Whitepaper
Evolving the Truveta Language Model
Healthcare data is abundant, but its clinical meaning often remains locked in unstructured notes, inconsistent formats, and siloed systems. Turning these data into research-ready evidence requires more than access; it requires transformation at scale.
This whitepaper explores how the Truveta Language Model (TLM) has evolved from a collection of domain-specific models into a governed, agentic artificial intelligence (AI) system that transforms heterogeneous clinical data into standardized, longitudinal patient journeys. By orchestrating extraction, normalization, and quality-controlled workflows, TLM enables scalable, transparent, and reproducible real-world evidence generation and powers real-time insight generation through Truveta Intelligence.
Download this whitepaper to explore:
- How TLM extracts and structures clinical concepts from billions of notes to capture signals not present in structured data.
- How normalization aligns diverse data to standard ontologies, enabling consistent analysis across health systems.
- How agentic workflows coordinate models, validation steps, and quality controls to ensure accuracy and traceability.
- How clinical experts, evaluation frameworks, and continuous monitoring support regulatory-ready real-world evidence.
