A Fair and Optimal Approach to Sequential Healthcare Rationing

TL;DR

This paper introduces new algorithms for fair and efficient healthcare resource allocation, ensuring priority respect and non-wastefulness, applicable to both sequential and simultaneous category processing, with proven uniqueness under strict precedence.

Contribution

It presents a simple, fast algorithm for healthcare rationing that satisfies key fairness axioms and extends to a general setting with a unique solution under strict category order.

Findings

The new algorithm is computationally faster than previous rules.

It satisfies four fundamental fairness axioms.

Under strict precedence, the rule is uniquely optimal.

Abstract

The COVID-19 pandemic underscored the urgent need for fair and effective allocation of scarce resources, from hospital beds to vaccine distribution. In this paper, we study a healthcare rationing problem where identical units of a resource are divided into different categories, and agents are assigned based on priority rankings. % We first introduce a simple and efficient algorithm that satisfies four fundamental axioms critical to practical applications: eligible compliance, non-wastefulness, respect for priorities, and maximum cardinality. This new algorithm is not only conceptually simpler but also computationally faster than the Reverse Rejecting rules proposed in recent work. % We then extend our analysis to a more general sequential setting, where categories can be processed both sequentially and simultaneously. For this broader framework, we introduce a novel algorithm that preserves the four fundamental axioms while achieving additional desirable properties that existing rules fail to satisfy. Furthermore, we prove that when a strict precedence order over categories is imposed, this rule is the unique mechanism that satisfies these properties.