Estimating the Patient's Price of Privacy in Liver Transplantation

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...

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Bibliographic Details
Published in:Operations research Vol. 56; no. 6; pp. 1393 - 1410
Main Authors: Sandikci, Burhaneddin, Maillart, Lisa M, Schaefer, Andrew J, Alagoz, Oguzhan, Roberts, Mark S
Format: Journal Article
Language:English
Published: Linthicum INFORMS 01.11.2008
Institute for Operations Research and the Management Sciences
Subjects:
Age groups
Analysis
applications
Blood groups
Decision analysis
Disease models
dynamic programming/optimal control
Economic aspects
Geography
health care
Hospitals
Human organs
Kidneys
Life expectancy
Liver
Liver diseases
Liver transplantation
Liver transplants
Markov
Markov analysis
Markov processes
Medical waiting lists
Modeling
Operations research
Optimal policy
Patients
Privacy
Studies
Surgeons
Transition probabilities
Transplantation
Transplants & implants
treatment
Waiting lists
United States
United States > US
ISSN:0030-364X, 1526-5463
Online Access:Get full text
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Summary: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.
Bibliography:SourceType-Scholarly Journals-1
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ISSN:0030-364X
1526-5463
DOI:10.1287/opre.1080.0648