Efficient and Fair Healthcare Rationing

The rationing of healthcare resources has emerged as an important issue, which has been discussed by medical experts, policy-makers, and the general public. We consider a rationing problem where medical units are to be allocated to patients. Each unit is reserved for one of several categories, and each category has a priority ranking over the patients. We present a class of allocation rules that respect the priorities, comply with the eligibility requirements, allocate the largest feasible number of units, and do not penalize agents for rising in the priority ranking of a category. The rules characterize all possible allocations that satisfy the first three properties and are polynomial-time computable.

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Publication Details

Journal
Journal of Artificial Intelligence Research
Published
2024-10-23
DOI
https://doi.org/10.1613/jair.1.15024
Citations
1
Primary Topic
Health Systems, Economic Evaluations, Quality of Life
Type
article
Field-Weighted Citation Impact
1.13

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article

Efficient and Fair Healthcare Rationing

Haris Aziz, Florian Brandl
1 citations
Journal of Artificial Intelligence Research
Health Systems, Economic Evaluations, Quality of Life
1.13
article

Efficient and Fair Healthcare Rationing

Haris Aziz, Florian Brandl
article en
1 citations

Abstract

The rationing of healthcare resources has emerged as an important issue, which has been discussed by medical experts, policy-makers, and the general public. We consider a rationing problem where medical units are to be allocated to patients. Each unit is reserved for one of several categories, and each category has a priority ranking over the patients. We present a class of allocation rules that respect the priorities, comply with the eligibility requirements, allocate the largest feasible number of units, and do not penalize agents for rising in the priority ranking of a category. The rules characterize all possible allocations that satisfy the first three properties and are polynomial-time computable.

Journal of Artificial Intelligence ResearchVol. 81
University of Bonn (DE), UNSW Sydney (AU)
National Science Foundation, Commonwealth Scientific and Industrial Research Organisation, Deutsche Forschungsgemeinschaft
Decent work and economic growth
Openalex Percentile: Top 11%
Health Systems, Economic Evaluations, Quality of Life
1.13
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