https://doi.org/10.4081/ecj.2024.12562
The importance of discrete event simulation as a methodology for performance evaluation in the emergency department
Submitted: 12 April 2024
Accepted: 1 August 2024
Published: 9 August 2024
Accepted: 1 August 2024
Abstract Views: 3599
PDF: 399
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Emergency Departments (ED) face the challenge of providing high-quality patient care under difficult conditions due to staff shortages or overcrowding. These challenges mean that more than ever, ED need to find ways to provide high-quality patient care despite limited resources and bottlenecks. Process analysis using Discrete Event Simulation (DES), taking into account performance-related assessment indicators, can help to improve patient care and resource utilization of staff and infrastructure. Based on process observations, interviews and time studies, a process model was developed in a general hospital ED to realistically simulate workflows. The results allow the assumption that digital technologies and an increase in staff capacity can reduce length of stay and waiting times for patients while improving staff distribution and infrastructure utilization. The study suggests that DES has great potential for use as a performance evaluation tool in the ED. In times of increasing digitalization, the potential of artificial intelligence in the context of process improvements, but also the challenges of this technology, must be given greater consideration.
Cubukcuoglu C, Nourian P, Sariyildiz IS, et al. A discrete event simu-lation procedure for validating programs of requirements: the case of hospital space planning. SoftwareX 2020;12:100539.
DOI: https://doi.org/10.1016/j.softx.2020.100539
Jun JB, Jacobson SH, Swisher JR. Application of discrete-event simu-lation in health care clinics: a survey. J Operational Res Soc 1999;50:109-23.
DOI: https://doi.org/10.1057/palgrave.jors.2600669
Günal MM, Pidd M. Discrete event simulation for performance mod-elling in health care: a review of the literature. J Simulation 2010;4:42-51.
DOI: https://doi.org/10.1057/jos.2009.25
Angler Y, Loßin A, Götz, O. Process flows of an emergency depart-ment. how process modelling and simulation can help improve the efficiency and quality of patient care. 2024. Available from: https://www.shs-conferences.org/articles/shsconf/pdf/2024/04/shsconf_shw2023_02009.pdf
DOI: https://doi.org/10.1051/shsconf/202418402009
Duguay C, Chetouane F. Modeling and improving Emergency De-partment systems using Discrete Event Simulation. Simulation 2007;83:311-20.
DOI: https://doi.org/10.1177/0037549707083111
Çetin SB, Cebeci F, Eray O. The effect of computer-based decision support system on emergency department triage: non-randomised controlled trial. Internat Emerg Nurs 2023;70:101341.
DOI: https://doi.org/10.1016/j.ienj.2023.101341
Fernandes M, Vieira SM, Leite F, et al. Clinical decision support sys-tems for triage in the emergency department using intelligent systems: a re-view. Artificial Intelligence Med 2020;102:101762.
DOI: https://doi.org/10.1016/j.artmed.2019.101762
Fitzgerald M, Cameron P, Mackenzie C, et al. Trauma resuscitation errors and computer-assisted decision support. Arch Surg 2011;146:218-25.
DOI: https://doi.org/10.1001/archsurg.2010.333
Tedesco D, Capodici A, Gribaudo G, et al. Innovative health technolo-gies to improve emergency department performance. Eur J Public Health 2022;32:ckac131.169.
DOI: https://doi.org/10.1093/eurpub/ckac131.169
Paul JA, Lin L. Models for improving patient throughput and waiting at hospital emergency departments. J Emerg Med 2012;43:1119-26.
DOI: https://doi.org/10.1016/j.jemermed.2012.01.063
Ramsey Z, Palter JS, Hardwick J, et al. Decreased nursing staffing adversely affects emergency department throughput metrics. Western J Emerg Med 2018;19:496-500.
DOI: https://doi.org/10.5811/westjem.2018.1.36327
Darraj A, Hudays A, Hazazi A, et al. The association between emer-gency department overcrowding and delay in treatment: a systematic review. Healthcare (Basel) 2023;11:385.
DOI: https://doi.org/10.3390/healthcare11030385
de la Roche MRP, Dyer N, Froats M, et al. Effect of a physician assis-tant on quality and efficiency metrics in an emergency department: Population cohort study. Can Fam Physician 2021;67:e61-7.
DOI: https://doi.org/10.46747/cfp.6702e61
King NMA, Habeeb M, Helps S. The contribution of physician asso-ciates or assistants to the emergency department: a systematic scoping review. J Am Coll Emerg Physicians Open 2023;4:e12989.
DOI: https://doi.org/10.1002/emp2.12989
Nahhas A, Awaldi A, Reggelin T. Simulation and the emergency de-partment overcrowding problem. Procedia Engineering 2017;178:368-76.
DOI: https://doi.org/10.1016/j.proeng.2017.01.068
Hajrizi E, Berisha D. Application of discrete events simulation for the department of emergency at Peja Regional Hospital Case Study Kosovo. IFAC-PapersOnLine 2019;52:376-81.
DOI: https://doi.org/10.1016/j.ifacol.2019.12.556
Castanheira-Pinto A, Gonçalves BS, Lima RM, et al. Modeling, as-sessment and de-sign of an emergency department of a public hospital through discrete-event simulation. Appl Sci 2021;11:805.
DOI: https://doi.org/10.3390/app11020805
Soldatenkova A, Calabrese A, Levialdi Ghiron N, et al. Emergency department performance assessment using administrative data: a managerial framework. PLoS One 2023;18:e0293401.
DOI: https://doi.org/10.1371/journal.pone.0293401
Taleb M, Khalid R, Ramli R, et al. An integrated approach of discrete event simulation and a non-radial super efficiency data envelopment analysis for performance evaluation of an emergency department. Expert Systems with Applications 2023;220:119653.
DOI: https://doi.org/10.1016/j.eswa.2023.119653
Purbey S, Mukherjee K, Bhar C. Performance measurement system for healthcare processes. Internat J Productivity Performance Manag 2007;56:241-51.
DOI: https://doi.org/10.1108/17410400710731446
Zaadoud B, Chbab Y, Chaouch A. Do performance measurement models have any impact on primary health care? A systematic review. Interna-tional J Health Governance 2020;25:319-34.
DOI: https://doi.org/10.1108/IJHG-04-2020-0039
Ouda E, Sleptchenko A, Simsekler MCE. Comprehensive review and future research agenda on discrete-event simulation and agent-based simula-tion of emergency departments. Simulation Modelling Practice Theory 2023;129:102823.
DOI: https://doi.org/10.1016/j.simpat.2023.102823
Duma D, Aringhieri R. Real-time resource allocation in the emergency department: a case study. Omega 2023;117:102844.
DOI: https://doi.org/10.1016/j.omega.2023.102844
Piliuk K, Tomforde S. Artificial intelligence in emergency medicine. A systematic literature review. Internat J Med Informatics 2023;180:105274.
DOI: https://doi.org/10.1016/j.ijmedinf.2023.105274
Abuhay TM, Robinson S, Mamuye A et al. Machine learning integrat-ed patient flow simulation: why and how? J Simulation 2023;17:580-93.
DOI: https://doi.org/10.1080/17477778.2023.2217334
Masoumian Hosseini M, Masoumian Hosseini ST, Qayumi K, et al. The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review. Arch Acad Emerg Med 2023;11:e38.
Chenais G, Lagarde E, Gil-Jardiné C. Artificial intelligence in emer-gency medicine: viewpoint of current applications and foreseeable opportuni-ties and challenges. J Med Internet Res 2023;25:e40031.
DOI: https://doi.org/10.2196/40031
Mittermaier M, Raza M, Kvedar JC. Collaborative strategies for de-ploying AI-based physician decision support systems: challenges and de-ployment approaches. NPJ Digit Med 2023;6:137.
DOI: https://doi.org/10.1038/s41746-023-00889-6
European Parliamentary Research Service. Artificial intelligence in healthcare - Applications, risks, and ethical and societal impacts. Panel for the Future of Science and Technology 2022. 2022. Available from: https://www.europarl.europa.eu/RegData/etudes/STUD/2022/729512/EPRS_STU(2022)729512_EN.pdf
How to Cite
Angler, Y., Lossin, A., & Goetz, O. (2024). The importance of discrete event simulation as a methodology for performance evaluation in the emergency department. Emergency Care Journal, 20(4). https://doi.org/10.4081/ecj.2024.12562
Copyright (c) 2024 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
