Failure-Aware Kidney Exchange

Algorithmic matches in fielded kidney exchanges do not typically result in an actual transplant. We address the problem of cycles and chains in proposed matches failing after the matching algorithm has committed to them. We show that failure-aware kidney exchange can significantly increase the expected number of lives saved (i) in theory, on random graph models; (ii) on real data from kidney exchange match runs between 2010 and 2014; (iii) on synthetic data generated via a model of dynamic kidney exchange. We design a branchand-price-based optimal clearing algorithm specifically for the probabilistic exchange clearing problem and show that this new solver scales well on large simulated data, unlike prior clearing algorithms. We show experimentally that taking failed parts from an initial match and instantaneously rematching them with other vertices still in the waiting pool can result in significant gains. Finally, we show that failure-aware matching can increase overall system efficiency and simultaneously increase the expected number of transplants to highly-sensitized patients, in both static and dynamic models.