Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

MARKOV DECISION PROCESS MODELS FOR IMPROVING EQUITY IN LIVER ALLOCATION

ICTEN, ZEYNEP GOZDE (2012) MARKOV DECISION PROCESS MODELS FOR IMPROVING EQUITY IN LIVER ALLOCATION. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

[img]
Preview
PDF
Primary Text

Download (2MB) | Preview

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.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
ICTEN, ZEYNEP GOZDEgozdeicten@yahoo.com
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairMAILLART, LISA M
Committee MemberSCHAEFER, ANDREW J
Committee MemberROBERTS, MARK S
Committee MemberKHAROUFEH, JEFFREY P
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

Metrics

Monthly Views for the past 3 years

Plum Analytics


Actions (login required)

View Item View Item