Publicado jun 30, 2021



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Alvaro Junior Caicedo Rolón

Leonardo Rivera Cadavid

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Resumen

Los sistemas de servicios médicos de emergencia (SME) desempeñan una función fundamental en la sociedad al prestar un servicio vital en la atención inicial de urgencias. La investigación presenta la primera revisión de la literatura que estudia el problema de selección de hospital en los sistemas de SME. Los principales hallazgos fueron: la integración de la decisión de selección del hospital con la localización y el número de ambulancias, el despacho o el enrutamiento de las ambulancias. Los principales criterios de selección fueron la cercanía, las capacidades de atención del hospital y la fila más corta o mayor número de camas libres. Las medidas de desempeño más usadas fueron el menor tiempo de traslado y de espera. Las metodologías cuantitativas más aplicadas fueron la simulación de eventos discretos, los modelos de colas y la programación lineal entera mixta y los software CPLEX y Arena. La aplicación de metaheurísticas es escasa, se han implementado aplicaciones móviles y sistemas de información por internet para la selección del hospital en tiempo real. Se recomienda implementar el diseño de los métodos de selección de hospitales y los desarrollos tecnológicos, considerando la participación de los actores del sistema SME.

Keywords

Ambulâncias, emergências, administração de serviços de saúde, tecnologia da informaçãoAmbulances, emergencies, health services administration, information technologyAmbulancias, urgencias médicas, administración de los servicios de salud, tecnología de la información

References
1. Ministerio de Salud y Protección Social. Resolución 0926 del 30 de marzo de 2017. Por la cual se reglamenta el desarrollo y operación del Sistema de Emergencias Médicas. Bogotá; 2017.
2. Congreso de la Republica de Colombia. Ley 1438 del 19 enero de 2011. Bogotá; 2011.
3. Holtermann KA, Ross AG‎. Desarrollo de sistemas de servicios de emergencias médicas: experiencia de los Estados Unidos de América para países en desarrollo. Washington DC: PAHO; 2003.
4. Reuter-Oppermann M, van den Berg PL, Vile JL. Logistics for emergency medical service systems. Health Systems. 2017;6(3):187-208. https://doi.org/10.1057/s41306-017-0023-x
5. Barroeta J, Boada N. Los servicios de emergencia y urgencias médicas extra-hospitalarias en España. Madrid: Mensor; 2011.
6. Aringhieri R, Bruni ME, Khodaparasti S, Van Essen JT. Emergency medical services and beyond: Addressing new challenges through a wide literature review. Computers & Operations Research. 2017;78:349-368. https://doi.org/10.1016/j.cor.2016.09.016
7. Gholami-Zanjani S, Pishvaee M, Torab S. OR Models for Emergency Medical Service (EMS) Management. In: Kahraman C, et al. (eds). Operations Research Applications in Health Care Management. Springer; 2018. p.395-422.
8. Kergosien Y, Bélanger V, Soriano P, Gendreau M, Ruiz A. A generic and flexible simulation-based analysis tool for EMS management. Int J Prod Res. 2015;53(24):7299-7316. https://doi.org/10.1080/00207543.2015.1037405
9. Aboueljinane L, Sahin E, Jemai Z. A review on simulation models applied to emergency medical service operations. Comput Ind Eng. 2013;66(4):734-750. https://doi.org/10.1016/j.cie.2013.09.017
10. Rojas Cortés V, Romero L, Barrera D, Suárez DR. Selección de hospital destino para el traslado de urgencia de pacientes. Rev Gerenc Polit Salud. 2018;17(35):1-17. https://doi.org/10.11144/Javeriana.rgps17-35.shdt
11. Aboueljinane L, Sahin E, Jemai Z, Marty J. 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. 2014;47:46-59. https://doi.org/10.1016/j.simpat.2014.05.007
12. Gnanasekaran AM, Moshref-Javadi M, Zhong H, Moghaddam M, Lee S. Impact of patient’s priority and resource availability in ambulance dispatching. In IIE Annual Conference. Proceedings. Institute of Industrial and Systems Engineers (IISE); 2013.
13. Lee S. The role of hospital selection in ambulance logistics. IIE Transactions on Healthcare Systems Engineering. 2014;4(2):105-117. https://doi.org/10.1080/19488300.2014.914608
14. Laksono P, Wulan SR, Supangkat SH, Sunindyo WD. AHP and dynamic shortest path algorithm to improve optimum ambulance dispatch in emergency medical response. In ICT For Smart Society (ICISS), 2017 International Conference on. IEEE; 2017. p.1-6.
15. Knyazkov K, Derevitsky I, Mednikov L, Yakovlev A. Evaluation of dynamic ambulance routing for the transportation of patients with acute coronary syndrome in Saint-Petersburg. Procedia Computer Science. 2015;66:419-428. https://doi.org/10.1016/j.procs.2015.11.048
16. Rodríguez AK, Osorno GM, Maya PA. Relocalización de vehículos en servicios de emergencias médicas: una revisión. Ingeniería y Ciencia. 2016;12(23):163-202. https://doi.org/10.17230/ingciencia.12.23.9
17. López M, Vinicio H. Modelo cuantitativo para la localización de ambulancias de gestión sanitaria y su impacto en los tiempos de arribo, coordinadas por el Centro Local ECU911 Macas dentro de la provincia de Morona Santiago. Tesis de maestría. Ambato: Universidad Técnica de Ambato; 2017.
18. Tlili T, Harzi M, Krichen S. Swarm-based approach for solving the ambulance routing problem. In International Knowledge Based and Intelligent Information and Engineering Systems, KES2017, 6-8 September. Marseille: Procedia Computer Science; 2017. p.350-357.
19. Phyo KZ, Sein MM. Optimal Route Assessment for Emergency Vehicles Travelling on Complex Road Network. In: International Workshop on Multi-disciplinary Trends in Artificial Intelligence. Cham: Springer; 2017. p.380-390.
20. Azizan M, Lim C, Hatta W, Go L, Teoh S. Simulation of emergency medical services delivery performance based on real map. Int J Eng Technol. 2013;5(3):2620-2627. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.411.2313&rep=rep1&type=pdf
21. Fernández-Altuna M, Martínez del Prado A, Arriarán Rodríguez E, Gutiérrez Rayón D, Toriz Castillo, H & Lifshitz Guinzberg A. Uso de los MeSH: una guía práctica. Investigación en educación médica. 2016;5(20):220-229. https://doi.org/10.1016/j.riem.2016.02.004
22. Leo G, Lodi A, Tubertini P, Di Martino M. Emergency department management in Lazio, Italy. Omega. 2016;58:128-138. https://doi.org/10.1016/j.omega.2015.05.007
23. Yang D, Liu M, Su Q. Cost-effective analyses of joint planning in emergency medical services: A case study. In Service Systems and Service Management (ICSSSM), 2016 13th International Conference on. IEEE; 2016. p.1-6.
24. Wears RL, Winton CN. Simulation modeling of prehospital trauma care. In Simulation Conference Proceedings, 1993. Winter. IEEE; 1993. p.1216-1224.
25. Sung I, Lee T. Optimal allocation of emergency medical resources in a mass casualty incident: Patient prioritization by column generation. Eur J Oper Res. 2016;252(2):623-634. https://doi.org/10.1016/j.ejor.2016.01.028
26. Almehdawe E, Jewkes B, He QM. Analysis and optimization of an ambulance offload delay and allocation problem. Omega. 2016;65:148-158.
27. Bull M. An Index to Measure Efficiency Of Hospital Networks for Mass Casualty Disasters. Doctoral dissertation. University of Central Florida; 2012.
28. Wang Y, Luangkesorn KL, Shuman L. Modeling emergency medical response to a mass casualty incident using agent-based simulation. Socio-Economic planning sciences. 2012;46(4):281-290. https://doi.org/10.1016/j.seps.2012.07.002
29. Chi CL. Medical decision support systems based on machine learning. Doctoral dissertation. University of Iowa; 2009.
30. Enders P. Applications of stochastic and queueing models to operational decision making. Doctoral dissertation. Carnegie Mellon University; 2010.
31. Vijayalakshmi C, Anitha N. Design of an Optimization Routing Model for Real Time Emergency Medical Service System in Chennai Using Fuzzy Techniques. Jude D, Smys S (eds). Computational Vision and Bio Inspired Computing. Cham: Springer; 2018. p.266-279.
32. Househ MS, Yunus F. Emergency Department Waiting Times (EDWaT): A patient flow management and quality of care rating mHealth application. In ICIMTH; 2014. p.229-232.
33. Nimbalkar RA, Fadnavis RA. Domain specific search of nearest hospital and Healthcare Management System. In Engineering and Computational Sciences (RAECS), 2014 Recent Advances in. IEEE; 2014. p.1-5.
34. Jotshi A, Gong Q, Batta R. Dispatching and routing of emergency vehicles in disaster mitigation using data fusion. Socio-Econ Plan Sci. 2009;43(1):1-24. https://doi.org/10.1016/j.seps.2008.02.005
35. Mirino AE, Risald, Suyoto. Best routes selection using Dijkstra and Floyd-Warshall algorithm. In Information & Communication Technology and System (ICTS), 2017 11th International Conference on. IEEE; 2017. p.155-158.
36. Katayama Y, Kitamura T, Kiyohara K, Iwami T, Kawamura T, Izawa, J, et al. Improvements in patient acceptance by hospitals following the introduction of a smartphone app for the emergency medical service system: A population-based before-and-after observational study in Osaka City, Japan. JMIR mHealth and uHealth; 2017;5(9). https://doi.org/10.2196/mhealth.8296
37. Schooley BL, Murad A, Abed Y, Horan TA. A mHealth system for patient handover in emergency medical services. In ISCRAM; 2013.
38. Holtmann C, Müller-Gorchs M, Rashid A, Weidenhaupt K, Ziegler V, et al. Medical opportunities by mobile IT usage–a case study in the stroke chain of survival. In: European Conference on eHealth 2007; 2007.
39. Sprivulis P, Gerrard B. Internet-accessible emergency department workload information reduces ambulance diversion. Prehosp Emerg Care. 2005;9(3):285-291. https://doi.org/10.1080/10903120590962094
40. El-Masri S, Saddik B. An emergency system to improve ambulance dispatching, ambulance diversion and clinical handover communication. A proposed model. J Med Syst. 2012;36(6):3917-3923. https://doi.org/10.1007/s10916-012-9863-x
41. Poulymenopoulou M, Malamateniou F, Vassilacopoulos G. Emergency healthcare process automation using mobile computing and cloud services. J Med Syst. 2012;36(5):3233-3241. https://doi.org/10.1007/s10916-011-9814-y
42. Ramírez-Nafarrate A, Fowler JW, Wu T. Design of centralized ambulance diversion policies using simulation-optimization. In Proceedings of the Winter Simulation Conference. Winter Simulation Conference; 2011. p.1251-1262.
43. Shin K, Sung I, Lee T. Emergency medical service system design evaluator. In Proceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World. IEEE Press; 2013. p.2410-2421.
44. Ingolfsson A, Erkut E, Budge S. Simulation of single start station for Edmonton EMS. J Oper Res Soc. 2003;54(7):736-746. https://doi.org/10.1057/palgrave.jors.2601574
45. Stein OA. Emergency medical service response system performance in an urban South African setting: a computer simulation model. Doctoral dissertation. University of Cape Town; 2014.
46. Su S, Shih CL. Resource reallocation in an emergency medical service system using computer simulation. The American journal of emergency medicine. 2002;20(7):627-634. https://doi.org/10.1053/ajem.2002.35453
47. Repede JF, Bernardo JJ. Developing and validating a decision support system for locating emergency medical vehicles in Louisville, Kentucky. Eur J Oper Res. 1994;75(3):567-581. https://doi.org/10.1016/0377-2217(94)90297-6
48. Su S, Shih CL. Modeling an emergency medical services system using computer simulation. Int J Med Inform. 2003;72(1-3):57-72. https://doi.org/10.1016/j.ijmedinf.2003.08.003
49. Chockalingam A, Jayakumar K, Lawley MA. A stochastic control approach to avoiding emergency department overcrowding. In Simulation Conference (WSC), Proceedings of the 2010 Winter. IEEE; 2010. p.2399-2411.
50. Van Buuren M, van der Mei R, Aardal K, Post H. Evaluating dynamic dispatch strategies for emergency medical services: TIFAR simulation tool. In Simulation Conference (WSC), Proceedings of the 2012 Winter. IEEE; 2012.
51. Aringhieri R, Carello G, Morale D. Ambulance location through optimization and simulation: The case of Milano urban area; 2007.
52. Lee T, Cho SH, Jang H, Turner JG. A simulation-based iterative method for a trauma center – Air ambulance location problem. In Simulation Conference (WSC), Proceedings of the 2012 Winter. IEEE; 2012.
53. Newgard CD, Mann NC, Hsia RY, Bulger EM, Ma OJ, Staudenmayer K, et al. Patient choice in the selection of hospitals by 9‐1‐1 emergency medical services providers in trauma systems. Acad Emerg Med. 2013;20(9):911-919. https://doi.org/10.1111/acem.12213
54. Newgard, CD, Nelson MJ, Kampp M, Saha S, Zive D, Schmidt T, et al. Out-of-hospital decision-making and factors influencing the regional distribution of injured patients in a trauma system. J Trauma. 2011;70(6):1345. https://doi.org/10.1097/TA.0b013e3182191a1b
55. Vandeventer S, Studnek JR, Garrett JS, Ward SR, Staley K, Blackwell T. The association between ambulance hospital turnaround times and patient acuity, destination hospital, and time of day. Prehosp Emerg Care. 2011;15(3):366-370. https://doi.org/10.3109/10903127.2011.561412
56. Velásquez-Restrepo PA, Rodríguez-Quintero AK, Jaén-Posada, JS. Metodologías cuantitativas para la optimización del servicio de urgencias: Una revisión de la literatura. Rev Gerenc Polit Salud. 2011;10(21):196-218. https://www.redalyc.org/pdf/545/54522293012.pdf
57. Hoot NR, Aronsky D. Systematic review of emergency department crowding: Causes, effects, and solutions. Ann Emerg Med. 2008;52(2):126-136. https://doi.org/10.1016/j.annemergmed.2008.03.014
58. Aringhieri R, Carello G, Morale D. Supporting decision making to improve the performance of an Italian Emergency Medical Service. Ann Oper Res. 2016;236(1):131-148. https://doi.org/10.1007/s10479-013-1487-0
Cómo citar
Caicedo Rolón, A. J., & Rivera Cadavid, L. (2021). Selección de hospital en los sistemas de servicios médicos de emergencia: una revisión de literatura. Gerencia Y Políticas De Salud, 20. https://doi.org/10.11144/Javeriana.rgps20.hsem
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