BIO: Andrew Schaefer is Noah Harding Chair and Professor of Computational and Applied Mathematics at Rice University. Previously he was John Swanson Chair at the University of Pittsburgh. He received his PhD in Industrial and Systems Engineering from Georgia Tech in 2000. His research interests include stochastic optimization methodology and its application to health care problems. In particular, he is interested in optimizing decisions arising in the treatment of a variety of diseases, including end-stage liver disease, HIV/AIDS, influenza, and cancer.


In accordance with the National Organ Transplant Act (NOTA), 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, 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 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 (1) the degree to which an individual patient can benefit from the flexibility inherent to the current health reporting requirements, and (2) alternative recommendations that dominate the current requirements with respect to the two criteria of interest.