Decision-making model for hospital selection in the emergency medical system

References

• Aalst, W. V D., & Stahl, C. (2011). Modeling business processes. A petri net-oriented approach. MIT Press.
   Google Scholar
• Aboueljinane, L., Sahin, E., Jemai, Z., & Marty, J. (2014). A simulation study to improve the performance of an emergency medical service: Application to the French Val-de-Marne department. Simulation Modelling Practice and Theory, 47, 46–59. https://doi.org/10.1016/j.simpat.2014.05.007
   Web of Science ®Google Scholar
• Acker, J. E., Pancioli, A. M., Crocco, T. J., Eckstein, M. K., Jauch, E. C., Larrabee, H., Meltzer, N. M., Mergendahl, W. C., Munn, J. W., Prentiss, S. M., Sand, C., Saver, J. L., Eigel, B., Gilpin, B. R., Schoeberl, M., Solis, P., Bailey, J. R., Horton, K. B., & Stranne, S. K. (2007). Implementation strategies for emergency medical services within stroke systems of care: A policy statement from the American Heart Association/American Stroke Association expert panel on emergency medical services systems and the stroke council. Stroke, 38(11), 3097–3115. https://doi.org/10.1161/STROKEAHA.107.186094
   PubMed Web of Science ®Google Scholar
• Acuna, J. A., Zayas-Castro, J. L., & Charkhgard, H. (2020). Ambulance allocation optimization model for the overcrowding problem in US emergency departments: A case study in Florida. Socio-Economic Planning Sciences, 71(October), 100747. https://doi.org/10.1016/j.seps.2019.100747
   Google Scholar
• Ajami, S., Ketabi, S., Yarmohammadian, M., & Bagherian, H. (2011). Waiting time in emergency department by simulation. In E. M. Borycki, J. A. Bartle-Clar, M. S. Househ, C. E. Kuziemsky, & E. G. Schraa (Eds.), International perspectives in health informatics (pp. 196–200). IOS Press. https://doi.org/10.3233/978-1-60750-709-3-196
   Google Scholar
• Allon, G., Deo, S., & Lin, W. (2013). The impact of size and occupancy of hospital on the extent of ambulance diversion: Theory and evidence. Operations Research, 61(3), 544–562. https://doi.org/10.1287/opre.2013.1176
   Web of Science ®Google Scholar
• Almehdawe, E., Jewkes, B., & He, Q. M. (2016). Analysis and optimization of an ambulance offload delay and allocation problem. Omega, 65, 148–158. https://doi.org/10.1016/j.omega.2016.01.006
   Web of Science ®Google Scholar
• Aringhieri, R., Bruni, M. E., Khodaparasti, S., & van Essen, J. T. (2017). Emergency medical services and beyond: Addressing new challenges through a wide literature review. Computers & Operations Research, 78, 349–368. https://doi.org/10.1016/j.cor.2016.09.016
   Web of Science ®Google Scholar
• Barad, M. (2003). An introduction to Petri nets. International Journal of General Systems, 32(6), 565–582. https://doi.org/10.1080/03081070310001623366
   Web of Science ®Google Scholar
• Bedoya-Valencia, L., & Kirac, E. (2016). Evaluating alternative resource allocation in an emergency department using discrete event simulation. Simulation, 92(12), 1041–1051. https://doi.org/10.1177/0037549716673150
   Google Scholar
• Budge, S., Ingolfsson, A., & Zerom, D. (2010). Empirical analysis of ambulance travel times: The case of Calgary emergency medical services. Management Science, 56(4), 716–723. https://doi.org/10.1287/mnsc.1090.1142
   Web of Science ®Google Scholar
• Bureau, U. S. C. (2021). Statistics for all states and counties, and for cities and towns with a population of 5,000 or more.
   Google Scholar
• Caicedo-Rolón, A. J., Bravo-Bastidas, J. J., & Rivera-Cadavid, L. (2022). Hospital selection and patient transport model in the emergency medical system. IISE Transactions on Healthcare Systems Engineering, 12(3), 232–244. https://doi.org/10.1080/24725579.2022.2053926
   Google Scholar
• Caicedo, A., & Rivera, L. (2021). Caracterización de los servicios de urgencias de una ciudad fronteriza colombiana. Emergencia, 33(33), 239–241.
   PubMedGoogle Scholar
• Caicedo, Á., & Rivera, L. (2021). Hospital selection in emergency medical service systems: A literature review/Selección de hospital en los sistemas de servicios médicos de emergencia: una revisión de literatura/Seleção de hospitais em sistemas de serviços médicos de emergência: uma revis. Revista Gerencia y Politicas de Salud, 20, 1f–1f.
   Google Scholar
• Cairns, C., Kang, K., & Santo, L. (2018). National hospital ambulatory medical care survey: 2018 emergency department summary tables. In Advance Data (Issue 372, p. 38).
   Google Scholar
• Chang, K. C., Tseng, M. C., & Tan, T. Y. (2004). Prehosplital delay after acute Stroke in Kaohsiung, Taiwan. Stroke, 35(3), 700–704. https://doi.org/10.1161/01.STR.0000117236.90827.17
   PubMed Web of Science ®Google Scholar
• Claudio, D., Ricondo, L., Freivalds, A., & Okudan Kremer, G. E. (2012). Physiological and descriptive variables as predictors for the Emergency Severity Index. IIE Transactions on Healthcare Systems Engineering, 2(2), 131–141. https://doi.org/10.1080/19488300.2012.680572
   Google Scholar
• Derni, O., Boufera, F., & Khelfi, M. F. (2019). Coloured Petri net for modelling and improving emergency department based on the simulation model. International Journal of Simulation and Process Modelling, 14(1), 72–86. https://doi.org/10.1504/IJSPM.2019.097717
   Google Scholar
• Diefenbach, M., & Kozan, E. (2011). Effects of bed configurations at a hospital emergency department. Journal of Simulation, 5(1), 44–57. https://doi.org/10.1057/jos.2010.1
   Web of Science ®Google Scholar
• DPH, D. of P. H. (2019). EMS Regions in Massachusetts. DPH, D. of P. H.
   Google Scholar
• Duma, D., & Aringhieri, R. (2023). Real-time resource allocation in the emergency department: A case study. Omega (United Kingdom), 117, 102844. https://doi.org/10.1016/j.omega.2023.102844
   Google Scholar
• Eachempati, S. R., Robb, T., Ivatury, R. R., Hydo, L. J., & Barie, P. S. (2002). Factors associated with mortality in patients with penetrating abdominal vascular trauma. The Journal of Surgical Research, 108(2), 222–226. https://doi.org/10.1006/jsre.2002.6543
   PubMed Web of Science ®Google Scholar
• El-Masri, S., & Saddik, B. (2012). An emergency system to improve ambulance dispatching, ambulance diversion and clinical handover communication -A proposed model. Journal of Medical Systems, 36(6), 3917–3923. https://doi.org/10.1007/s10916-012-9863-x
   PubMed Web of Science ®Google Scholar
• EMS, O. of E. M. S. (2021). DPH-approved statewide point of entry plans. EMS, O. of E. M. S.
   Google Scholar
• Enders, P. (2010). Applications of stochastic and queueing models to operational decision making Carnegie Mellon University. In ProQuest Dissertations and Theses (Issue April)
   Google Scholar
• Fields, E., Claudio, D., Okudan, G. E., Smith, C. A., & Freivalds, A. (2009). Triage Decision Making : Discrepancies in assigning the Emergency Severity Index [Paper presentation]. 699–705.
   Google Scholar
• Gnanasekaran, A. M., Moshref-Javadi, M., Zhong, H., Moghaddam, M., & Lee, S. (2013). Impact of patients priority and resource availability in ambulance dispatching. IIE Annual Conference and Expo 2013, 1727–1736.
   Google Scholar
• Golmohammadi, D. (2016). Predicting hospital admissions to reduce emergency department boarding. International Journal of Production Economics, 182, 535–544. https://doi.org/10.1016/j.ijpe.2016.09.020
   Web of Science ®Google Scholar
• Guasch, A., Piera, M. Á., Casanovas, J., & Figueras, J. (2005). Modelado y simulación. Aplicación a procesos logísticos de fabricación y servicios. Alfaomega.
   Google Scholar
• Ibrahim, I. M., Liong, C.-Y., Bakar, S. A., Ahmad, N., & Najmuddin, A. F. (2017). Minimizing patient waiting time in emergency department of public hospital using simulation optimization approach [Paper presentation]. 060005. https://doi.org/10.1063/1.4980949
   Google Scholar
• Jensen, K., & Kristensen, L. (2009). Coloured Petri Nets. Modelling and Validation of Concurrent Systems. Springer. https://doi.org/10.1128/AAC.03728-14
   Google Scholar
• Kidher, E., Krasopoulos, G., Coats, T., Charitou, A., Magee, P., Uppal, R., & Athanasiou, T. (2012). The effect of prehospital time related variables on mortality following severe thoracic trauma. Injury, 43(9), 1386–1392. https://doi.org/10.1016/j.injury.2011.04.014
   PubMed Web of Science ®Google Scholar
• Kim, S. H., & Whitt, W. (2014). Are call center and hospital arrivals well modeled by nonhomogeneous poisson processes? Manufacturing & Service Operations Management, 16(3), 464–480. https://doi.org/10.1287/msom.2014.0490
   Web of Science ®Google Scholar
• Knyazkov, K., Derevitsky, I., Mednikov, L., & Yakovlev, A. (2015). Evaluation of dynamic ambulance routing for the transportation of patients with acute coronary syndrome in Saint-petersburg. Procedia Computer Science, 66Issue(January), 419–428. In(Elsevier Masson SAS. https://doi.org/10.1016/j.procs.2015.11.048
   Google Scholar
• Kuo, Y. H., Rado, O., Lupia, B., Leung, J. M. Y., & Graham, C. A. (2016). Improving the efficiency of a hospital emergency department: A simulation study with indirectly imputed service-time distributions. Flexible Services and Manufacturing Journal, 28(1-2), 120–147. https://doi.org/10.1007/s10696-014-9198-7
   Web of Science ®Google Scholar
• Lee, S. (2014a). Role of parallelism in ambulance dispatching. IEEE Transactions on Systems, Man, and Cybernetics, 44(8), 1113–1122. https://doi.org/10.1109/TSMC.2013.2296280
   Web of Science ®Google Scholar
• Lee, S. (2014b). The role of hospital selection in ambulance logistics. IIE Transactions on Healthcare Systems Engineering, 4(2), 105–117. https://doi.org/10.1080/19488300.2014.914608
   Google Scholar
• Leo, G., Lodi, A., Tubertini, P., & Di Martino, M. (2016). Emergency department management in Lazio, Italy. Omega (United Kingdom), 58, 128–138. https://doi.org/10.1016/j.omega.2015.05.007
   Google Scholar
• Lowthian, J. A., Curtis, A. J., Jolley, D. J., Stoelwinder, J. U., McNeil, J. J., & Cameron, P. A. (2012). Demand at the emergency department front door: 10-year trends in presentations. The Medical Journal of Australia, 196(2), 128–132. https://doi.org/10.5694/mja11.10955
   PubMed Web of Science ®Google Scholar
• Masmoudi, M., Leclaire, P., Vincent, C., & Casalino, E. (2014). Modelling and simulation of the doctors’ availability in emergency department with SIMIO Software. Case of study: Bichat-Claude Bernard Hospital. In M. S. Abbes, J.-Y. Choley, F. Chaari, A. Jarraya, & M. Haddar (Eds.), Mechatronic systems: Theory and applications (p. 174). https://doi.org/10.1007/978-3-319-07170-1
   Google Scholar
• Menéndez, S., Navarrete, N., & Paz, A. (2017). Tiempos de respuesta del sistema prehospitalario en pacientes con sospecha diagnóstica de ataque cerebral en Bogotá durante los años 2013 y 2014. Acta Neurológica Colombiana, 33(3), 135–141. https://doi.org/10.22379/24224022147
   Google Scholar
• MSPS, M. de S. y P. S. (2019). Observatorio nacional de Calidad en Salud, Indicadores de calidad resolución 0256/16.
   Google Scholar
• Newgard, C. D., Schmicker, R., Hedges, J. R., Trickett, J. P., Davis, D. P., Bulger, E. M., Aufderheide, T. P., Minei, J. P., Hata, J. S., Gubler, K. D., Brown, T. B., Yelle, J.-D., Bardarson, B., & Nichol, G. (2010). Emergency medical services intervals and survival in Trauma: Assessment of the “Golden Hour” in a North American Prospective Cohort. Annals of Emergency Medicine, 55(3), 235–246.e4. https://doi.org/10.1016/j.annemergmed.2009.07.024
   PubMed Web of Science ®Google Scholar
• Nino, V., Valladares, L., & Claudio, D. (2023). Mitigating inconsistencies in the Emergency Severity Index. In Proceedings of the 2023 IISE Annual Conference, New Orleans, LA.
   Google Scholar
• Nino, V., Schiel, C., Bellows, B., & Claudio, D. (2020). Wearable devices and decision theory in the emergency triage process. Proceedings of the 2020 IISE Annual Conference, 1003–1008.
   Google Scholar
• Ramirez-Nafarrate, A., Fowler, J. W., & Wu, T. (2011). Design of centralized Ambulance Diversion policies using Simulation-Optimization. Proceedings of the 2011 Winter Simulation Conference S., 1251–1262. https://doi.org/10.1109/WSC.2011.6147846
   Google Scholar
• Rojas, V., Romero, L., Barrera, D., & Suárez, D. (2018). Selección de hospital destino para el traslado de urgencia de pacientes. Gerencia y Políticas de Salud, 17(35), 211–221. https://doi.org/10.11144/Javeriana.rgps17-35.shdt
   Google Scholar
• Salimifard, K., Hosseini, S., & Moradi, M. (2013). Improving emergency department processes using coloured Petri Nets. In D. Moldt & H. Rölke (Eds.), Joint Proceedings of: Application and Theory of Petri Nets (Vol. 989, pp. 335–349). Springer International Publishing. https://doi.org/10.1007/978-3-030-21571-2
   Google Scholar
• Sepehri, Z., Arabzad, S. M., & Sajadi, S. M. (2015). Analysing the performance of emergency department by simulation: The case of Sirjan Hospital. International Journal of Services and Operations Management, 20(3), 289. https://doi.org/10.1504/IJSOM.2015.067769
   Google Scholar
• Swaroop, M., Straus, D. C., Agubuzu, O., Esposito, T. J., Schermer, C. R., & Crandall, M. L. (2013). Pre-hospital transport times and survival for Hypotensive patients with penetrating thoracic trauma. Journal of Emergencies, Trauma, and Shock, 6(1), 16–20. https://doi.org/10.4103/0974-2700.106320
   PubMedGoogle Scholar
• Torres, M. B. (2012). An index to measure efficiency of hospital networks for mass casualty disasters. University of Central Florida.
   Google Scholar
• Tsai, C.-H., Weng, S.-J., Huang, C.-Y., Yang, C.-Y., Li, J.-Y., & Tsai, K.-T. (2010). The impact of different decision behavior models of emergency physicians on the performance of emergency departments. Proceedings of the 2010 Spring Simulation Multiconference on - SpringSim ’10, 1. https://doi.org/10.1145/1878537.1878614
   Google Scholar
• Zhang, X. L., Zhu, T., Luo, L., He, C. Z., Cao, Y., & Shi, Y. K. (2013). Forecasting emergency department patient flow using Markov chain. In 2013 10th International Conference on Service Systems and Service Management – Proceedings of ICSSSM 2013, 278–282. https://doi.org/10.1109/ICSSSM.2013.6602537
   Google Scholar
• Zhao, Y., Peng, Q., Strome, T., Weldon, E., Zhang, M., & Chochinov, A. (2015). Bottleneck detection for improvement of emergency department efficiency. Business Process Management Journal, 21(3), 564–585. https://doi.org/10.1108/BPMJ-06-2014-0060
   Web of Science ®Google Scholar