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.