Current organ distribution and allocation policies have resulted in persistent disparities in access to donated organs for transplantation across different waitlisted candidates based on their geographic location, sex, and/or disease. We discuss a novel optimization scheme that leverages machine learning and simulation techniques to devise allocation policies that could alleviate these disparities and allow for a more efficient use of donated organs in the United States. We find that our proposed allocation policies could provide substantial waitlist mortality reduction (of the order of 20% for end-stage liver disease patients), while providing a more equitable organ access in comparison with other proposals.
Nikos Trichakis is an Associate Professor of Operations Management at the MIT Sloan School of Management. His research interests include optimization under uncertainty, data-driven optimization and analytics, with application in healthcare, supply chain management, and finance. Trichakis is also interested in the interplay of fairness and efficiency in resource allocation problems and operations, and the inherent tradeoffs that arise in balancing these objectives.