Volume : 11, Issue : 12, December – 2024
Title:
RESOURCE ALLOCATION MODELS FOR EMERGENCY MEDICAL SERVICES: A DECISION-MAKING FRAMEWORK
Authors :
Saeed Saleh Almansour. Mohsen Faheed Alharethi, Mohammed Saleh Ali Alabbas, Marzoq Moaili M Aldosari, Alatshan Hussain Hajwan, Hussain Hamad Al Haydar, Hussain Ali Hussain Almakayil, Almakayil Shutaywi Mohammed
Abstract :
Efficient resource allocation is a cornerstone of Emergency Medical Services (EMS), ensuring timely responses and optimal care delivery. This systematic review explores the development and application of resource allocation models, focusing on predictive analytics, optimization techniques, and machine learning frameworks. By synthesizing current research, the article identifies best practices and highlights challenges such as data quality, financial constraints, and geographic disparities. It proposes a comprehensive decision-making framework to balance operational efficiency and equity in EMS. The findings underscore the transformative potential of advanced models in enhancing response times, patient outcomes, and cost-effectiveness while addressing the dynamic needs of emergency healthcare systems.
Keywords: Resource allocation, Emergency Medical Services (EMS), decision-making frameworks, predictive analytics, optimization models, machine learning, healthcare efficiency, response times, operational equity, EMS system challenges.
Cite This Article:
Please cite this article in press Saeed Saleh Almansour et al., Resource Allocation Models For Emergency Medical Services: A Decision-Making Framework .,Indo Am. J. P. Sci, 2024; 11 (12).
Number of Downloads : 10
References:
1. Al-Shaqsi, S. et al. (2020). Agent-based simulation in EMS optimization. Simulation Modelling Practice and Theory. DOI: 10.1016/j.simpat.2020.101906
2. Brotcorne, L., Laporte, G., & Semet, F. (2003). Ambulance location and relocation models. European Journal of Operational Research. DOI: 10.1016/S0377-2217(02)00364-8
3. Chanta, S., Mayorga, M., & McLay, L. (2014). Improving emergency medical service response time through pre-positioning of ambulances. Operations Research for Health Care. DOI: 10.1016/j.orhc.2014.03.003
4. Charles Paulino de Oliveira, Elisangela Martins de Sá, Flávio Vinícius Cruzeiro Martins. (2020). A multi-period and bi-objective approach for locating ambulances: a case study in Belo Horizonte, Brazil. arXiv, https://doi.org/10.48550/arXiv.2012.06655
5. Church, R., & ReVelle, C. (2016). Maximal covering location model. Operations Research. DOI: 10.1287/opre.2016.1502
6. Huang, C., & Liu, S. (2017). Geographic accessibility of emergency medical services. Transportation Research Part D. DOI: 10.1016/j.trd.2017.07.008
7. Headrick, R.W., Morgan, G.W. Resource allocation in multifacility Emergency Medical Service Systems. J Med Syst 12, 121–128 (1988). https://doi.org/10.1007/BF00996634
8. Jennings, P. A., Cameron, P., & Walker, T. (2009). Adaptability of EMS systems during crises. Prehospital Emergency Care. DOI: 10.1080/10903120902706208
9. Jiang, S. et al. (2022). Predictive analytics for EMS using machine learning. Journal of Emergency Medical Informatics. DOI: 10.1007/s12245-022-00432-y
10. Jones, T., & Allen, R. (2021). Hybrid resource models for EMS efficiency. Journal of Health Economics and Management. DOI: 10.1016/j.hecm.2021.08.011
11. Kim, J. et al. (2020). AI applications in urban EMS. Asian Journal of Emergency Medicine. DOI: 10.1002/ajem.2020.1912
12. Kim, S., & Lee, J. (2021). Predictive analytics for emergency medical services. Journal of Health Analytics. DOI: 10.1016/j.jhealanal.2021.03.001
13. McCormack, R., Coates, T., & Kuo, T. (2018). Cost analysis of prehospital care using advanced allocation models. Journal of Emergency Medicine. DOI: 10.1016/j.jemermed.2018.03.021
14. Rajagopalan, H., & Saydam, C. (2015). A logistics approach to EMS allocation. Health Systems Management. DOI: 10.1007/s12273-015-0612-8
15. Rossi, F. et al. (2021). EMS adaptations during pandemics. European Journal of Emergency Medicine. DOI: 10.1097/MEJ.0000000000000793
16. Sharma, P. et al. (2019). GIS for rural EMS optimization. International Journal of Emergency Services. DOI: 10.1108/IJES-2019-0010
17. Takeda, H. et al. (2018). Impact of telemedicine-integrated EMS on patient outcomes. Journal of Telemedicine and Telecare. DOI: 10.1177/1357633X17751322
18. Wang, X., & Du, P. (2019). GIS-based optimization for EMS station placement. Transportation Research Part A. DOI: 10.1016/j.tra.2019.04.003
19. Xu, Fang, Mengfan Yan, Lun Wang, and Shaojian Qu. 2023. “The Robust Emergency Medical Facilities Location-Allocation Models under Uncertain Environment: A Hybrid Approach” Sustainability 15, no. 1: 624. https://doi.org/10.3390/su15010624
20. Yisha Xiang · Jun Zhuang, (2014). Amedical resource allocation model for serving emergency victims with deteriorating health conditions. Ann Oper Res (2016) 236:177–196. DOI 10.1007/s10479-014-1716-1.
21. Zhao, H. et al. (2022). Optimizing ambulance response times with real-time data. International Journal of Emergency Medicine. DOI: 10.1186/s12245-022-00432-y
22. Zhang, Y. et al. (2021). Hybrid decision-making frameworks for EMS. Health Systems Management. DOI: 10.1057/sjhs.2021.16