Alleviating the Patient’S Price of Privacy Through a Partially Observable Waiting List
34 Pages Posted: 1 Aug 2011
Date Written: August 1, 2011
In the United States, end-stage liver disease patients join a waiting list and then make accept/reject decisions for transplantation as deceased-donor organs are offered to them over time. These decisions are largely influenced by the patient’s prospect for future offers, which can be ascertained most accurately by knowing the entire composition of the waiting list. Under the current transplantation system, however, UNOS (United Network for Organ Sharing), in an effort to strike a balance between privacy and transparency, only publishes an aggregated version of the waiting list. However, it is not clear whether the published information is good enough (compared to perfect information) to help patients make optimal decisions that maximize their individual life expectancies.
We model and analyze this accept/reject problem from an individual patient’s perspective using a partially observed Markov decision process (POMDP) framework, which incorporates the imperfect waiting list information as currently published into patient’s decision-making. In addition to analyzing structural properties of this model, we compare, in a clinically driven numerical study, the results of this model to those of an existing MDP model that differs from our model in assuming the availability of perfect waiting list information. This comparison allows us to assess the quality of the published imperfect information as measured by a patient’s so-called price of privacy (i.e., the opportunity loss in expected life days due to a lack of perfect waiting list information). Previous work estimates a percent loss of about 5%, on average, when a patient has no waiting list information compared to full information. In this paper, we find that the currently published partial information is nearly sufficient to eliminate this loss, resulting in a negligible price of privacy and supporting current UNOS practice.
Keywords: Dynamic programming, Partially and completely observable Markov decision process models, Medical decision making, Liver transplantation, Value of information
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