Mass-Casualty Distribution for Emergency Healthcare: A Simulation Analysis

Mohsin Nasir Jat; Raza Ali Rafique

doi:10.1007/s13753-020-00260-3

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Article Navigation > International Journal of Disaster Risk Science > 2020 > 11(3): 364-377

Mohsin Nasir Jat, Raza Ali Rafique. Mass-Casualty Distribution for Emergency Healthcare: A Simulation Analysis[J]. International Journal of Disaster Risk Science, 2020, 11(3): 364-377. doi: 10.1007/s13753-020-00260-3

Citation:

Mohsin Nasir Jat, Raza Ali Rafique. Mass-Casualty Distribution for Emergency Healthcare: A Simulation Analysis[J]. International Journal of Disaster Risk Science, 2020, 11(3): 364-377. doi: 10.1007/s13753-020-00260-3

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Mass-Casualty Distribution for Emergency Healthcare: A Simulation Analysis

doi: 10.1007/s13753-020-00260-3

Mohsin Nasir Jat¹,
Raza Ali Rafique^{2
,
,}

1 Edwards School of Business, University of Saskatchewan, Saskatoon, SK S7N5A7, Canada;
2 School of Management and Marketing, Kean University, Union, NJ 07083, USA

Funds:

The authors would like to acknowledge the support of Mayo, Jinnah, and General hospitals in Lahore, Pakistan with data gathering and model validation. The authors are also grateful to Mr. Sana Ullah, Research Associate at Lahore University of Management Sciences, for his assistance with this work.

Available Online: 2021-04-26

Abstract

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.
- Casualty distribution,
- Emergency response,
- Mass casualty incidents,
- Pakistan,
- Urban terrorism

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References(33)

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