Cihan Dagli and Richard Threlkeld presented a poster titled “AI-Enabled Digital Support to Increase Placement of Hard-to-Place Deceased Donor Kidneys” at the 2023 American Transplant Congress in San Diego, CA.
American Transplant Congress 2023
NSF Research Grant Awarded!
Our team of researchers at Missouri S&T, Saint Louis University, and United Network for Organ Sharing was awarded a $1.8M research grant from the National Science Foundation.
Check out the press release!
Collaborative Research: FW-HTF-R: Embedding Preferences in Adaptable Artificial Intelligence Decision Support for Transplant Healthcare to Reduce Kidney Discard
Overview. Transplantation provides patients suffering from end-stage kidney disease a better quality of life and long-term survival. 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 recipients. Organ Procurement Organizations (OPOs) have great difficulty finding transplant centers to accept less medically desirable (or 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 propose to integrate Artificial Intelligence (AI) decision support into the kidney offer process to support 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. Based on preliminary work from our planning grant, we are 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. In this research, we will (1) measure human 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 a randomized control trial using United Network for Organ Sharing (UNOS) Lab’s SimUNet, an ecologically valid kidney offer simulation platform for behavioral experiments, to estimate the impact on kidney discard. This research is driven by an existing partnership between transplant and ethics experts at Saint Louis University Hospital (SLU), behavioral scientists at UNOS, and experts in AI and human factors from Missouri University of Science & Technology (S&T).
Keywords: kidney transplant offers, trust, AI decision support system
Intellectual Merit. AI systems suffer from technical and human-integration challenges. Existing deep learning models are generic black boxes and cannot adapt in real-time to specific users. We propose to implicitly and explicitly solicit preferences as part of the training process to allow users to integrate the model predictions into their existing decision-making process. As part of this process, we will aggregate fairness notions across stakeholders to determine appropriate metrics for improving model performance. Within the deep learning model, we will impose trade-offs to increase fairness without significantly reducing accuracy, enhance explainability by converting feature relevance scores and convolution layer weights into linguistic expressions, and use transfer learning to rapidly integrate new data (such as preferences for customizing to specific workers) into deep learning models. In addition, we will conduct human subjects experiments to evaluate how uncertainty and explainability presentation influence trust, confidence, ease-of-use, and performance. Ultimately, each of these pieces (training, preference elicitation, deep learning model, interface presentation) will be combined and evaluated behaviorally using the ecologically-valid UNOS Lab’s SimUNet to estimate the impact on kidney discard.
Broader Impacts. Ultimately, this research aims to reduce kidney discard for high-risk organs by at least 10%. 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. In the transplant context, this will support the evidence-based application of more controversial management strategies, such as accelerated placement, which bypasses transplant centers that are unlikely to accept high-risk kidneys. In addition, this project will expand SimUNet to incorporate the OPO perspective to demonstrate the value of system-level behavioral science platforms for contexts with supply and demand roles. Ultimately, this project will train students from diverse backgrounds in transdisciplinary convergent research and serve as the basis for course projects and modules in systems engineering, psychology, and philosophy.
NSF Planning Grant Awarded!
Our team of researchers at Missouri S&T and Saint Louis University was awarded a planning grant from the National Science Foundation.
Check out the press release!
FW-HTF-P: Collaborative Proposal: Teaming Transplant Professionals and Artificial Intelligence Tools to Reduce Kidney Discard
Overview. While over 94,000 people are on the kidney transplant waiting list, less than a third will receive one this year. Thousands of usable kidneys are discarded due to inefficient workflow processes and negative perceptions for using lower quality organs. Organ Procurement Organizations (OPOs) have great difficulty finding transplant centers to accept procured lower quality kidneys. A single kidney can run through thousands of offers before one, if any, transplant center accepts it. The current process of manually placing lower quality organs via phone calls and emails is not working.
We will revolutionize this process with the aid of an artificial intelligence (AI) system and usable trustworthy interfaces that are fully integrated into the transplant workflow between demand-side (transplant center) and supply-side (OPO) organizations. Over the course of the planning grant, we will (1) document a transplant work system architecture and identify challenges for re-designing this work process, (2) develop a proof-of-concept AI system to predict which candidates are most likely to accept a lower quality kidney that is at risk of discard, and (3) perform human subjects experiments to scope the interface design and predict technology adoption factors. These efforts will build capacity for integrating AI into transplant healthcare and scope future research activities in a larger FW-HTF proposal. We frame this challenge as a system engineering problem to integrate technical, human, and process elements into our proposed solution.
We will perform system-based participatory research centered around a design-a-thon event to (a) build consensus on design criteria for the AI and interface in a realistic workflow, (b) evaluate mock-ups in focus groups, and (c) anticipate challenges for system implementation. The key workers include organ procurement coordinators, transplant coordinators, and transplant physicians and surgeons. This research is driven by an existing partnership between transplant experts at Saint Louis University Hospital (SLU) and experts in AI and human factors from Missouri University of Science & Technology.
Intellectual Merit. Despite their popularity, AI systems suffer from technical, human, and integration challenges. It is time-consuming and costly to manually design neural architectures, so we propose an approach that uses evolutionary algorithms to find the optimal architecture for a particular data set. This will facilitate real-time adaptation as the data inputs evolve over time. In addition, it is critical for AI systems to be explainable and transparent, particularly in high stakes contexts. We will perform human subject experiments with lay populations to evaluate how uncertainty visualizations and metrics influence performance, confidence, trust, technology acceptance, and willingness to choose riskier options. In a larger FW-HTF proposal, we aim to integrate this solution into the actual workflow between Mid-America Transplant and regional transplant centers, including SLU. Once validated, this research can also be applied to other data-intensive high-stakes scenarios (e.g. military operations, automated critical infrastructure).
Broader Impacts. Increasing automation via AI will reduce organ discard and increase the number of available kidneys for transplant by increasing utilization of lower quality kidneys. These efforts to increase the efficiency and efficacy of workers and systems involved in allocating organs for transplant is particularly critical because a recent Executive Order aims to make OPO performance metrics enforceable via regulation in the near future. This is a particularly controversial approach because evaluations of OPO performance are really an evaluation of the whole system. Reducing kidney discard is a two-way street because the demand-side (i.e., transplant centers) must also be more willing to accept imperfect kidneys.
Findings on technology acceptance will influence the design of future training programs. In addition to pursuing a future FW-HTF proposal, we will build on this planning grant by pursuing a National Research Traineeship award to develop an evidence-based interdisciplinary graduate-level program on human systems integration.