Tuva Health develops open-source software that normalizes and unifies unstructured healthcare data from various sources, including claims and medical records, into a longitudinal data model. This platform enables healthcare organizations to enhance data quality, streamline analytics, and significantly reduce data platform costs by up to 90%.
Funding
$5.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
BVFounders
Product
Problem
Healthcare organizations face challenges in managing and leveraging unstructured data from disparate sources like claims, medical records, and other clinical data. This makes it difficult to derive meaningful insights, build comprehensive analytics, and improve patient outcomes. The process of normalizing and unifying this data is often complex, time-consuming, and expensive.
Solution
Tuva Health offers an open-source data platform that normalizes and unifies unstructured healthcare data from various sources into a longitudinal data model. The platform transforms raw data from claims, medical records, lab results, FHIR, and ADT feeds within a data warehouse. It enriches the harmonized data with clinical concepts, measures, groupers, and risk models, enabling rapid creation of business-critical insights. Tuva also validates data quality, proactively monitoring for issues and tracing their impact on analytic use cases.
Target Audience
Tuva Health targets healthcare data analysts, researchers, and engineers, as well as healthcare organizations seeking to build and control their data platforms cost-effectively.
Features
- Open-source data model, data marts, terminology, value sets, and data quality tools
- Fully managed data platform built around the open-source core
- Data model customization and extensibility to meet specific data and use-case needs
- Data harmonization, unifying claims, medical records, lab results, FHIR, and ADT data
- Data enrichment with clinical concepts, measures, groupers, and risk models
- Data validation to proactively monitor and identify data quality problems
- Integration with cloud data warehouses like Snowflake and Databricks