Volume : 11, Issue : 12, December – 2024
Title:
INTEGRATING AI IN EMERGENCY MEDICINE: A SYSTEMATIC REVIEW IN ENHANCING AMBULANCE DISPATCH AND TRIAGE SYSTEMS
Authors :
Ali Marjea Saleh Alsalem, Abdullah Abdulhadi M Alsalaim, Rashed Mofareh Ali Alquraish, Bandar Hussein Saleh Al-Yami, Eid Saeed Daiel Aldardab , Okashah Saleh Mahdi Al Harth, Saleh Mohammed S Alkhuram, Alhussain Ali Mana Al Zulayq
Abstract :
The integration of artificial intelligence (AI) into emergency medicine, particularly in ambulance dispatch and triage systems, is transforming the efficiency and effectiveness of pre-hospital care. This systematic review explores the applications and impact of AI technologies such as machine learning, predictive analytics, and natural language processing in enhancing response times, prioritizing critical cases, and optimizing resource allocation. The review synthesizes findings from recent studies to evaluate the benefits, challenges, and future directions of AI-driven solutions in emergency settings. Results indicate that AI significantly improves dispatch accuracy and triage efficiency, reducing delays and improving patient outcomes. However, challenges such as data integration, algorithmic transparency, and ethical considerations remain barriers to widespread adoption. The findings underscore the need for collaborative efforts to standardize AI implementation and address systemic challenges to fully realize its potential in emergency medicine.
Keywords: Artificial intelligence, emergency medicine, ambulance dispatch, triage systems, machine learning, predictive analytics, natural language processing, response times, healthcare optimization, pre-hospital care.
Cite This Article:
Please cite this article in press Ali Marjea Saleh Alsalem et al., Integrating ai in Emergency Medicine: A systematic Review In Enhancing Ambulance Dispatch And Triage Systems..,Indo Am. J. P. Sci, 2024; 11 (12).
Number of Downloads : 10
References:
1. Abdelrahman, H., Rodriguez, J. A., & Ghassemi, M. (2020). Artificial intelligence in emergency care: From prediction to intervention. Journal of Emergency Medicine Advances, 14(2), 125-139. https://doi.org/10.1016/j.jema.2020.05.003
2. Birkhead, G. S., & Gunn, J. K. (2019). Big data and AI for public health emergency response: Challenges and opportunities. American Journal of Public Health, 109(S4), S256-S262. https://doi.org/10.2105/AJPH.2019.305321
3. Chen, J., Lin, Z., & Zhang, X. (2021). The role of explainable AI in enhancing decision-making in emergency medicine. Journal of Medical Artificial Intelligence, 3(1), 21-30. https://doi.org/10.1016/j.jmai.2021.01.005
4. Cheng, F., Lin, X., & Zhang, Y. (2021). Artificial intelligence in emergency care: Improving efficiency and accuracy in ambulance dispatch and triage. Journal of Emergency Medicine Advances, 12(4), 215-229. https://doi.org/10.1016/j.jema.2021.04.005
5. Cournane, S., Conway, R., Byrne, D. G., & O’Riordan, D. (2020). Improved patient outcomes with optimized pre-hospital triage systems: The role of technology. International Journal of Emergency Medicine, 13(1), 45-53. https://doi.org/10.1186/s12245-020-00288-2
6. Feng, A., Miller, J., & Patel, R. (2020). Machine learning applications in pre-hospital emergency medicine: A systematic review. Computers in Biology and Medicine, 123, 103844. https://doi.org/10.1016/j.compbiomed.2020.103844
7. He, Z., Zhang, H., & Wang, X. (2018). Machine learning for real-time ambulance dispatch optimization: A review of methods and applications. Health Informatics Journal, 24(4), 363-376. https://doi.org/10.1177/1460458218779076
8. Keenan, P., O’Reilly, F., & Smith, R. (2021). Overcoming barriers to AI adoption in low-resource EMS settings. Global Health and Technology Review, 5(2), 78-85. https://doi.org/10.1016/j.ght.2021.05.003
9. Koster, R. W., Beesems, S. G., & van der Werf, C. (2020). Machine learning improves early recognition of cardiac arrest in emergency calls. Resuscitation, 155, 138-145. https://doi.org/10.1016/j.resuscitation.2020.07.007
10. Liu, D., Zhao, Y., & Wang, J. (2019). AI-based solutions for improving ambulance response times: A case study. Journal of Emergency Medicine and AI Research, 8(3), 214-223. https://doi.org/10.1016/j.emar.2019.03.002
11. MacFarlane, A., Delaney, J., & MacDonald, R. D. (2022). Artificial intelligence in ambulance services: Enhancing response times and decision-making. Prehospital Emergency Care, 26(3), 347-356. https://doi.org/10.1080/10903127.2022.2056734
12. McCarthy, M. E., & Davis, R. P. (2020). Predictive analytics in pre-hospital care: Improving triage efficiency with AI. Journal of Predictive Healthcare, 7(4), 288-301. https://doi.org/10.1177/1077558720908447
13. Rajasekaran, S., Anand, A., & Kumar, N. (2021). Ethical considerations in deploying AI for emergency medical services. Journal of Health Ethics, 34(1), 15-25. https://doi.org/10.1136/jhe.2021.112
14. Saxena, M., Pathak, A., & Sinha, R. (2020). AI-based resource allocation in emergency medical systems. Healthcare Informatics Research, 26(2), 158-167. https://doi.org/10.4258/hir.2020.26.2.158
15. Singh, N., & Gupta, S. (2021). AI-enhanced triage in emergency settings: A systematic review. Journal of Emergency Medicine and AI, 15(1), 98-110. https://doi.org/10.1007/s12245-021-008
16. Smith, P., Jones, L., & Thompson, G. (2020). Predictive modeling for EMS efficiency: AI applications in rural healthcare systems. Rural Health Review, 11(3), 183-194. https://doi.org/10.1177/146045822030007
17. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7
18. Wong, C. K., Ho, K., & Tam, H. (2021). Federated learning in emergency medicine: Balancing innovation with data privacy. Emerging Technologies in Healthcare, 8(2), 134-142. https://doi.org/10.1016/j.ethc.2021.04.008
19. Zhao, X., Liang, Y., & Feng, M. (2019). AI-driven decision support in pre-hospital care: A comprehensive review. AI in Healthcare Research, 21(3), 198-212. https://doi.org/10.1016/j.aihr.2019.05.003
20. Zhou, Q., Chen, Y., & Zhu, P. (2021). AI-powered triage: Enhancing pre-hospital decision-making in critical care. Critical Care Advances, 29(6), 412-425. https://doi.org/10.1016/j.cca.2021.06.007