Position: Machine Learning for Heart Transplant Allocation Policy Optimization Should Account for Incentives
This addresses a critical gap in healthcare policy optimization by highlighting incentive issues in organ allocation, though it is incremental as it builds on existing data-driven methods.
The paper argues that current machine learning approaches for heart transplant allocation overlook incentive misalignments among stakeholders, and proposes integrating mechanism design and related fields to develop incentive-aware policies for improved robustness and fairness.
The allocation of scarce donor organs constitutes one of the most consequential algorithmic challenges in healthcare. While the field is rapidly transitioning from rigid, rule-based systems to machine learning and data-driven optimization, we argue that current approaches often overlook a fundamental barrier: incentives. In this position paper, we highlight that organ allocation is not merely a static optimization problem, but rather a complex game involving transplant centers, clinicians, and regulators. Focusing on US adult heart transplant allocation, we identify critical incentive misalignments across the decision-making pipeline, and present data showing that they are having adverse consequences today. Our main position is that the next generation of allocation policies should be incentive aware. We outline a research agenda for the machine learning community, calling for the integration of mechanism design, strategic classification, causal inference, and social choice to ensure robustness, efficiency, and fairness in the face of strategic behavior from the various constituent groups.