Advancements in AI have presented a unique opportunity to transform and clean massive streams of healthcare data to make it available for research, innovation, and patient care. This whitepaper explains the Truveta Language Model, a large-language model used to clean billions of EHR data points for health research.
Cardiovascular disease is a leading cause of death in the US, but researchers face many challenges when seeking to study the safety and effectiveness of drugs and devices used to treat it. This whitepaper highlights historical limitations of RWD and explains how Truveta addresses them to accelerate research on outcomes and care.
Medical device companies are challenged by a significant lack of real-world data on device use in clinical practice. In this whitepaper, learn how Truveta provides critical device specific information linked to deep clinical data to accelerate device research for over 150,000 unique medical devices.
Founded by health systems, Truveta has a deep commitment to patient privacy. This whitepaper shares details of Truveta’s advanced de-identification process and how identifiers in Truveta Data are managed and protected according to HIPAA standards.
Our security systems protect data through every stage of the data process and have been validated to meet the most rigorous standards for security and privacy. This paper focuses on how we store and secure Truveta Data.
Truveta Data comprises billions of EHR data points, representing ~100 million patient journeys from more than 20,000 clinics and 800 hospitals, updated daily. Truveta Data is held to the highest standards of data quality and provenance, with a rigorous quality control process, outlined in this whitepaper.
Truveta Studio delivers fast, convenient analytics, empowering everyone on the team to learn from Truveta Data. Learn more about Truveta Studio and how it empowers researchers with powerful insights to improve patient outcomes, accelerate R&D, and inform public policy.
We believe it is a moral imperative to harness the power of data to improve healthcare. We know we’ve taken on an enormous responsibility, and we pursue our work with a commitment to patient privacy, broadly enabling research and transparency about our data, all in close coordination with our member health systems.
We share the technical details behind Truveta Mapper the underlying AI technology in the Truveta Language Model. Truveta Mapper leverages a multi-task sequence-to-sequence transformer model to perform alignment across multiple ontologies in a zero-shot, unified, and end-to-end manner. This paper was originally published at arXiv.