Mitigating Information Asymmetry in Liver Allocation

Sepehr Nemati (), Zeynep G. Icten (), Lisa M. Maillart () and Andrew J. Schaefer ()
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Sepehr Nemati: Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida 32611
Zeynep G. Icten: bGNS Healthcare, Cambridge, Massachusetts 02139
Lisa M. Maillart: Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261
Andrew J. Schaefer: Department of Computational and Applied Mathematics, Rice University, Houston, Texas 77005

INFORMS Journal on Computing, 2020, vol. 32, issue 2, 234-248

Abstract: In accordance with the National Organ Transplant Act, which requires the efficient and equitable allocation of donated organs, the United Network for Organ Sharing (UNOS) prioritizes patients on the liver transplant waiting list within given geographic areas based mainly on their most recently reported health status. Accordingly, the UNOS requires patients to update their health status at a frequency that depends on their last reported health status. However, patients may elect to update any time within the required timeframe, which creates opportunities to game the system, leading to information asymmetries between the UNOS and the patients on the waiting list. This information asymmetry can be alleviated through more frequent updating requirements but at the price of an increased update burden (e.g., data collection costs and patient inconvenience). We propose a model that determines health reporting requirements that simultaneously minimize these two (possibly conflicting) criteria (i.e., inequity due to information asymmetry and update burden). Calibrating the model with clinical data, we examine (i) the degree to which an individual patient can benefit from the flexibility inherent to the current health reporting requirements and (ii) alternative recommendations that dominate the current requirements with respect to the two criteria of interest.

Keywords: health care; stochastic programming; organ transplantation; multi-objective optimization; information asymmetry; Markov decision processes (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:32:y:2020:i:2:p:234-248

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