An Application of Markov Decision Processes in Health Economics: Mitigating Inequities in Organ Allocation via Revised Health Reporting Frequencies
Due to the scarcity of donated livers, it is critical that the United Network for Organ Sharing (UNOS), the organization responsible for nationwide allocation of donated organs in the US, manage organs in an efficient, effective and equitable way. UNOS prioritizes patients awaiting liver transplantation based primarily on their medical urgency, as measured by their model for end-stage liver disease (MELD) score, and requires each patient to report their MELD score at a frequency that depends on their last reported MELD score (the sicker, the more frequent). As a result of this flexibility, patients may conceal changes in their MELD score and “game” the system. Mitigating the resulting inequity by requiring very frequent updates, however, is impractical and would add to the already significant data processing burden. Using a Markov decision process model parameterized by clinical data and cost-effectiveness analysis, we examine (i) the degree to which an individual patient can benefit from the updating flexibility, and (ii) how the resulting inequities may be mitigated by revising the updating frequencies without significantly adding to the data processing burden. We provide a menu of updating policies that balance inequity and data processing and suggest that requiring the sicker (healthier) patients to update more (less) frequently than they must under the current policy can improve both metrics.
This is joint work with Dr Lisa Maillart, Dr Andrew Schaefer, Dr Mark Roberts and Dr Atul Bhandari.
Zeynep Gozde Icten
Zeynep G Icten, PhD, is a Research Associate in United BioSource Scientific Consulting’s Modeling and Simulation Practice. Zeynep holds a Bachelor’s Degree in Industrial Engineering from Bogazici University and a Master’s Degree in Industrial Engineering from University of Pittsburgh. She completed her Doctoral Degree in Industrial Engineering at University of Pittsburgh.
Dr Icten’s main research interest is decision making under uncertainty with healthcare applications. She has been working on stochastic dynamic decision making problems, health economics models, cost-effectiveness analyses, and optimization of healthcare operations. She has expertise in oncology and deceased-donor organ transplantation models. Her further research interests include reliability and maintenance optimization, sustainability and energy systems design, and game-theoretic models.