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Estimating the Patient's Price of Privacy in Liver Transplantation

Burhaneddin Sandkç, Lisa M. Maillart, Andrew J. Schaefer, Oguzhan Alagoz, Mark S. Roberts

The University of Chicago Booth School of Business, Chicago, Illinois 60637
Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
Department of Industrial and Systems Engineering, University of Wisconsin, Madison, Wisconsin 53706
Department of General Internal Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
burhan{at}uchicago.edu
maillart{at}pitt.edu
schaefer{at}ie.pitt.edu
alagoz{at}engr.wisc.edu
robertsm{at}upmc.edu

In the United States, patients with end-stage liver disease must join a waiting list to be eligible for cadaveric liver transplantation. Due to privacy concerns, the details of the composition of this waiting list are not publicly available. This paper considers the benefits associated with creating a more transparent waiting list. We study these benefits by modeling the organ accept/reject decision faced by these patients as a Markov decision process in which the state of the process is described by patient health, quality of the offered liver, and a measure of the rank of the patient in the waiting list. We prove conditions under which there exist structured optimal solutions, such as monotone value functions and control-limit optimal policies. We define the concept of the patient's price of privacy, namely, the number of expected life days lost due to the lack of complete waiting list information. We conduct extensive numerical studies based on clinical data, which indicate that this price of privacy is typically on the order of 5% of the optimal solution value.

Subject classifications: dynamic programming/optimal control; applications; Markov; health care; treatment.
History: Received January 2007; revision received December 2007; accepted January 2008.