Targeted Priority Mechanisms in Transplantation: Incentivizing, Not Enforcing, Efficient Matching of Organs

The persistent imbalance between organ supply and demand poses a significant challenge for the lifesaving treatment of transplantation. This study introduces innovative targeted priority mechanisms inspired by the Eurotransplant Senior Program (ESP), designed to bridge the supply-demand gap while optimizing the matching between organs and recipients without mandating offer acceptance. These mechanisms diverge from traditional prioritization by offering voluntary participation and granting explicit priority to disadvantaged patients for a specified range of organs, provided they restrict their offer acceptance to this set. Using a comprehensive queueing model, our study analyzes the strategic decisions of waitlisted candidates under targeted priority mechanisms in equilibrium, establishing the optimal program design. Our evaluation of these mechanisms’ impact on social welfare underscores their potential to improve overall welfare without adversely affecting any patient group. The practical applicability and impact of these mechanisms are demonstrated through a case study within the U.S. kidney allocation system, using a clinically detailed simulation model targeting elderly patients aged 65 and above. Simulation outcomes reveal a significant participation rate (70%) even when the program offers priority over kidneys that are conventionally perceived as marginal. Furthermore, our findings highlight the mechanisms’ potential to increase transplants by up to 920, decrease nonuse by 25%, and prevent up to 193 waitlist deaths annually compared with the current system. Improvements in the outcomes of even nonelderly patients highlight the mechanisms’ potential to comprehensively address organ underutilization and improve organ-recipient matching in a real-world context. This paper was accepted by Nicos Savva, healthcare management. Funding: This work was supported by the Division of Civil, Mechanical and Manufacturing Innovation [Grant 2339767]. This work was supported in part by the Health Resources and Services Administration [Contract HHSH250-2019-00001C]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05724 .