ICTEN, ZEYNEP GOZDE
(2012)
MARKOV DECISION PROCESS MODELS FOR IMPROVING EQUITY IN LIVER ALLOCATION.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
In the United States, end-stage liver disease (ESLD) patients are prioritized primarily by their Model for End-stage Liver Disease score (MELD) to receive organ offers. Therefore, patients are required to update their MELD score at predefined frequencies that depend on the patient's last reported score. One aim of this dissertation is to mitigate inequities that stem from patients' flexibility regarding MELD score updates. We develop a Markov decision process (MDP) model to examine the degree to which an individual patient can benefit from the updating flexibility, and provide a menu of updating requirements that balance inequity and data processing more efficiently than the current updating requirements. We also derive sufficient conditions under which a structured optimal updating policy exists.
As the coordinator of the harvesting Organ Procurement Organization (OPO) extends offers according to MELD score prioritization, the organ becomes less desirable. To avoid
not placing the organ, the OPO coordinator can initiate an expedited placement, i.e., offer the organ to a transplant center, which can then allocate it to any of its patients. A second aim of this dissertation is to mitigate inequities induced by the OPO coordinator's premature departure from the prioritized list of patients via an expedited placement.
As a preliminary step to studying the inequity induced by expedited liver placement, we conduct an extensive analysis of the current expedited liver placement practice based on
recent data. We investigate different aspects of extending offers, e.g., the number of offers extended concurrently, and patients' response characteristics. Several of the results from this analysis serve as inputs for a second MDP model that examines how many concurrent offers the OPO coordinator should extend and when the coordinator should initiate an expedited placement. Numerical experimentation reveals a structured optimal policy, and we test the sensitivity of the model outcomes with respect to changes in model inputs. Lastly, we examine how our model outputs compare to the analogous measures observed in current practice and how they can be used to improve current practice.
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Details
Item Type: |
University of Pittsburgh ETD
|
Status: |
Unpublished |
Creators/Authors: |
|
ETD Committee: |
Title | Member | Email Address | Pitt Username | ORCID |
---|
Committee Chair | MAILLART, LISA M | | | | Committee Member | SCHAEFER, ANDREW J | | | | Committee Member | ROBERTS, MARK S | | | | Committee Member | KHAROUFEH, JEFFREY P | | | |
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Date: |
2 February 2012 |
Date Type: |
Publication |
Defense Date: |
7 September 2011 |
Approval Date: |
2 February 2012 |
Submission Date: |
9 November 2011 |
Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
Number of Pages: |
105 |
Institution: |
University of Pittsburgh |
Schools and Programs: |
Swanson School of Engineering > Industrial Engineering |
Degree: |
PhD - Doctor of Philosophy |
Thesis Type: |
Doctoral Dissertation |
Refereed: |
Yes |
Uncontrolled Keywords: |
Markov decision processes, dynamic programming, optimal stopping, structured optimal policies, Pareto optimality, sensitivity analysis, medical decision making,
organ transplantation, information asymmetry, societal welfare. |
Date Deposited: |
02 Feb 2012 17:20 |
Last Modified: |
15 Nov 2016 13:35 |
URI: |
http://d-scholarship.pitt.edu/id/eprint/6230 |
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