Identifying AI Opportunities in Donor Kidney Acceptance: Incremental Hierarchical Systems Engineering Approach

Abstract

The current organ placement process for transplantation is an evolving system of systems with emergent behavior. This highly integrated complex system consists of Organ Procurement Organizations (OPOs), Transplant Centers (TXC), patients, and their interactions. The number of waitlisted kidney candidates is nearly five times the available supply. Unfortunately, over twenty percent of donated deceased donor kidneys (supply) are discarded due to issues with kidney quality. While some of this discard is medically necessary, some represent a lost opportunity. One approach is to develop a decision support system to identify the right candidate for the right donor at the right time and then communicate that analysis to various stakeholders in different locations over time. This paper uses an incremental hierarchical systems engineering approach to capture the current kidney allocation systems architecture and identify opportunities for an Artificial Intelligence (AI) decision support system to reduce kidney discard. The incremental hierarchical (top to bottom) approach was combined with model-based system engineering (MBSE) to aid in eliciting stakeholders’ needs, behaviors, boundaries, and interactions. This approach led to a structured development process for the attractor “reducing kidney discard” and facilitated systematically documenting the opportunity space. Stakeholders reviewed proposed AI decision support systems, ensuring that decision points with more significant opportunities were addressed. Ultimately, the effectiveness of the systems engineering approach is justified with a data-driven deep learning TXC decision support system validated by transplant surgeons. Future work will include developing data-driven models for all stakeholders using current data incorporating the most recent kidney allocation policy changes.