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. Of the waitlisted candidates, about a fifth receive a transplant, most often from deceased donors. Unfortunately, due to quality concerns, approximately 20% of deceased donor kidneys are discarded in current practice [7]. While some of this discard is medically necessary, some represents lost opportunities due to inefficiencies in the system. Systems engineering provides tools that focus on designing, integrating, and managing the emergence behavior of complex systems. In contrast to mechanical systems where the output measures the linear component interaction, the kidney allocation system produces a nonlinear emergent behavior affected by medical, network, and human factors [1]. Therefore, a multidisciplinary approach is required to capture stakeholders’ needs and workflows in system architecture. architecture The system identifies opportunities for technological advancements such as Artificial Intelligence (AI) decision support systems. Stakeholders reviewed proposed AI decision support systems, ensuring that decision points with more significant opportunities were addressed.
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