Author
Listed:
- Yanhan (Savannah) Tang
(Department of Information Technology and Operations Management, Cox School of Business, Southern Methodist University, Dallas, Texas 75205)
- Alan Scheller-Wolf
(Operations Management Area, Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)
- Sridhar Tayur
(Operations Management Area, Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)
- Emily R. Perito
(Department of Surgery, University of California, San Francisco, California 94143)
- John P. Roberts
(Department of Surgery, University of California, San Francisco, California 94143)
Abstract
Split liver transplantation (SLT) is a procedure that potentially saves two lives using one liver, increasing the total benefit derived from the limited number of donated livers available. SLT may also improve equity by giving transplant candidates who are physically smaller (including children) increased access to liver transplants. However, SLT is rarely used in the United States. To help quantify the benefits of increased SLT utilization and provide decision support tools, we introduce a deceased-donor liver allocation model with both efficiency and fairness objectives. We formulate our model as a multiqueue fluid system, incorporating the specifics of donor-recipient size matching and patients’ dynamically changing health conditions. Leveraging a novel decomposition result, we find the exact optimal matching procedure, enabling us to benchmark the performance of different allocation policies against the theoretical optimal. Numerical results, utilizing data from the Organ Procurement and Transplantation Network, show that increased utilization of SLT can significantly reduce patient deaths, increase total quality-adjusted life years, and improve fairness among different patient groups.
Suggested Citation
Yanhan (Savannah) Tang & Alan Scheller-Wolf & Sridhar Tayur & Emily R. Perito & John P. Roberts, 2025.
"Split Liver Transplantation: An Analytical Decision Support Model,"
Operations Research, INFORMS, vol. 73(4), pages 1785-1804, July.
Handle:
RePEc:inm:oropre:v:73:y:2025:i:4:p:1785-1804
DOI: 10.1287/opre.2022.0131
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