Rethinking Mass Casualty Distribution – Embedding a Resilient Hospital Selection Algorithm into a Mass Casualty Distribution Simulation Model

EasyChair Preprint no. 5757

Abstract

Considering hospitals’ resilience potential in addition to the shortest-distance and medical-care adequacy policy to distribute mass casualties (MC) is an imperative practice for mass casualty distribution decision making. This study developed a novel hospital selection algorithm composed of driving time from the disaster site to hospital, care adequacy, and mobilization ability to determine the best hospital choice for MC distribution. Next, we developed an MC distribution simulation model embedding the algorithm to generate optimized distribution decisions for various MC incident scenarios. The simulation model was tested by using the Formosa Fun Coast Dust Explosion. Regarding super overload on some responsible emergency hospitals in the FFCDE event, the model with mobilization ability shows a better-balanced distribution of mass casualties to the initial receiving hospitals than without the ability. The study findings can contribute to surge capacity planning and resource assessment of emergency medical services for future disasters.

Keyphrases: Discrete Event Simulation, mass casualty distribution, Resilience

@Booklet{EasyChair:5757,
  author = {Sheuwen Chuang and Chia-Hsin Cheng and Ching-An Lee},
  title = {Rethinking Mass Casualty Distribution – Embedding a Resilient Hospital Selection Algorithm into a Mass Casualty Distribution Simulation Model},
  howpublished = {EasyChair Preprint no. 5757},

  year = {EasyChair, 2021}}