LexisNexis? Risk Solutions Optimizes De-Identified Data Matching with Patient Centric Token

摘要

WHAT the challenge you were looking to solve was? Medical research is notorious for not including participants representative of the diversity of our country, relying upon incomplete patient journeys viewed through the lens of only one of the many health providers we all visit. From COVID-19 and maternal mortality to diabetes and heart disease, healthcare inequities are pervasive in the healthcare system. There are more than five billion permutations of 285 million U.S. adult identities due to address and name changes during a person's lifetime. Current methods of relating de-identified data do not work when a person's name or address changes, so their data remains fragmented. Historically, barriers have limited data sharing, restricting the ability to improve or measure patient outcomes. This disconnect limits the longitudinal view of a patient, impacting the ability to understand differences in patient populations, or control for various risk factors in research populations - factors that can have a dramatic impact on the research results.