Type of publication:
Journal article
Author(s):
Shaltout, Amr Essam; Elfatih Elbadri, Mohammed; Kaur, Kiranjot; Alsharif, Mohammed M; Alkhazendar, Aliaa H; *Hassouba, Omar Nasr; Ahmad, Muhammad Nabeel; Osman, Mazin; Zahid, Areeba; Banjamin, Shaun.
Citation:
Cureus. 17(9):e92796, 2025 Sep.
Abstract:
This systematic review evaluated the accuracy and timeliness of global prehospital triage systems in mass disasters, following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020
guidelines. A comprehensive search of PubMed/MEDLINE, Embase, Scopus, and Cochrane Library up to June 2025 identified 344 records, of which four studies met eligibility criteria after screening and full-text assessment. Included studies analyzed conventional systems such as Simple Triage and Rapid Treatment (START), JumpSTART, Sort, Assess, Lifesaving Interventions, Treatment/Transport (SALT), and Modified Physiological Triage Tool (MPTT), as well as artificial intelligence (AI)-assisted approaches and diagnostic adjuncts like portable ultrasound. Sample sizes ranged from targeted reviews of 30-60 studies (systematic and evidence-based reviews) to practical evaluations of triage innovations involving prehospital and emergency responders. Data extraction captured accuracy, timeliness, and resource allocation, while risk of bias was assessed using the A Measurement Tool to Assess Systematic Reviews version 2 (AMSTAR-2) and the Scale for the Assessment of Narrative Review Articles (SANRA), with ratings ranging from low to moderate. Results demonstrated that traditional systems such as START and SALT provide rapid categorization but remain prone to over- and under-triage depending on responder training and situational factors. AI-driven models and portable diagnostic technologies significantly improved decision speed, diagnostic precision, and prioritization of life-saving interventions, reducing delays in critical care. Overall, while no single algorithm proved universally superior, integration of training, simulation-based preparedness, and emerging AI-supported tools was consistently associated with improved triage performance in chaotic, resource-limited disaster environments.
DOI: 10.7759/cureus.92796
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