Rapid Response and Deep Dive: University of New Mexico's Clinical & Translational Science Center Simplifies Analytics and Accelerates Time to Insight with TriNetX Platform

The University of New Mexico's Clinical & Translational Science Center faced slow, restrictive existing analytics platforms with limited compute resources and slow query response. Implementing TriNetX's analytics platform sped up their research workflow substantially: a kidney-disease manifestations project that previously would have required three to four years to complete was finished in three months. The team also used the platform to conduct COVID-19 vaccine safety studies far more rapidly across patient records than traditional methods would have allowed.

TriNetX is a federated real-world-data network where data stays behind each health system's firewall and is queried in place, used for cohort discovery and trial-site feasibility.

This sits in the Data Providers stage of the AI use-case lifecycle: Named examples from the real-world-data, claims and competitive-intelligence providers pharma AI systems are trained on, benchmarked against, or run inside of.