Collaborative Research: FW-HTF-R: Embedding Preferences in Adaptable Artificial Intelligence Decision Support for Transplant Healthcare to Reduce Kidney Discard

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Transplantation provides patients suffering from end-stage kidney disease a better quality of life and long-term survival compared to chronic dialysis. However, approximately 20% of deceased donor kidneys are discarded and never transplanted. While some discards may be medically appropriate, others reflect missed opportunities. Even kidneys deemed less desirable may provide survival benefits to some patients. Organ Procurement Organizations (OPOs) have great difficulty finding transplant centers to accept less medically desirable (higher risk) kidneys. At their discretion, OPOs can use accelerated placement to bypass the priority list for “hard-to-place” kidneys. However, due to a lack of data-driven guidance, this mechanism is not systematically applied and likely underutilized. To enable transformative change, we will integrate Artificial Intelligence (AI) decision support into the kidney offer process for both demand at the transplant center and supply at the OPO. Key workers include OPO staff (organ procurement coordinators, operations directors, medical directors), transplant center staff (coordinators, physicians, surgeons), and transplant patients. This research is driven by a partnership between transplant and ethics experts at Saint Louis University Hospital, behavioral scientists at the United Network for Organ Sharing (UNOS), and experts in AI and human factors from Missouri University of Science & Technology.Building on a FW-HTF planning grant, this project is developing an AI decision support system for (a) transplant centers to accept/deny high-risk kidney offers and (b) OPOs to identify hard-to-place kidneys sooner. This research will (1) measure worker preferences to customize the support system’s operation and interface, (2) aggregate fairness preferences as defined by diverse stakeholders to improve fairness in the model output, (3) evaluate the effect of embedding uncertainty and explainability into the interface, (4) develop deep learning ensemble models that can adapt over time while being explainable, and (5) conduct randomized control trials using UNOS Lab’s SimUNet, a realistic kidney offer simulation platform for behavioral experiments, to estimate the impact on kidney discard. Within the deep learning model, this project will impose trade-offs to increase fairness without significantly reducing accuracy, enhance explainability by converting feature relevance into linguistic expressions, and integrate new data (such as customizing for worker preferences) through transfer learning as conditions change in kidney transplant practices. Ultimately, this research aims to reduce kidney discard for “hard-to-place” organs by at least 10%. In addition, this work will support critical advancements in ethics and training, issues that will be critical in overcoming system-level barriers to integrate AI into healthcare.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.