From Data Silos to Revenue Streams: Federated Learning and Incentivized AI for Healthcare With NVIDIA FLARE

Hospitals hold some of the most valuable clinical data in the world, but two persistent challenges block its potential: They can't share data due to privacy regulations, and they receive no compensation for enabling research or innovation.

At Partex, we're addressing both issues through a real-world data (RWD) foundation model, built using federated learning and powered by NVIDIA FLARE™. This approach allows us to train advanced AI models across distributed hospital data without moving or exposing any sensitive information—ensuring full compliance and preserving trust.

But privacy is only part of the equation. Hospitals need a reason to participate. That's why we've implemented an attribution-based revenue model that rewards institutions both for the volume of data contributed and the impact that data has on real-world AI outputs. Each time our foundation model is queried—whether for biomarker discovery, patient stratification, or trial optimization—we trace which datasets contributed to the response and compensate the relevant providers accordingly.

This system transforms data from a passive asset into an active, recurring revenue stream for hospitals—creating a win-win model for healthcare systems, researchers, and patients alike.