Mitigating Information Asymmetry in Liver Allocation
Sepehr Nemati, Zeynep G. Icten, Lisa M. Maillart, Andrew J. Schaefer
INFORMS Journal on Computing · 2020 · Volume 32 · Issue 2
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
Enable JavaScript for citation tools and additional paper interactions.