Decision Support System for Hospital Selection in Emergency Medical Services: A Discrete Event Simulation Approach
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32870
Decision Support System for Hospital Selection in Emergency Medical Services: A Discrete Event Simulation Approach

Authors: D. Tedesco, G. Feletti, P. Trucco


The present study aims to develop a Decision Support System (DSS) to support operational decisions in Emergency Medical Service (EMS) systems regarding the assignment of medical emergency requests to Emergency Departments (ED). This problem is called “hospital selection” and concerns the definition of policies for the selection of the ED to which patients who require further treatment are transported by ambulance. The employed research methodology consists of a first phase of review of the technical-scientific literature concerning DSSs to support the EMS management and, in particular, the hospital selection decision. From the literature analysis, it emerged that current studies mainly focused on the EMS phases related to the ambulance service and consider a process that ends when the ambulance is available after completing a mission. Therefore, all the ED-related issues are excluded and considered as part of a separate process. Indeed, the most studied hospital selection policy turned out to be proximity, thus allowing to minimize the travelling time and to free-up the ambulance in the shortest possible time. The purpose of the present study consists in developing an optimization model for assigning medical emergency requests to the EDs also considering the expected time performance in the subsequent phases of the process, such as the case mix, the expected service throughput times, and the operational capacity of different EDs in hospitals. To this end, a Discrete Event Simulation (DES) model was created to compare different hospital selection policies. The model was implemented with the AnyLogic software and finally validated on a realistic case. The hospital selection policy that returned the best results was the minimization of the Time To Provider (TTP), considered as the time from the beginning of the ambulance journey to the ED at the beginning of the clinical evaluation by the doctor. Finally, two approaches were further compared: a static approach, based on a retrospective estimation of the TTP, and a dynamic approach, focused on a predictive estimation of the TTP which is determined with a constantly updated Winters forecasting model. Findings reveal that considering the minimization of TTP is the best hospital selection policy. It allows to significantly reducing service throughput times in the ED with a negligible increase in travel time. Furthermore, an immediate view of the saturation state of the ED is produced and the case mix present in the ED structures (i.e., the different triage codes) is considered, as different severity codes correspond to different service throughput times. Besides, the use of a predictive approach is certainly more reliable in terms on TTP estimation, than a retrospective approach. These considerations can support decision-makers in introducing different hospital selection policies to enhance EMSs performance.

Keywords: Emergency medical services, hospital selection, discrete event simulation, forecast model.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 173


[1] Carvalho, A.S., Captivo, M.E. and Marques, I. 2020. Integrating the ambulance dispatching and relocation problems to maximize system’s preparedness. In European Journal of Operational Research, 283(3), pp. 1064–1080. doi:10.1016/j.ejor.2019.11.056.
[2] Reuter-Oppermann, Melanie, and Clemens Wolff. 2020. Towards a Unified Understanding of Data-Driven Support for Emergency Medical Service Logistics. In Proceedings of the 53rd Hawaii International Conference on System Sciences.
[3] Lavoie, G. et al. 2020. A Realistic Simulation Model of Montreal Emergency Medical Services. In Springer Proceedings in Mathematics and Statistics. Springer, pp. 67–78. doi:10.1007/978-3-030-39694-7_6.
[4] Knyazkov, K. et al. 2015. Evaluation of Dynamic Ambulance Routing for the Transportation of Patients with Acute Coronary Syndrome in Saint-petersburg. In Procedia Computer Science. Elsevier B.V., pp. 419–428. doi:10.1016/j.procs.2015.11.048.
[5] Caicedo Rolón ÁJ, Rivera Cadavid L. 2021. Hospital selection in emergency medical service systems: A literature review. In Revista Gerencia y Politicas de Salud 20:1f–1f.
[6] McLay, L.A. and Mayorga, M.E. 2013. A dispatching model for server-to-customer systems that balances efficiency and equity. In Manufacturing and Service Operations Management, 15(2), pp. 205–220. doi:10.1287/msom.1120.0411.
[7] McLay, L.A. and Mayorga, M.E. 2013. A model for optimally dispatching ambulances to emergency calls with classification errors in patient priorities. In IIE Transactions (Institute of Industrial Engineers), 45(1), pp. 1–24. doi:10.1080/0740817X.2012.665200.
[8] Sreekanth, V.K. and Roy, R.B. 2017. Equity-constrained dispatching models for emergency medical services. In Team Performance Management, 23(1–2), pp. 28–45. doi:10.1108/TPM-10-2015-0051.
[9] Park, S.H. and Lee, Y.H. 2019. Two-Tiered Ambulance Dispatch and Redeployment considering Patient Severity Classification Errors. In Journal of Healthcare Engineering, 2019. doi:10.1155/2019/6031789.
[10] Yoon, S. and Albert, L.A. 2021. Dynamic dispatch policies for emergency response with multiple types of vehicles. In Transportation Research Part E: Logistics and Transportation Review, 152. doi:10.1016/j.tre.2021.102405.
[11] Enayati, S. et al. 2018. Ambulance redeployment and dispatching under uncertainty with personnel workload limitations. In IISE Transactions, 50(9), pp. 777–788. doi:10.1080/24725854.2018.1446105.
[12] Haghani, A., Tian, Q. and Hu, H. 2004. Simulation Model for Real-Time Emergency Vehicle Dispatching and Routing. In Transportation Research Record: Journal of the Transportation Research.
[13] Lim, C.S., Mamat, R. and Braunl, T. 2011. Impact of ambulance dispatch policies on performance of emergency medical services. In IEEE Transactions on Intelligent Transportation Systems, pp. 624–632. doi:10.1109/TITS.2010.2101063.
[14] Van Buuren, M. et al. 2012. Evaluating dynamic dispatch strategies for emergency medical services: TIFAR simulation tool. In Proceedings - Winter Simulation Conference. doi:10.1109/WSC.2012.6465214.
[15] Gnanasekaran, A.M. et al. 2013. Impact of Patients Priority and Resource Availability in Ambulance Dispatching. In Proceedings of the 2013 Industrial and Systems Engineering Research Conference.
[16] Hafiz Azizan, M. et al. 2013. Simulation of Emergency Medical Services Delivery Performance Based on Real Map. In International Journal of Engineering and Technology (IJET), 5(3), pp. 2620–2627.
[17] Shin, K., Sung, I. and Lee, T. 2013. Emergency medical service system design evaluator. In Proceedings of the 2013 Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013, pp. 2410–2421. doi:10.1109/WSC.2013.6721615.
[18] Bandara, D., Mayorga, M.E. and Mclay, L.A. 2014. Priority dispatching strategies for EMS systems. In Journal of the Operational Research Society, 65(4), pp. 572–587. doi:10.1057/jors.2013.95.
[19] Li, X. and Saydam, C. 2016. Balancing ambulance crew workloads via a tiered dispatch policy. In Pesquisa Operacional, 36(3), pp. 399–419. doi:10.1590/0101-7438.2016.036.03.0399.
[20] Zarkeshzadeh, M. et al. 2016. A novel hybrid method for improving ambulance dispatching response time through a simulation study. In Simulation Modelling Practice and Theory, 60, pp. 170–184. doi:10.1016/j.simpat.2015.10.004.
[21] Amorim, M., Ferreira, S. and Couto, A. 2018. Emergency Medical Service Response: Analyzing Vehicle Dispatching Rules. In Transportation Research Record, 2672(32), pp. 10–21. doi:10.1177/0361198118781645.
[22] Aringhieri, R. et al. 2018. A Simulation and Online Optimization Approach For The Real-Time Management Of Ambulances. In Proceedings of the 2018 Winter Simulation Conference.
[23] Granberg, T.A. and Nguyen, H.T.N. 2018. Simulation Based Prediction Ofthe Near-Future Emergency Medical Services System State. In Proceedings of the 2018 Winter Simulation Conference.
[24] Bélanger, V. et al. 2020. A recursive simulation-optimization framework for the ambulance location and dispatching problem. In European Journal of Operational Research, 286(2), pp. 713–725. doi:10.1016/j.ejor.2020.03.041.
[25] Ridler, S., Mason, A.J. and Raith, A. 2021. A simulation and optimisation package for emergency medical services. In European Journal of Operational Research (Preprint). doi:10.1016/j.ejor.2021.07.038.
[26] Theeuwes, N., van Houtum, G.J. and Zhang, Y. 2021. Improving Ambulance Dispatching with Machine Learning and Simulation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH, pp. 302–318. doi:10.1007/978-3-030-86514-6_19.
[27] Chanta, S., Mayorga, M.E. and Mclay, L. 2012. A Hybrid Tabu Search for Locating and Dispatching Emergency Response Units Using an Imbedded Queuing Model. In Lim, G. and Herrmann, J.W. (eds) Proceedings of the 2012 Industrial and Systems Engineering Research Conference. Available at:
[28] Toro-Díaz, H. et al. 2013. Joint location and dispatching decisions for Emergency Medical Services. In Computers and Industrial Engineering, 64(4), pp. 917–928. doi:10.1016/j.cie.2013.01.002.
[29] Toro-Díaz, H. et al. 2015. Reducing disparities in large-scale emergency medical service systems. In Journal of the Operational Research Society, 66(7), pp. 1169–1181. doi:10.1057/jors.2014.83.
[30] López, J., Lanzarini, L. and de Giusti, A. 2011. Evolutionary multiobjective optimization for emergency medical services. In Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO ’11. New York, New York, USA: ACM Press, p. 83. doi:10.1145/2001858.2001907.
[31] Kleinoscheg, G. et al. 2014. Improving emergency medical dispatching with emphasis on mass-casualty incidents. In Studies in Health Technology and Informatics. IOS Press, pp. 108–115. doi:10.3233/978-1-61499-397-1-108.
[32] Repoussis, P.P. et al. 2016. Optimizing emergency preparedness and resource utilization in mass-casualty incidents. In European Journal of Operational Research, 255(2), pp. 531–544. doi:10.1016/j.ejor.2016.05.047.
[33] Díaz-Ramírez, J. and Baldoquin, M. 2017. Effects of ambulance dispatching and relocation decisions on EMS quality. In Proceedings of the International Conference on Industrial Engineering and Operations Management. Bogota, Colombia. Available at:
[34] Boujemaa, R. et al. 2020. Multi-period stochastic programming models for two-tiered emergency medical service system. In Computers and Operations Research, 123. doi:10.1016/j.cor.2020.104974.
[35] Golabian, H. et al. 2021. A multi-verse optimizer algorithm for ambulance repositioning in emergency medical service systems. In Journal of Ambient Intelligence and Humanized Computing
[Preprint]. doi:10.1007/s12652-021-02918-2.
[36] Gul, Muhammet, and Erkan Celik. 2020. “An Exhaustive Review and Analysis on Applications of Statistical Forecasting in Hospital Emergency Departments.” Health Systems.
[37] Kadri, Farid, Fouzi Harrou, and Ying Sun. 2018. “A Multivariate Time Series Approach to Forecasting Daily Attendances at Hospital Emergency Department.” 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings 2018-Janua (1): 1–6.
[38] Steins, Krisjanis, Niki Matinrad, and Tobias Andersson Granberg. 2019. “Forecasting the Demand for Emergency Medical Services.” Proceedings of the Annual Hawaii International Conference on System Sciences 2019-Janua: 1855–64.
[39] Bandara, Kasun, Christoph Bergmeir, Sam Campbell, Deborah Scott, and Dan Lubman. 2020. “Towards Accurate Predictions and Causal ‘What-If’ Analyses for Planning and Policy-Making: A Case Study in Emergency Medical Services Demand.” Proceedings of the International Joint Conference on Neural Networks.
[40] Khaldi, Rohaifa, Abdellatif El Afia, and Raddouane Chiheb. 2019. “Forecasting of Weekly Patient Visits to Emergency Department: Real Case Study.” Procedia Computer Science 148 (January): 532–41.
[41] Sudarshan, Vidya K., Mikkel Brabrand, Troels Martin Range, and Uffe Kock Wiil. 2021. “Performance Evaluation of Emergency Department Patient Arrivals Forecasting Models by Including Meteorological and Calendar Information: A Comparative Study.” Computers in Biology and Medicine 135 (January): 104541.
[42] Wang, Zhaonan, Tianqi Xia, Renhe Jiang, Xin Liu, Kyoung Sook Kim, Xuan Song, and Ryosuke Shibasaki. 2021. “Forecasting Ambulance Demand with Profiled Human Mobility via Heterogeneous Multi-Graph Neural Networks.” Proceedings - International Conference on Data Engineering 2021-April: 1751–62.
[43] Aboagye-Sarfo, Patrick, Qun Mai, Frank M. Sanfilippo, David B. Preen, Louise M. Stewart, and Daniel M. Fatovich. 2015. “A Comparison of Multivariate and Univariate Time Series Approaches to Modelling and Forecasting Emergency Department Demand in Western Australia.” Journal of Biomedical Informatics 57: 62–73.
[44] Afilal, Mohamed, Farouk Yalaoui, Frédéric Dugardin, Lionel Amodeo, David Laplanche, and Philippe Blua. 2016. “Forecasting the Emergency Department Patients Flow.” Journal of Medical Systems 40 (7).
[45] Aroua, Abdeljelil, and Georges Abdul-Nour. 2015. “Forecast Emergency Room Visits-a Major Diagnostic Categories Based Approach.” International Journal of Metrology and Quality Engineering 6 (2).
[46] Calegari, Rafael, Flavio S. Fogliatto, Filipe R. Lucini, Jeruza Neyeloff, Ricardo S. Kuchenbecker, and Beatriz D. Schaan. 2016. “Forecasting Daily Volume and Acuity of Patients in the Emergency Department.” Computational and Mathematical Methods in Medicine 2016.
[47] Chen, Albert Y., Tsung Yu Lu, Matthew Huei Ming Ma, and Wei Zen Sun. 2016. “Demand Forecast Using Data Analytics for the Preallocation of Ambulances.” IEEE Journal of Biomedical and Health Informatics 20 (4): 1178–87.
[48] Grekousis, George, and Ye Liu. 2019. “Where Will the next Emergency Event Occur? Predicting Ambulance Demand in Emergency Medical Services Using Artificial Intelligence.” Computers, Environment and Urban Systems 76 (April): 110–22.
[49] Jin, Ruidong, Tianqi Xia, Xin Liu, Tsuyoshi Murata, and Kyoung Sook Kim. 2021. “Predicting Emergency Medical Service Demand with Bipartite Graph Convolutional Networks.” IEEE Access 9: 9903–15.
[50] Zhou, Zhengyi, and David S. Matteson. 2016. “Predicting Melbourne Ambulance Demand Using Kernel Warping.” Annals of Applied Statistics 10 (4): 1977–96.