7 operation of the ED and the open facility to any type of illness and all age categories. These challenges increased the waiting time and staff utilization rates in the ED. Therefore, patient flow is highly influenced resulting in unnecessary costs. The long waiting time is a major problem facing EDs nowadays and should be considered as a high priority in order to ensure patient satisfaction; knowing that patient LoS may also affect resource utilization rates and hospital revenue. In this study, simulation using Arena is used in order to build a realistic model for an ED at a hospital in North Lebanon. This model is then verified and validated in order to match the real system, where improvements can be suggested for a better patient flow process and management optimization. Improvements are proposed by running different simulations using Arena Process Analyzer tool and optimization is added in order to reach an optimal solution using Arena OptQuest tool.
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Mifflin, H. (2007). The American Heritage Medical Directory.
American Nurses Association. Nurse Staffing. Retrieved from American Nurses Association: http://www.nursingworld.org/MainMenuCategories/Policy-Advocacy/State/Legislative-Agenda-Reports/State-StaffingPlansRatios
American Nurses Association. Analysis of the American Nurses Association Staffing Survey (Warwick, RI: Cornerstone Communications Group, 2001); California Nurses Association,“Mandatory Overtime Is Detrimental to Patient Care and the Health of Nurses,” 20 April 2001.
http://dx.doi.org/10.1177/216507996601401005
Hellmich, N. (2008). Aging population making more visits to the doctor’s. USA today. Retreived from: http://www.usatoday.com/news/health/2008-08-06-er_N.htm
http://dx.doi.org/10.1097/01.eem.0000340962.93668.12
Bodenheimer, T. (2005). High and rising health care costs. Part 1: seeking an explanation. Annals of internal medicine, 142(10), 847-854.
http://dx.doi.org/10.7326/0003-4819-142-10-200505170-00010
Berwick, D. M., & Hackbarth, A. D. (2012). Eliminating waste in US health care. Jama, 307(14), 1513-1516.
http://dx.doi.org/10.1001/jama.2012.362
Centeno, M. A., Giachetti, R., Linn, R., & Ismail, A. M. (2003, December). Emergency departments II: a simulation-ilp based tool for scheduling ER staff. In Proceedings of the 35th conference on Winter simulation: driving innovation (pp. 1930-1938). Winter Simulation Conference.
http://dx.doi.org/10.1109/wsc.2003.1261656
Daemi A. The Role of Electronic Triage System in Management of Hospital Emergency Department. Bull Emerg Trauma. 2016;4(1):62–3.
http://dx.doi.org/10.1186/s13049-016-0254-z
In: UML: Modeling Business System. Activity Diagrams. 2016. Available from: https://sourcemaking.com/uml/modeling-business-systems/external-view/activity-diagrams
http://dx.doi.org/10.4018/9781599041742.ch001
Shukla N, Keast JE, Ceglarek D. Role activity diagram-based discrete event simulation model for healthcare service delivery processes; International Journal of Systems Science. Operations & Logistics; 2015. pp. 1–16.
http://dx.doi.org/10.1080/23302674.2015.1088098
Nabaei A, Hamian M, Parsaei MR, Safdari R, Samad-Soltani T, Zarrabi H, et al. Topologies and performance of intelligent algorithms: a comprehensive review. Artificial Intelligence Review. 2016:1–25.
http://dx.doi.org/10.1007/s10462-016-9517-3
Lebcir R, Demir E, Ahmad R, Vasilakis C, Southern D. A discrete event simulation model to evaluate the use of community services in the treatment of patients with Parkinson's disease in the United Kingdom. BMC Health Serv Res. 2017;17(1):50.
http://dx.doi.org/10.1186/s12913-017-1994-9
Guo H, Goldsman D, Tsui K-L, Zhou Y, Wong S-Y. Using simulation and optimisation to characterise durations of emergency department service times with incomplete data. International Journal of Production Research. 2016;54(21):6494–511.
http://dx.doi.org/10.1080/00207543.2016.1205760
Dubovsky SL, Antonius D, Ellis DG, Ceusters W, Sugarman RC, Roberts R, et al. A preliminary study of a novel emergency department nursing triage simulation for research applications. BMC Res Notes. 2017;10(1):15.
http://dx.doi.org/10.1186/s13104-016-2337-3
S. Oueida, P. Abi Char, S. Kadry, and S. Ionescu. (2016).Simulation Mdels for Enhancing The Health Care Systems. FAIMA journal, December 2016, vol 4 issue 4.
http://dx.doi.org/10.4018/ijphme.2017010103
Thyagarajan S, Misra A. Contextualising Simulation in Emergency Medicine Department and Pediatric Intensive Care Unit in India. J Pediatr Crit Care 2016;3:44-50. DOI:10.21304/2016.0303.00133
http://dx.doi.org/10.21304/2016.0303.00133
Ferraro Nicole M., Day, Theodore Eugene. Simulation to Predict Effect of Citywide Events on Emergency Department Operations. Pediatric Quality & Safety: January/February 2017 - Volume 2 - Issue 1 - p e008.
http://dx.doi.org/10.1097/pq9.0000000000000008
Aggarwal R Just-in-time simulation-based training BMJ Qual Saf 2017;26:866-868.
http://dx.doi.org/10.1136/bmjqs-2017-007122
Wanying Chen, Alain Guinet, and Tao Wang. (2017) Modeling and Simulation of the Emergency Department of an Italian Hospital. Book Stochastic Modeling and Analytics in Healthcare Delivery Systems. pp 57-82. World Scientific ISBN 978-981-3220-84-3.
http://dx.doi.org/10.1142/9789813220850_0003
V. Lien, M. Niels, R. Katrien, B. Kris. (2017). Quality assessment of input data for emergency department simulation. Proceedings of the International Workshop on Innovative Simulation for Health Care, 2017,p. 1-10 (Art N° 1).
http://dx.doi.org/10.22360/springsim.2017.anss.014
Zhengchun Liu, Dolores Rexachs, Francisco Epelde, Emilio Luque. (2017) An agent-based model for quantitatively analyzing and predicting the complex behavior of emergency departments. Journal of Computational Science 21, pages 11-23.
http://dx.doi.org/10.1016/j.jocs.2017.05.015
Hu, Xia (2017).Application of Mathematical and Computational Models to Mitigate the Overutilization of Healthcare Systems. Master thesis, university of Maryland.
http://dx.doi.org/10.1007/978-4-431-53862-2_3
Shao-Jen Weng, Yeong-Yuh Xu, Donald Gotcher, and Lee-Min Wang. 2017. A pilot study of available bed forecasting system (ABFS) in the emergency healthcare network. In Proceedings of the Summer Simulation Multi-Conference (SummerSim '17). Society for Computer Simulation International, San Diego, CA, USA, Article 15, 8 pages.
http://dx.doi.org/10.22360/summersim.2017.scsc.015
Gan, Sarimah; Nasirin, Syed; Awang Piut, Suzana; Kheng, Cheah Phee; and A. Bahar, Iza Azura, "Discrete-Event Simulation Modelling Trials in Government Hospital: Preliminary Evidence from the Women and Children Hospital Sabah" (2017). PACIS 2017 Proceedings. 150.
http://dx.doi.org/10.1063/1.4980987
Steward, D., Glass, T.F. & Ferrand, Y.B.(2017). Simulation-Based Design of ED Operations with Care Streams to Optimize Care Delivery and Reduce Length of Stay in the Emergency Department. J Med Syst (2017) 41: 162.
http://dx.doi.org/10.1007/s10916-017-0804-6
Nabeel Mandahawi, Mohammed Shurrab, Sameh Al-Shihabi, Abdallah A. Abdallah & Yousuf M. Alfarah (2017).Utilizing six sigma to improve the processing time: a simulation study at an emergency department. Journal of Industrial and Production Engineering Vol. 34 , Iss. 7,2017. http://dx.doi.org/10.1080/21681015.2017.1367728
Dehghani, M., Moftian, N., Rezaei-Hachesu, P., & Samad-Soltani, T. (2017). A Step-by-Step Framework on Discrete Events Simulation in Emergency Department; A Systematic Review . Bulletin of Emergency & Trauma, 5(2), 79–89.
http://dx.doi.org/10.7860/jcdr/2017/27660.10852
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