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dc.contributor.authorCaicedo Rolon, Alvaro Jr
dc.contributor.authorGuerrero Gómez, Gustavo
dc.contributor.authorMoreno Gamboa, Faustino
dc.coverage.spatialColombiaen_US
dc.date.accessioned2025-10-21T12:52:47Z
dc.date.available2025-10-21T12:52:47Z
dc.date.issued2024-01-31
dc.identifier.issn2461-4254en_US
dc.identifier.urihttps://repositorioinstitucional.ufpso.edu.co/xmlui/handle/20.500.14167/5827
dc.description.abstractLas salas de urgencias son una de las áreas más complejas y vitales de las instituciones de salud, donde se presentan problemas de congestión, largas esperas y largas estancias hospitalarias, lo que afecta la puntualidad, la capacidad de respuesta y la calidad del servicio. Esta investigación tuvo como objetivo diseñar un modelo detallado del flujo de pacientes para mejorar el rendimiento de las salas de urgencias utilizando redes de Petri jerárquicas y coloreadas. Posteriormente, se simuló el modelo para evaluar escenarios considerando decisiones tácticas como la planificación del personal médico, decisiones operativas como el ajuste de horarios de trabajo y decisiones estratégicas como el aumento de camas de observación. El mejor escenario reduciría los tiempos de espera promedio para los pacientes de triaje II en un 17,30 % y un 47,57 %, y para los de triaje III en un 33,49 % y un 43,49 % para consultas médicas en consultorio o sala de cirugía menor, respectivamente. Además, el tiempo de espera en observación y la tasa de pacientes que no fueron atendidos por un médico se reducirían en un 92,45 % y un 74,67 %, respectivamente. Estos resultados mejoran la calidad y la puntualidad del servicio y evitan poner en riesgo la salud y la vida del paciente. El modelo diseñado incluyó más atributos para los pacientes en relación con el lugar de atención médica en urgencias, el número de visitas al médico y el médico que atenderá al paciente. Además, el modelo de simulación incluye las camas de observación como un recurso limitado que impide el ingreso de nuevos pacientes. Finalmente, este modelo es una herramienta para ayudar a los administradores de urgencias a tomar decisiones a corto, mediano y largo plazo para abordar problemas como la sobrepoblación, los largos tiempos de espera y de estancia, y las altas tasas de pacientes que no son atendidos por un médico.en_US
dc.language.isoengen_US
dc.relationhttps://repositorio.ufps.edu.co/entities/publication/88757f19-adce-4fa4-b0b1-0b6d1ff95d3cen_US
dc.titleDiseño de un modelo para mejorar el rendimiento de las salas de urgencias utilizando una red de Petri coloreadaen_US
dc.title.translatedDesign of a model for improving emergency room performance using a colored Petri neten_US
dc.contributor.cvlacAlvaro Jr Caicedo Rolon [0001399791]en_US
dc.contributor.cvlacGustavo Guerrero Gómez [0001413037]en_US
dc.contributor.cvlacFaustino Moreno Gamboa [0001412451]en_US
dc.contributor.googlescholarAlvaro Jr Caicedo Rolon [CPFa7UsAAAAJ]en_US
dc.contributor.googlescholarGustavo Guerrero Gómez [La8nZsAAAAAJ]en_US
dc.contributor.orcidAlvaro Jr Caicedo Rolon [0000-0002-3651-3364]en_US
dc.contributor.orcidGustavo Guerrero Gómez [0000-0002-4316-7113]en_US
dc.contributor.orcidFaustino Moreno Gamboa [0000-0002-3586-4306]en_US
dc.contributor.researchgateAlvaro Jr Caicedo Rolon [Alvaro-Caicedo-Rolon]en_US
dc.contributor.researchgateFaustino Moreno Gamboa [Faustino-Moreno]en_US
dc.contributor.scopusAlvaro Jr Caicedo Rolon [57212574941]en_US
dc.contributor.scopusGustavo Guerrero Gómez [57205119123]en_US
dc.contributor.scopusFaustino Moreno Gamboa [57218627086]en_US
dc.description.abstractenglishEmergency rooms are one of the most complex and vital areas of healthcare institutions, which have presented overcrowding, long waiting, and length of stay times, affecting the timeliness, responsiveness, and quality of service. This research aimed to design a detailed patient flow model to improve emergency room performance using the hierarchical timed colored Petri nets. Then, the model was simulated to evaluate scenarios considering tactical decisions such as physician staff planning, operational decisions such as adjusting work schedules, and strategic decisions such as increasing observation beds. The best scenario would reduce the average waiting times for triage II patients by 17.30 % and 47.57 %, and triage III by 33.49 % and 43.49 % for medical consultation in the office or the minor surgery room, respectively. In addition, the waiting time in observation and the rate of patients left without being seen by a physician would be reduced by 92.45 % and 74.67 %, respectively. These results improve the quality and timeliness of the service and avoid putting the patient’s health and life at risk. The designed model included more attributes for patients concerning the place of medical care in the emergency room, the number of visits to the physician, and the physician who will care for the patient. Moreover, the simulation model includes observation beds as a limited resource blocking new patient admission. Finally, this model is a tool to support emergency room managers in making short, medium, and long-term decisions to address problems such as overcrowding, long waiting and length of stay times, and high rates of patients left without being seen by a physician.en_US
dc.subject.proposalAtención médicaen_US
dc.subject.proposalsimulaciónen_US
dc.subject.proposaltoma de decisionesen_US
dc.subject.proposalgestiónen_US
dc.subject.proposalhospitalariaen_US
dc.subject.proposallogísticaen_US
dc.subject.proposalemergenciasen_US
dc.subject.proposalcongestiónen_US
dc.subject.proposalmodeladoen_US
dc.subject.proposalpuntualidaden_US
dc.subject.keywordshealthcareen_US
dc.subject.keywordssimulationen_US
dc.subject.keywordsdecision-makingen_US
dc.subject.keywordsmanagementen_US
dc.subject.keywordshospitalen_US
dc.subject.keywordslogisticsen_US
dc.subject.keywordsemergencyen_US
dc.subject.keywordsovercrowdingen_US
dc.subject.keywordsmodelingen_US
dc.subject.keywordstimelinessen_US
dc.publisher.facultyFacultad ingenieríasen_US
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTen_US
dc.relation.referencesDerni, 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. https://doi.org/10.1504/ ijspm.2019.097717en_US
dc.relation.referencesSalimifard, K., Hosseini, S., Moradi, M. (2013). Improving Emergency Department Processes Using Coloured Petri Nets. Joint Proceedings of the International Workshop on Petri Nets and Software Engineering (PNSE’13) and the International Workshop on Modeling and Business Environments (ModBE’13) co-located with the 34th International Conference on Application and Theory of Petri Nets and Concurrency Petri Nets 2013. Vol. 989. Milano, 335–349. Available at: https://ceur-ws.org/Vol-989/ paper02b.pdfen_US
dc.relation.referencesCaicedo, A., Rivera, L. (2021). Caracterización de los servicios de urgencias de una ciudad fronteriza colombiana. Emergencia, 33, 239–241. Available at: https://revistaemergencias.org/wp-content/uploads/2023/08/Emergencias-2021_33_3_239-241.pdfen_US
dc.relation.referencesAboueljinane, L., Frichi, Y. (2022). A simulation optimization approach to investigate resource planning and coordination mechanisms in emergency systems. Simulation Modelling Practice and Theory, 119, 102586. https://doi.org/10.1016/j.simpat. 2022.102586en_US
dc.relation.referencesMSPS, M. de S. y P. S. (2019). Observatorio nacional de Calidad en Salud, Indicadores de calidad resolución 0256/16.en_US
dc.relation.referencesAshour, O. M., Okudan Kremer, G. E. (2014). Dynamic patient grouping and prioritization: a new approach to emergency department flow improvement. Health Care Management Science, 19 (2), 192–205. https://doi.org/10.1007/s10729-014-9311-1en_US
dc.relation.referencesAjami, S., Ketabi, S., Yarmohammadian, M., Bagherian, H. (2012). Wait Time in Emergency Department (ED) Processes. Medical Archives, 66 (1), 53. https://doi.org/10.5455/medarh.2012.66.53-57en_US
dc.relation.referencesLuscombe, R., Kozan, E. (2016). Dynamic resource allocation to improve emergency department efficiency in real time. European Journal of Operational Research, 255 (2), 593–603. https://doi.org/10.1016/j.ejor.2016.05.039en_US
dc.relation.referencesIbrahim, 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. AIP Conference Proceedings. https://doi.org/10.1063/1.4980949en_US
dc.relation.referencesM. de S. y P. S. MSPS (2015). Resolución 5596 del 2015. Por la cual se definen los criterios técnicos para el Sistema de Selección y Clasificación de pacientes en los servicios de urgencias ‘Triage’.en_US
dc.relation.referencesKuo, Y.-H., Rado, O., Lupia, B., Leung, J. M. Y., Graham, C. A. (2014). 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/s10696-014-9198-7en_US
dc.relation.referencesDiefenbach, 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.2010.1en_US
dc.relation.referencesSinreich, D., Jabali, O., Dellaert, N. P. (2012). Reducing emergency department waiting times by adjusting work shifts considering patient visits to multiple care providers. IIE Transactions, 44 (3), 163–180. https://doi.org/10.1080/0740817x.2011.609875en_US
dc.relation.referencesErshadi, M. M., Shafaeizadeh, A. (2021). Simulation-based improvement and modification for performances of hospitals: a case study. International Journal of Simulation and Process Modelling, 16 (2), 90. https://doi.org/10.1504/ijspm.2021.115857en_US
dc.relation.referencesDoudareva, E., Carter, M. (2021). Using Discrete Event Simulation to Improve Performance At Two Canadian Emergency Departments. 2021 Winter Simulation Conference (WSC). https://doi.org/10.1109/wsc52266.2021.9715411en_US
dc.relation.referencesBedoya-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/10.1177/0037549716673150en_US
dc.relation.referencesZhao, 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-06-2014-0060en_US
dc.relation.referencesCocke, S., Guinn, D., MacBlane, E., Walshak, S., Willenbrock, N., White, K. P. et al. (2016). UVA emergency department patient flow simulation and analysis. 2016 IEEE Systems and Information Engineering Design Symposium (SIEDS). https:// doi.org/10.1109/sieds.2016.7489282en_US
dc.relation.referencesDaldoul, D., Nouaouri, I., Bouchriha, H., Allaoui, H. (2015). Optimization on human and material resources in Emergency Department. 2015 International Conference on Industrial Engineering and Systems Management (IESM). https://doi.org/10.1109/ iesm.2015.7380224en_US
dc.relation.referencesHoot, N. R., LeBlanc, L. J., Jones, I., Levin, S. R., Zhou, C., Gadd, C. S., Aronsky, D. (2008). Forecasting Emergency Department Crowding: A Discrete Event Simulation. Annals of Emergency Medicine, 52 (2), 116–125. https://doi.org/10.1016/ j.annemergmed.2007.12.011en_US
dc.relation.referencesLee, S. (2014). The role of hospital selection in ambulance logistics. IIE Transactions on Healthcare Systems Engineering, 4 (2), 105–117. https://doi.org/10.1080/19488300.2014.914608en_US
dc.relation.referencesCastanheira-Pinto, A., Gonçalves, B., Lima, R. M., Dinis-Carvalho, J. (2021). Data Modelling and Validation of An Emergency Department Simulation Model-A Lean Healthcare Approach. Industrial Engineering and Operations Management, 1–11. https://doi.org/10.1007/978-3-030-78570-3_1en_US
dc.relation.referencesHosseini, S. M., Mohammadi, K., Yaghoubi, M. (2022). Optimization of Service Process in Emergency Department Using Discrete Event Simulation and Machine Learning Algorithm. Arch. Acad. Emerg. Med., 10 (1), e44.en_US
dc.relation.referencesGuasch Petit, A., Piera Eroles, M. A., Casanovas Garcia, J., Figueras Jové, J. (2003). Modelado y simulación : aplicación a procesos logísticos de fabricación y servicios. https://doi.org/10.5821/ebook-9788498802351en_US
dc.relation.referencesBanks, J., Carson, J. (1984). Discrete-event system simulation. New Yersey: Prentice Hall, INC.en_US
dc.relation.referencesGhomri, L., Alla, H. (2018). Continuous Petri nets and hybrid automata: two bisimilar models for the simulation of positive systems. International Journal of Simulation and Process Modelling, 13 (1), 24. https://doi.org/10.1504/ijspm.2018.090264en_US
dc.relation.referencesvan der Aalst, W. M. P., Stahl, C. (2011). Modeling Business Processes. The MIT Press. https://doi.org/10.7551/mitpress/ 8811.001.0001en_US
dc.relation.referencesBarad, M. (2003). Tutorial and Survey Articles: An introduction to Petri Nets. International Journal of General Systems, 32 (6), 565–582. https://doi.org/10.1080/03081070310001623366en_US
dc.relation.referencesJensen, K., Kristensen, L. M. (2009). Coloured Petri Nets. Springer Berlin Heidelberg. https://doi.org/10.1007/b95112en_US
dc.relation.referencesCeballos-Acevedo, T. M., Velásquez-Restrepo, P. A., Jaén-Posada, J. S. (2014). Duración de la estancia hospitalaria. Metodologías para su intervención. Gerencia y Políticas de Salud, 13 (27). https://doi.org/10.11144/javeriana.rgyps13-27.dehmen_US
dc.identifier.DOI10.21303/2461-4262.2024.002979en_US


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