The equitable and efficient allocation of scarce healthcare resources is a critical societal challenge, particularly evident during public health crises such as the COVID-19 pandemic. Traditional, ad-hoc rationing methods often lack transparency, consistency, and the capacity to systematically integrate complex ethical considerations. This article proposes an integrated framework for healthcare resource allocation that leverages advanced algorithmic mechanism design and matching theory. Drawing from established principles of efficiency (e.g., Pareto optimality, utilitarianism) and various facets of fairness (e.g., priority, non-discrimination, diversity, local justice), the framework employs algorithms such as bipartite matching and multi-attribute optimization to systematically distribute resources. Key results include enhanced transparency, optimized resource utilization, and the systematic integration of multiple, potentially competing, ethical values, all while maintaining computational feasibility and scalability. The discussion addresses the advantages over traditional methods, highlights the critical need for bias mitigation and public engagement in algorithmic design, and outlines limitations and areas for future research. The article concludes with policy implications, advocating for investment in research, clear ethical guidelines, robust data infrastructure, and interdisciplinary collaboration to ensure that algorithmic allocation systems are technically sound, ethically robust, and practically implementable for serving the collective good.

Healthcare rationing, resource allocation, mechanism design

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