Lost in Transplantation: Characterizing Racial Gaps in Physician Organ Offer Acceptance
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3715275.3732097.pdf
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1.41 MB
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Adobe PDF
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8438291ed778aeb0ff32521417af3690
Author(s)
Adam, Hammaad
Bermea, Rene
Yang, Ming Ying
Celi, Leo Anthony
Ghassemi, Marzyeh
Date Issued
June 23, 2025
Publisher
ACM|The 2025 ACM Conference on Fairness, Accountability, and Transparency
Citation
Hammaad Adam, Rene S. Bermea, Ming Ying Yang, Leo Anthony Celi, and Marzyeh Ghassemi. 2025. Lost in Transplantation: Characterizing Racial Gaps in Physician Organ Offer Acceptance. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT '25). Association for Computing Machinery, New York, NY, USA, 1450–1467.
Version
Final published version
Abstract
There are known racial disparities in the organ transplant allocation system in the United States. While recent research has focused on designing scores and matching algorithms for organ allocation, prior work has yet to study how transplant center physician decisions on offer acceptance—the final step in the allocation process—contribute to the observed disparities. In this paper, we use data from the Scientific Registry of Transplant Recipients to examine the role of candidate race in the acceptance of heart, liver, and lung transplant offers. We find that Black race was associated with significantly lower odds of offer acceptance for livers and lungs. Further, existing allocation scores such as MELD and LAS did not account for clinical factors that made Black patients harder to match. Our analysis also revealed that donor candidate race-match was associated with significantly higher odds of offer acceptance for hearts, livers, and lungs. Finally, we found that rejecting an offer was associated with lower survival times for all three organs. Our findings demonstrate the additional barriers that Black patients face in accessing organ transplants and the consequences of these barriers on patient survival. Overall, our work highlights the limitations of technical solutions to socio-technical problems; new allocation scores and other algorithmic updates will not improve equity if they do not explicitly account for gaps in the ensuing human decisions.
Description
FAccT ’25, Athens, Greece
MIT Department
Institute for Medical Engineering and Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
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DOI of Published Version
https://doi.org/10.1145/3715275.3732097
