Event – Rotman Seminar: Dynamic exception points for fair liver allocation (Nov. 3)

Abstract  |  While the U. S. deceased donor organ transplantation system benefits tens of thousands of individuals each year, the system is demonstrably inequitable. A person’s chance of receiving an organ transplant today often relies on factors such as race, ethnicity, sex, and socioeconomic status. This study is focused on inequities based on transplant patients’ height – which disproportionately affects women across ethnicities, in addition to Hispanics and Asians broadly – because they can receive transplants from a smaller pool of available deceased donors for medical reasons. Reduced likelihood of transplantation leads to higher mortality rates and longer waiting times. In the current allocation policy, patients on the waiting list receive priority, dynamically, based on their Model for End-Stage Liver Disease (MELD) scores, which reflect the severity of liver disease. We propose a simple adjustment – providing additional (exception) points based on height and MELD score – that can be easily implemented in practice.

We model the liver allocation system as a multiclass fluid model of overloaded queues with multiple heterogeneous servers. We impose explicit equity constraints for all static patient classes, i.e., height. We characterize the optimal solution to the fluid model under the objective of minimizing pre-transplant mortality. We show that the optimal policy, called the Equity Adjusted Mortality Risk Policy, is an intuitive dynamic index policy, where the indices depend on patients’ acceptance probabilities of the organ offers, mortality risks, and the shadow prices calculated from the dual dynamical system. The discretized version of the optimal policy is numerically solved using estimates from clinical data. A detailed simulation study showx that for women, the disparity can be almost completely eliminated. Hispanics and Asians greatly benefit from receiving these MELD exception points as well. These improved fairness can be achieved without decreasing the overall efficiency of the current liver allocation system.  Link to paper.

Bio  |  Mustafa Akan is an Associate Professor of Operations Management at the Tepper School of Business, Carnegie Mellon University. His thesis received the Best Dissertation Award of the Aviation Applications Section of INFORMS in 2008. He held the Xerox Faculty Chair in 2009 and served as the Operations Management and Manufacturing Ph. D. program coordinator in 2009-2011. He is the co-recipient of the 2009 INFORMS Best Paper in Service Science, POMS 2012 Healthcare Best Paper awards, and the 2013 Lave-Weil Prize. Dr. Akan won the Gerald L. Thompson Teaching Award in 2014 at Carnegie Mellon University. He received the NSF CAREER Award in 2014. His research interests include pricing and revenue management, mechanism design, liver allocation, matching markets, stochastic modeling, queueing theory, manufacturing and service operations management, and healthcare delivery systems.

Announcement at: Rotman School of Management