Enhancing Emergency Response through Artificial Intelligence in Emergency Medical Services Dispatching; a Letter to Editor
Archives of Academic Emergency Medicine,
Vol. 11 No. 1 (2023),
15 November 2022
,
Page e60
https://doi.org/10.22037/aaem.v11i1.2097
Abstract
The emergency medical dispatcher (EMD) serves as a crucial link between individuals in need of emergency medical assistance and the emergency medical services (EMS) resource delivery system. Through their expertise and training, EMDs are able to accurately assess emergency situations, provide appropriate guidance over the phone, and dispatch the necessary EMS personnel to the scene. With adequate training, program management, supervision, and medical guidance, the EMD can accurately assess the caller's needs, choose an appropriate response approach, furnish relevant information to responders, and offer suitable assistance and guidance to patients through the caller. By diligently adhering to a written and medically approved EMD protocol, informed decisions regarding EMS responses can be made in a reliable, replicable, and fair manner (1, 2).
- Artificial intelligence
- Emergency medical services
- Emergency medical dispatcher
How to Cite
References
Crabb DB, Elmelige YO, Gibson ZC, Ralston DC, Harrell C, Cohen SA, et al. Unrecognized cardiac arrests: A one-year review of audio from emergency medical dispatch calls. Am J Emerg Med. 2022; 54:127-30.
Dong X, Ding F, Zhou S, Ma J, Li N, Maimaitiming M, et al. Optimizing an emergency medical dispatch system to improve prehospital diagnosis and treatment of acute coronary syndrome: Nationwide retrospective study in China. J Med Internet Res. 2022;24(11): e36929.
Ledziński Ł, Grześk G. Artificial Intelligence Technologies in Cardiology. J Cardiovasc Dev Dis. 2023;10(5).
Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Health J. 2021;8(2): e188-e94.
Scholz ML, Collatz-Christensen H, Blomberg SNF, Boebel S, Verhoeven J, Krafft T. Artificial intelligence in Emergency Medical Services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the Capital Region of Denmark as case in point. Scand J Trauma Resusc Emerg Med. 2022;30(1):36.
Chenais G, Lagarde E, Gil-Jardiné C. Artificial Intelligence in Emergency Medicine: Viewpoint of Current Applications and Foreseeable Opportunities and Challenges. J Med Internet Res. 2023;25:e40031.
Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. Artificial Intelligence in healthcare: Elsevier; 2020:25-60.
Chang I, Lee SC, Do Shin S, Song KJ, Ro YS, Park JH, et al. Effects of dispatcher-assisted bystander cardiopulmonary resuscitation on neurological recovery in paediatric patients with out-of-hospital cardiac arrest based on the pre-hospital emergency medical service response time interval. Resuscitation. 2018;130:49-56.
Byrsell F, Claesson A, Ringh M, Svensson L, Jonsson M, Nordberg P, et al. Machine learning can support dispatchers to better and faster recognize out-of-hospital cardiac arrest during emergency calls: a retrospective study. Resuscitation. 2021;162:218-26.
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