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Empowered Analysis Using Artificial Intelligence Algorithms for a New Era of Health Sector Preparedness and Response Strategies to Chemical, Biological, Radiological, and Nuclear Major Incidents in the Middle East and North Africa

Published online by Cambridge University Press:  21 May 2025

Hassan Farhat Open the ORCID record for Hassan Farhat [Opens in a new window] ,
Gregory Ciottone ,
Guillaume Alinier ,
Alan M Batt ,
James Laughton ,
Mariana Helou ,
Nidaa Bajow  and
Mohamed Ben Dhiab
Hassan Farhat
Affiliation:
Hamad Medical Corporation Ambulance Service, Doha, Qatar Faculty of Medicine “Ibn Eljazzar” of Sousse, Sousse, Tunisia
Gregory Ciottone
Affiliation:
Harvard Medical School, Boston, United States
Guillaume Alinier
Affiliation:
Hamad Medical Corporation Ambulance Service, Doha, Qatar School of Health and Social Work, University of Hertfordshire, Hatfield, United Kingdom
Alan M Batt
Affiliation:
Faculty of Health Sciences, Queen’s University, Kingston, Ontario, Canada Institute for Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada Faculty of Medicine, Nursing and Health Sciences, Monash, Australia
James Laughton
Affiliation:
Hamad Medical Corporation Ambulance Service, Doha, Qatar
Mariana Helou
Affiliation:
School of Medicine, Lebanese American University, Beirut, Lebanon
Nidaa Bajow
Affiliation:
Security Forces Hospital, Riyadh, Saudi Arabia
Mohamed Ben Dhiab
Affiliation:
Faculty of Medicine “Ibn Eljazzar” of Sousse, Sousse, Tunisia
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Abstract

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Background/Introduction:

Chemical, biological, radiological, and nuclear (CBRN) incidents pose increasing transborder risks globally, necessitating enhanced health sector preparedness.

Objectives:

This study aimed to develop a comprehensive CBRN preparedness assessment tool (PAT), operational response guidelines (ORG), and tabletop simulation scenarios for the health sectors of the Middle East and North Africa (MENA) region.

Method/Description:

A mixed-methods approach comprised a systematic review of the literature up to 2022 in English and French, modified expert interviews (MIM), and an online Delphi questionnaire. Content analysis was performed on interview data. Using R-Studio™, consensus metrics and artificial intelligence techniques, including natural language processing, sentiment analysis, and unsupervised machine learning (ML) clustering algorithms, were deployed for advanced data analysis across all phases.

Results/Outcomes:

The literature review identified 63 relevant studies illustrating various preparedness strategies. The MIM’s thematic analysis, reinforced by AI-driven content analysis, emphasized the need for stronger inter-regional cooperation facilitated by organizations such as WHO and standardized tabletop simulation training. A robust consensus was achieved on the proposed assessment tool and operational response guidelines. ML analysis identified distinct expert clusters, providing additional consensus perspectives.

Conclusion:

The study emphasized the urgency for collaborative CBRN response strategies within MENA, valuing the innovative aspect of our suggested PAT, ORG, and simulation scenarios. This work advocates a dynamic, resilient approach to disaster medicine preparedness, which is crucial for regional security and global health resilience, especially in the MENA. It also highlights the significant role of AI analysis methods in enriching analytical outcomes in disaster medicine research and promoting data-informed preparedness strategies.

Type
Meeting Abstracts
Information
Prehospital and Disaster Medicine , Volume 40 , Supplement S1: Abstracts of the World Health Organization (WHO) 6th Emergency Medical Teams (EMT) Global Meeting 2024 , May 2025 , pp. s19
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of World Association for Disaster and Emergency Medicine