Estimating the Patient’s Price of Privacy in Liver Transplantation
Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA 15261
sandikci@ie.pitt.edu, maillart@pitt.edu, Schaefer@ie.pitt.edu
Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI 53706
alagoz@engr.wisc.edu
Department of General Internal Medicine, University of Pittsburgh, Pittsburgh, PA 15213
robertsm@upmc.edu
End-stage liver disease (ESLD), which includes diseases such as primary biliary
cirrhosis and hepatitis, is the 12th leading cause of death in the United States in
large part because transplantation is the only available therapy for ESLD. ESLD
patients must join a waiting list to be eligible for cadaveric liver transplantation.
When a cadaveric liver becomes available, it is offered to patients in this waiting list
using an allocation mechanism that prioritizes the patients based on their
characteristics and the characteristics of the available liver. However, mostly due to
privacy concerns, the details of the composition of this waiting list are not publicly
available. While this practice ensures some level of confidentiality, it also forces
patients to make accept/reject decisions with incomplete information. 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
percentage of expected life days gained due to the more transparent 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.
