Mass-Casualty Distribution for Emergency Healthcare: A Simulation Analysis

Abstract

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.

How ambulance commanders manage a mass casualty incident

“…Several studies into MCIs analyse responses in developing countries. Jat and Rafique (2020) consider alternative approaches to MCI in Pakistan in terms of when to move casualties from the site and the number of casualties that are allowed to queue at the nearest hospital before diverting casualties to another hospital. Similarly, Cook et al.…”

“…Jat and Rafique (2020) describe how rapid transfer of patients from the scene of an MCI reduces mortality and they address ways to tackle the casualty load distribution problem created by rapid scene clearance. The recommendation is to distribute patients amongst several hospitals according to patient pathway requirements defined during triage, priority of need for treatment and availability of capacity.…”

“…Practitioners including Lennquist (2012) and Van Vactor (2012) describe practical responses to MCIs. Academics also address how civilian services are to respond to an MCI, including Lockey and MacKenzie (2005) and Jat and Rafique (2020) cite a considerable number of references. Reports on particular MCI including Craigie et al.…”

“…Aylwin et al. (2007) described the tendency to “over-triage” on-site at MCIs resulting in the overloading of hospital trauma surge capacity and leading to repeated triage throughout the early stages of the patient pathway.House of Commons (2006) commented upon the speed and resources responding to MCIs which has been addressed together with the triage issue by the early mobilisation of the on-call local doctors on the MERIT.Jat and Rafique (2020) describe how rapid transfer of patients from the scene of an MCI reduces mortality and they address ways to tackle the casualty load distribution problem created by rapid scene clearance. The recommendation is to distribute patients amongst several hospitals according to patient pathway requirements defined during triage, priority of need for treatment and availability of capacity.…”

Simulation analysis of critical factors of casualty transportation for disaster response: a case study of Istanbul earthquake

“…In this study, we studied a large-scale disaster in an urban area. Jat and Rafique (2020) developed a simulation model to distribute the casualties to the medical centers in terrorist attacks.…”