4 Ways Technology Can Reduce Racial Inequities in Organ Transplant

Within the healthcare system as a whole, Black and indigenous people and people of colour awaiting transplants and their families face bias and discrimination. Black Americans are less likely to be identified as transplant candidates, referred to for evaluation, put on the kidney transplant waitlist, receive a kidney transplant, or receive a higher-quality kidney from a living donor. In addition, they have a lower likelihood of transplant survival. Through a combination of equitable policies and technological innovation, many of these inequities can be addressed now:

1. Refined matching system: A matching and offer engine that allows both easy-to-use rules configuration and clear outcome visibility, eliminating obscure and vague matching outcomes.
2. Metrics around time elapsed in the referral and acceptance process: Studies show it takes more time for Black patients to be referred for a transplant to begin with and accepted into a transplant program. By measuring and monitoring time elapsed, coordinators
3. Simulation: A tool that allows users to see how updated rules affect historical results. Further, users can adjust allocation scoring to best address systemic gaps that significantly impact marginalized communities. A flexible workbench allows for scoring adjustments based on regular re-evaluations of the latest research to reflect extensions to normothermic preservation permit travel times as the science around organ viability evolves.
4. Increased Transparency Through APIs: Implement interfaces to enable customers to develop their extensive data intelligence insights that provide the ultimate transparency for all interested stakeholders. The intelligence derived from data records can be used to shape policy decisions and systemic interventions to enhance equity in transplant access and outcomes.
5. Increased Accountability: Accountability starts with clearly and unambiguously presenting how allocations and matches are made. Approaches include visually representing the rules used, what data-informed those rules, and what outcomes were derived. Accountability can be achieved through advanced analysis mechanisms including a fully configurable and transparent rules engine, securely exposing all decision data, and an ongoing mechanism for continued analysis.