Real-World Psychiatric Data Commons
A full-stack health data platform that unifies fragmented RWE datasets behind AI-powered discovery and FAIR scoring.
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At a glance
Real-world evidence lives in fragmented, poorly documented datasets, making discovery slow and quality hard to trust.
A full-stack platform for ICA.ai that unifies RWE datasets behind AI-powered discovery, automated FAIR quality scoring, and real-time team collaboration.
Cut data discovery time by 75% while making dataset quality visible and comparable across teams.
Overview
Real-world evidence is only as useful as it is findable. Teams at ICA.ai were losing days hunting through fragmented, inconsistently documented datasets — so I built the platform that unifies them behind one intelligent surface.
The engine combines AI-powered semantic discovery, automated FAIR (Findable, Accessible, Interoperable, Reusable) quality scoring, and real-time collaboration so teams can search, evaluate, and share datasets without leaving the tool.
By making both the data and its quality visible in one place, the platform cut discovery time by 75% and turned an opaque process into a transparent, collaborative one.
Highlights
- Unified fragmented RWE datasets behind one discovery surface
- AI-powered semantic search across datasets
- Automated FAIR quality scoring for every dataset
- Real-time team collaboration — 75% faster discovery