Archives of Academic Emergency Medicine. 2023; 11(1): e60

LE T T E R TO ED I TO R

Enhancing Emergency Response through Artificial Intelli-
gence in Emergency Medical Services Dispatching; a Letter
to Editor
Payam Emami1∗, Karim Javanmardi2

1. Department of Emergency Medical Sciences, Faculty of Paramedical Sciences, Kurdistan University of Medical Sciences, Sanandaj, Iran.

2. Education and Research Unit, Emergency Medical Service and Disaster Management Center, Urmia, Iran .

Received: June 2023; Accepted: July 2023; Published online: 22 August 2023

Cite this article as: Emami P, Javanmardi K. Enhancing Emergency Response through Artificial Intelligence in Emergency Medical Services

Dispatching; a Letter to Editor. Arch Acad Emerg Med. 2023; 11(1): e60. https://doi.org/10.22037/aaem.v11i1.2097.

Dear Editor
The emergency medical dispatcher (EMD) serves as a crucial

link between individuals in need of emergency medical as-

sistance 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 nec-

essary 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 informa-

tion to responders, and offer suitable assistance and guid-

ance 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 (AI) is the concept of a computer pro-

gram that utilizes existing information to make decisions

and enhances its performance based on accumulated expe-

rience. Machine learning (ML), a crucial aspect of AI, focuses

on developing systems that learn from past data to create

models for classification, clustering, or regression. ML al-

gorithms excel at recognizing patterns in intricate datasets,

making them valuable for interpreting outcomes and gener-

ating personalized clinical judgments (3). In recent years, ad-

vancements in AI have made a significant impact on various

industries, and EMS is no exception. AI has the potential to

revolutionize the way emergency medical dispatching is con-

ducted, improving response times, accuracy, and ultimately,

∗Corresponding Author: Payam Emami; Department of Emergency Medical
Sciences, Faculty of Paramedical Sciences, Kurdistan University of Medical Sci-
ences, Sanandaj, Iran. Email: p.emami@muk.ac.ir, Tel: +989188786115 , OR-
CID: https://orcid.org/0000-0002-1892-7539.

patient outcomes (4, 5).

AI has the potential to revolutionize how emergency calls are

managed and responded to, ultimately improving the quality

and efficiency of emergency medical care. Traditionally, EMS

dispatching has relied on human operators to receive emer-

gency calls, assess the situation, and dispatch appropriate re-

sources. However, this process can be time-consuming and

prone to human error, especially during high-pressure situa-

tions like out-of-hospital cardiac arrest when quick decisions

are crucial (5). AI-driven dispatching systems, equipped with

natural language processing and machine learning capabil-

ities, can analyze and prioritize incoming emergency calls

based on the severity and urgency of the situation. These sys-

tems can identify key information, such as location, symp-

toms, and available resources, and provide real-time recom-

mendations for dispatching appropriate medical teams or re-

sources (6). By leveraging AI, emergency call centers can op-

timize resource allocation, leading to faster response times

and improved patient outcomes. AI algorithms can analyze

historical data and ongoing trends to predict demand pat-

terns, enabling better deployment of EMS units and reduc-

ing response time variability. Additionally, AI can assist in

identifying potential cardiac events, strokes, or other med-

ical emergencies by analyzing voice patterns or vital signs

shared during emergency calls, facilitating early intervention

and potentially saving lives (7, 8).

ML system is able to recognize a higher proportion of out-of-

hospital cardiac arrest (OHCA) cases within the first minute

compared to human dispatchers. ML system has the poten-

tial to be a useful tool in emergency calls (9).

It is important to emphasize that while AI can improve emer-

gency dispatching, it should always complement, not re-

place, the human element. Trained professionals will con-

tinue to play a critical role in decision-making and providing

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P. Emami and K. Javanmardi 2

empathetic support during emergencies. AI should be seen

as a powerful tool to enhance their capabilities rather than a

substitute for their expertise. As AI continues to advance, it is

crucial for emergency medical services and policymakers to

embrace these technologies responsibly.

Safeguards must be in place to ensure data privacy, trans-

parency, and ongoing monitoring of AI systems to mitigate

biases and ensure equitable emergency response (5, 6).

1. Conclusion

The integration of AI in EMS dispatching has the potential to

revolutionize emergency care by optimizing resource alloca-

tion, reducing response times, and ultimately saving lives. It

is imperative that we invest in research, implementation, and

ongoing evaluation to maximize the benefits of AI while up-

holding ethical guidelines and maintaining the human touch

in emergency medical services.

2. Declarations

2.1. Acknowledgments

None.

2.2. Conflict of interest

The authors declare that they have no conflict of interest.

2.3. Funding and support

This research received no specific grant from any funding

agencies in the public, commercial, or non-profit sectors.

2.4. Authors’ contribution

PE and KJ: Conception and design of the work. PE: Drafting

the work and revising it critically for important intellectual

content and translation. PE and KJ: Read and approved the

final version and accountable for all aspects of the work.

2.5. Data Availability

Not applicable.

2.6. Using artificial intelligence chatbots

Not used in this article.

References

1. 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.

2. Dong X, Ding F, Zhou S, Ma J, Li N, Maimaitiming M, et al.

Optimizing an emergency medical dispatch system to im-

prove prehospital diagnosis and treatment of acute coro-

nary syndrome: Nationwide retrospective study in China.

J Med Internet Res. 2022;24(11): e36929.

3. Ledziński Ł, Grześk G. Artificial Intelligence Technologies

in Cardiology. J Cardiovasc Dev Dis. 2023;10(5).

4. Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence

in healthcare: transforming the practice of medicine. Fu-

ture Health J. 2021;8(2): e188-e94.

5. Scholz ML, Collatz-Christensen H, Blomberg SNF, Boebel

S, Verhoeven J, Krafft T. Artificial intelligence in Emer-

gency Medical Services dispatching: assessing the poten-

tial impact of an automatic speech recognition software

on stroke detection taking the Capital Region of Den-

mark as case in point. Scand J Trauma Resusc Emerg Med.

2022;30(1):36.

6. Chenais G, Lagarde E, Gil-Jardiné C. Artificial Intelligence

in Emergency Medicine: Viewpoint of Current Applica-

tions and Foreseeable Opportunities and Challenges. J

Med Internet Res. 2023;25:e40031.

7. Bohr A, Memarzadeh K. The rise of artificial intelligence

in healthcare applications. Artificial Intelligence in health-

care: Elsevier; 2020:25-60.

8. 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 pa-

tients with out-of-hospital cardiac arrest based on the pre-

hospital emergency medical service response time inter-

val. Resuscitation. 2018;130:49-56.

9. Byrsell F, Claesson A, Ringh M, Svensson L, Jonsson M,

Nordberg P, et al. Machine learning can support dispatch-

ers to better and faster recognize out-of-hospital cardiac

arrest during emergency calls: a retrospective study. Re-

suscitation. 2021;162:218-26.

This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0).
Downloaded from: https://journals.sbmu.ac.ir/aaem/index.php/AAEM/index


	Conclusion
	Declarations
	References