FW-HTF-P: Teaming Transplant Professionals and Artificial Intelligence Tools to Reduce Kidney Discard

Missouri University Of Science And Technology, Rolla MO

Investigators

Abstract

Thousands of procured kidneys are discarded each year due to inefficient workflow processes and negative perceptions for using lower quality or higher risk organs. While some of this discard is medically necessary, some represents lost opportunities to get patients off of dialysis and increase lifespans. This Future of Work at the Human Technology Frontier planning grant project aims to transform the organ transplant matching process. The future technology is an artificial intelligence (AI) system with usable trustworthy interfaces that are fully integrated into the transplant healthcare work context between the demand-side (transplant center) and supply-side (organ procurement organization). The future workers are organ procurement coordinators and transplant coordinators, physicians and surgeons. A single kidney can have thousands of offers before one, if any, transplant center accepts it. The current process of manually placing lower quality organs exacerbates lost opportunities. The AI system will identify transplant teams and candidates that are most likely to accept a lower quality organ so that the match can be identified quickly and the organ is less likely to be discarded. This planning grant will build capacity for integrating AI into transplant healthcare and engage workers as well as pre- and post-transplant patients in a design-a-thon event. This research is driven by transplant experts at Saint Louis University Hospital and experts in AI and human factors from Missouri University of Science & Technology. Once validated, this research can also be applied to other data-intensive high-stakes scenarios (e.g. military operations, critical infrastructure). AI systems often suffer from technical, human, and integration challenges. 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. It is time-consuming and costly to manually design neural architectures, so this project proposes 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. The project 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. 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.