The US heart transplant system prioritizes candidates based on the severity of their pre-transplant therapy; the premise is that this severity reflects medical urgency. However, it is widely suggested that this rule opens room for gaming the system, by assigning high-severity therapies to low-urgency patients. We study a novel model of the gaming decisions of heart transplant centers. We identify the underlying trade-offs and shed light on the conditions that give rise to such gaming.

In this paper, our goal is to (i) characterize the equilibrium behaviour of the agents under targeted priority mechanisms, (ii) identify the impact of these mechanisms on several performance metrics, including the discard rates of organs, and overall social welfare, and (iii) establish the impact of the selection of design parameters on different outcomes in equilibrium to investigate the optimal design of such mechanisms.

We optimize blood donor incentivization to reduce shortages and wastage. Our model considers random demand, perishability, and donor variability. Using a coupled queueing network approach and the Pipeline Queue paradigm, we derive a tractable convex reformulation. Results show improved performance compared to the threshold policy. It provides decision support for dynamic donor incentivization in blood supply chain management.