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ELICITING PATIENT PREFERENCES AND PLACING EXPEDITED ORGANS

Erkin, Zeynep (2012) ELICITING PATIENT PREFERENCES AND PLACING EXPEDITED ORGANS. Doctoral Dissertation, University of Pittsburgh.

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    Abstract

    Liver transplantation plays a crucial role in saving lives when no other alternatives exist. Each year approximately 5,500 liver transplants are performed in the US. However, annually still 2,000 lives are lost due to lack of livers. Much effort has been spent on improving the organ allocation system. In this dissertation, we focus on patient preference elicitation which is an essential component of medical decision models and expedited organ placement which is relatively unexplored component of the organ allocation system. When livers become available, they are offered to patients according to an order (match list) specified by a set of rules. Each patient can accept/reject the offer. Other researchers have considered this accept/decline decision. Estimating patient preferences over health states is an important component of these decision making models. Direct approaches, which involve asking patients abstract uestions, have significant drawbacks. We propose a new approach that infers patient preferences based on observed decisions via inverse optimization techniques. We illustrate our method on the timing of a living-donor liver transplant. If it appears that the standard allocation procedure will not result in a match before the organ becomes nonviable, the liver’s placement can be expedited, meaning that it is offered to a transplant center instead of an individual patient. We study the subsequent decision problem faced by a transplant center, namely which, if any, of its patients should receive the organ independent of their positions on the match list. We develop a simulation model and compare different policies for expedited liver placement. Our study indicates that a policy which gives higher priorities to patients whose likelihood of death is higher performs the best based on several metrics. We also formulate the transplant center’s decision problems as an average reward Markov Decision Process (MDP). Due to the complexity of the model, traditional methods used to solve MDP problems cannot be utilized for our model. Thus, we approximate the solution via Least Square Policy Iteration (LSPI) method. Despite the extensive search on basis functions, the LSPI method yields promising, yet not better outcomes than the policies found to be the best via simulation.


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    Item Type: University of Pittsburgh ETD
    ETD Committee:
    ETD Committee TypeCommittee MemberEmailORCID
    Committee ChairMaillart, Lisa M.maillart+@pitt.edu
    Committee MemberBailey, Matthewmdb025@bucknell.edu
    Committee MemberRajgopal, Jayantrajgopal@engr.pitt.edu
    Committee MemberRoberts, Mark S.mroberts@pitt.edu
    Committee MemberSchaefer, Andrew J.schaefer@pitt.edu
    Title: ELICITING PATIENT PREFERENCES AND PLACING EXPEDITED ORGANS
    Status: Published
    Abstract: Liver transplantation plays a crucial role in saving lives when no other alternatives exist. Each year approximately 5,500 liver transplants are performed in the US. However, annually still 2,000 lives are lost due to lack of livers. Much effort has been spent on improving the organ allocation system. In this dissertation, we focus on patient preference elicitation which is an essential component of medical decision models and expedited organ placement which is relatively unexplored component of the organ allocation system. When livers become available, they are offered to patients according to an order (match list) specified by a set of rules. Each patient can accept/reject the offer. Other researchers have considered this accept/decline decision. Estimating patient preferences over health states is an important component of these decision making models. Direct approaches, which involve asking patients abstract uestions, have significant drawbacks. We propose a new approach that infers patient preferences based on observed decisions via inverse optimization techniques. We illustrate our method on the timing of a living-donor liver transplant. If it appears that the standard allocation procedure will not result in a match before the organ becomes nonviable, the liver’s placement can be expedited, meaning that it is offered to a transplant center instead of an individual patient. We study the subsequent decision problem faced by a transplant center, namely which, if any, of its patients should receive the organ independent of their positions on the match list. We develop a simulation model and compare different policies for expedited liver placement. Our study indicates that a policy which gives higher priorities to patients whose likelihood of death is higher performs the best based on several metrics. We also formulate the transplant center’s decision problems as an average reward Markov Decision Process (MDP). Due to the complexity of the model, traditional methods used to solve MDP problems cannot be utilized for our model. Thus, we approximate the solution via Least Square Policy Iteration (LSPI) method. Despite the extensive search on basis functions, the LSPI method yields promising, yet not better outcomes than the policies found to be the best via simulation.
    Date: 02 February 2012
    Date Type: Publication
    Defense Date: 02 September 2011
    Approval Date: 02 February 2012
    Submission Date: 28 November 2011
    Release Date: 02 February 2012
    Access Restriction: No restriction; The work is available for access worldwide immediately.
    Patent pending: No
    Number of Pages: 136
    Institution: University of Pittsburgh
    Thesis Type: Doctoral Dissertation
    Refereed: Yes
    Degree: PhD - Doctor of Philosophy
    Uncontrolled Keywords: expedited organ placement, Markov Decision Process, Least Square Policy Iteration, health care, inverse optimization, quality-adjusted life years.
    Schools and Programs: Swanson School of Engineering > Industrial Engineering
    Date Deposited: 02 Feb 2012 12:14
    Last Modified: 16 Jul 2014 17:02

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