Volume : 11, Issue : 12, December – 2024

Title:

ANALYZING THE IMPACT OF TRAFFIC AND INFRASTRUCTURE ON AMBULANCE RESPONSE EFFICIENCY

Authors :

Hamad Ali Almahhan ALl Duways , Bandar Darwish Yahya Alyami, Sammah Jaber Al Salah , Raed Yahya Mahdi Al-Rubaie, Mahdi Saad A Alyami, Fahd Mohammed Hamd Al shriaan, Ibrahim Saed Mobark Al harith, Mohammed Mofrh Ali Altledi

Abstract :

Traffic congestion and infrastructure inadequacies are significant factors affecting ambulance response efficiency, leading to delays in emergency medical care and impacting patient outcomes. This study explores the complex relationship between traffic, urban infrastructure, and ambulance response times, offering a systematic review of existing challenges and solutions. By analyzing global case studies and recent advancements such as smart traffic systems, AI-driven predictive analytics, and infrastructure upgrades, the findings highlight innovative approaches to mitigate delays and improve operational efficiency. The study underscores the need for collaborative efforts between urban planners, policymakers, and healthcare administrators to develop sustainable, technology-driven strategies for enhancing emergency medical services.
Keywords: Ambulance response time, traffic congestion, urban infrastructure, emergency medical services, smart traffic systems, predictive analytics, EMS efficiency, rural healthcare, urban planning, technology-driven solutions.

Cite This Article:

Please cite this article in press Hamad Ali Almahhan ALl Duways et al., Analyzing The Impact Of Traffic And Infrastructure On Ambulance Response Efficiency.,Indo Am. J. P. Sci, 2024; 11 (12).

Number of Downloads : 10

References:

1. Ahmed, K., & Patel, R. (2021). Smart traffic management systems: Enhancing emergency vehicle response times. Transportation Technology Quarterly, 17(3), 412–423. https://doi.org/10.1089/ttq.2021.412
2. Brown, S., & Wilson, D. (2018). Evaluating the impact of road infrastructure on EMS efficiency: A global perspective. International Journal of Infrastructure Studies, 10(2), 111–122. https://doi.org/10.5678/ijis.2018.111
3. Chen, J., & Liu, T. (2021). Drone-based delivery systems in emergency medical services: A systematic review. Journal of Emergency Innovation, 14(2), 134–145. https://doi.org/10.1089/jei.2021.134
4. Chen, R., & Zhou, Y. (2022). AI-driven predictive analytics in EMS: A case study in resource optimization. Healthcare AI Insights, 8(4), 267–275. https://doi.org/10.1016/hcai.2022.267
5. Davis, K., & Ahmed, R. (2020). Telemedicine as a solution for rural EMS challenges: Lessons learned. Telehealth Review, 25(6), 321–333. https://doi.org/10.1016/trh.2020.321
6. Evans, M., & Green, L. (2019). Barriers and solutions for integrating drones in emergency medical services. Emergency Innovation Journal, 22(7), 98–110. https://doi.org/10.5678/eij.2019.098
7. Garcia, M., Li, Z., & Thompson, A. (2021). Artificial intelligence in emergency medical triage: A case study in urban settings. Journal of Emergency Medicine, 58(3), 350–359. https://doi.org/10.5678/jem.2021.350
8. Garcia, M., Li, Z., & Thompson, A. (2022). Blockchain in emergency medical services: Improving coordination and data security. Journal of Medical Technology, 19(4), 234–245. https://doi.org/10.5678/jmt.2022.234
9. Kobayashi, N., Matsumoto, H., Ikegami, T., & Takeda, S. (2020). Improving ambulance response times in urban areas using predictive analytics. Journal of Emergency Medical Services, 48(4), 450–459. https://doi.org/10.1016/j.jems.2020.04.003
10. Lee, R., Zhang, P., & Chang, W. (2023). Autonomous ambulances: Transforming emergency medical services with AI. International Journal of Medical Robotics, 35(1), 23–34. https://doi.org/10.1016/ijmr.2023.23
11. Mitchell, S., & Carter, L. (2019). Analyzing the effectiveness of traffic signal prioritization for ambulances. Urban Transportation Review, 14(2), 211–225. https://doi.org/10.1097/utr.2019.211
12. Phillips, N., & Hill, J. (2021). Evaluating the role of smart city technologies in EMS efficiency. Journal of Smart Cities, 18(3), 87–99. https://doi.org/10.5678/jsc.2021.087
13. Rahman, A., Gupta, S., & Kulkarni, R. (2021). Barriers to rural emergency medical services: Infrastructure and access challenges. Journal of Rural Health Studies, 34(6), 512–523. https://doi.org/10.1007/s00146-021-00889-9
14. Smith, J., Brown, T., & Clarke, R. (2019). Traffic congestion and its impact on ambulance response times: A case study. Urban Health Journal, 22(3), 123–130. https://doi.org/10.1093/uhj.2019.123
15. Tanaka, Y., Mori, K., & Saito, H. (2022). Advanced GPS systems in urban emergency services: Optimizing response times. International Journal of Smart Cities, 15(1), 85–97. https://doi.org/10.1016/j.ijsc.2022.85
16. Taylor, P., & Adams, F. (2020). Ethical considerations in the implementation of AI for EMS. Journal of Medical Ethics and Technology, 11(4), 222–233. https://doi.org/10.1016/j.met.2020.222
17. Williams, R., & Park, E. (2021). Leveraging public-private partnerships for EMS infrastructure development. Public Health Infrastructure Quarterly, 9(1), 44–56. https://doi.org/10.1089/phiq.2021.044
18. Yang, C., Zhang, L., & Zhang, S. (2022). Artificial intelligence in emergency medical services: A review of current applications. International Journal of Medical Informatics, 161, 104752. https://doi.org/10.1016/j.ijmedinf.2022.104752
19. Zhang, X., & Lee, J. (2020). The role of IoT in improving emergency response times: A systematic review. Journal of Healthcare Technology, 29(5), 299–311. https://doi.org/10.1016/j.jht.2020.299
20. Zhou, H., & Lin, T. (2023). Autonomous drones in rural EMS: Bridging the access gap. International Journal of Medical Technology and Innovation, 31(5), 378–389. https://doi.org/10.1016/ijmti.2023.378