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Principles of Scarce Medical Resource Allocation in Natural Disaster Relief

Author

Listed:
  • Hui Cao
  • Simin Huang

Abstract

Background. A variety of triage principles have been proposed. The authors sought to evaluate their effects on how many lives can be saved in a hypothetical disaster. Objective. To determine an optimal scarce resource–rationing principle in the emergency response domain, considering the trade-off between lifesaving efficiency and ethical issues. Method. A discrete event simulation model is developed to examine the efficiency of four resource-rationing principles: first come–first served, random, most serious first, and least serious first. Seven combinations of available resources are examined in the simulations to evaluate the performance of the principles under different levels of resource scarcity. Result. The simulation results indicate that the performance of the medical resource allocation principles is related to the level of the resource scarcity. When the level of the scarcity is high, the performances of the four principles differ significantly. The least serious first principle performs best, followed by the random principle; the most serious first principle acts worst. However, when the scarcity is relieved, there are no significant differences among the random, first come–first served, and least serious first principles, yet the most serious first principle still performs worst. Conclusion. Although the least serious first principle exhibits the highest efficiency, it is not ethically flawless. Considering the trade off between the lifesaving efficiency and the ethical issues, random selection is a relatively fair and efficient principle for allocating scarce medical resources in natural disaster responses.

Suggested Citation

  • Hui Cao & Simin Huang, 2012. "Principles of Scarce Medical Resource Allocation in Natural Disaster Relief," Medical Decision Making, , vol. 32(3), pages 470-476, May.
  • Handle: RePEc:sae:medema:v:32:y:2012:i:3:p:470-476
    DOI: 10.1177/0272989X12437247
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    Cited by:

    1. Santini, Alberto, 2021. "Optimising the assignment of swabs and reagent for PCR testing during a viral epidemic," Omega, Elsevier, vol. 102(C).
    2. Sanjay Mehrotra & Hamed Rahimian & Masoud Barah & Fengqiao Luo & Karolina Schantz, 2020. "A model of supply‐chain decisions for resource sharing with an application to ventilator allocation to combat COVID‐19," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(5), pages 303-320, August.
    3. Xiang Chu & QiuYan Zhong, 2015. "Post-earthquake allocation approach of medical rescue teams," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(3), pages 1809-1824, December.
    4. Yang Cao & Feng Zhen & Hao Wu, 2019. "Public Transportation Environment and Medical Choice for Chronic Disease: A Case Study of Gaoyou, China," IJERPH, MDPI, vol. 16(9), pages 1-21, May.
    5. Xinyu Zhang & Lin Zhao & Zhuang Cui & Yaogang Wang, 2015. "Study on Equity and Efficiency of Health Resources and Services Based on Key Indicators in China," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-15, December.
    6. Eduarda Asfora Frej & Lucia Reis Peixoto Roselli & Alexandre Ramalho Alberti & Murilo Amorim Britto & Evônio de Barros Campelo Júnior & Rodrigo José Pires Ferreira & Adiel Teixeira de Almeida, 2023. "Collaborative Decision Model for Allocating Intensive Care Units Beds with Scarce Resources in Health Systems: A Portfolio Based Approach under Expected Utility Theory and Bayesian Decision Analysis," Mathematics, MDPI, vol. 11(3), pages 1-15, January.

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