Mass-Casualty Distribution for Emergency Healthcare: A Simulation Analysis

• Published in: International journal of disaster risk science Vol. 11; no. 3; pp. 364 - 377
• Main Authors: Jat, Mohsin Nasir, Rafique, Raza Ali
• Format: Journal Article
• Language: English
• Published: Beijing Beijing Normal University Press 01.06.2020; Springer; Springer Nature B.V; SpringerOpen
• Subjects: Casualties; Casualty distribution; Climate Change; Computer simulation; Discrete event systems; Distribution; Earth and Environmental Science; Earth Sciences; Emergency response; Environment; Health services; Hospitals; Landscape/Regional and Urban Planning; Load distribution; Load distribution (forces); Mass casualty incidents; Metropolitan areas; Natural Hazards; Pakistan; Queues; Simulation analysis; Stress concentration; Sustainable Development; Terrorism; Traffic delay; Travel time; Urban terrorism; Canada; Pakistan; Pakistan; Urban terrorism; Mass casualty incidents; Casualty distribution; Emergency response
• ISSN: 2095-0055; 2192-6395
• DOI: 10.1007/s13753-020-00260-3

This study focuses on the casualty-load distribution problem that arises when a mass casualty incident (MCI) necessitates the engagement of multiple medical facilities. Employing discrete event simulations, the study analyzed different MCI response regimes in Lahore, Pakistan, that vary in terms of the level of casualty-load distribution and the required coordination between the incident site and the responding hospitals. Past terrorist attacks in this major metropolitan area were considered to set up experiments for comparing delays in treatment under the modeled regimes. The analysis highlights that the number of casualties that are allowed to queue up at the nearest hospital before diverting the casualty traffic to an alternate hospital can be an important factor in reducing the overall treatment delays. Prematurely diverting the casualty traffic from the incident site to an alternate hospital can increase the travel time, while a delay in diversion can overload the nearest hospital, which can lead to overall longer waiting times in the queue. The casualty distribution mechanisms based only on the responding hospitals’ available capacity and current load can perform inefficiently because they overlook the trade-off between the times casualties spend in traveling and in queues.