Vol. 3 No. 2 (2023): Journal of Machine Learning for Healthcare Decision Support
Articles

Machine Learning Models for Enhancing Emergency Medical Services: AI Approaches for Optimizing Response Times, Triage Processes, and Resource Allocation

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  • Dr. Peter Murphy
Dr. Peter Murphy
Professor of Computer Science, Dublin City University, Ireland
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Published 15-11-2023

Keywords

  • Emergency Medical Services,
  • Optimizing Response Times,
  • Resource Allocation
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Peter Murphy, “Machine Learning Models for Enhancing Emergency Medical Services: AI Approaches for Optimizing Response Times, Triage Processes, and Resource Allocation”, Journal of Machine Learning for Healthcare Decision Support, vol. 3, no. 2, pp. 83–99, Nov. 2023, Accessed: Jan. 22, 2025. [Online]. Available: https://medlines.uk/index.php/JMLHDS/article/view/50

Abstract

Machine learning models have become an important aspect of enhancing emergency medical services (EMS) or prehospital care. The continuous advancements in artificial intelligence (AI) and big data technologies have facilitated the integration of smart and intelligent systems in the healthcare and medical response domain at every level, whether it is in the prehospital or hospital setting. Adoption and implementation of decision support tools are guided by big data-driven simulation, machine learning models, and robust communication platforms that aid in analyzing and predicting the right treatment options for patients, reducing peak overcrowding, optimizing resource utilization, improving healthcare quality, and enhancing patient stays in hospitals, bringing us one step closer to achieving big data and AI in the hospital workflow.

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