Provides a federated data platform for healthcare organizations, enabling the integration and harmonization of structured and unstructured clinical data using NLP and OMOP CDM. This system supports real-world evidence generation and multicenter studies in oncology and cardiology, improving data quality and accelerating research while ensuring compliance with privacy regulations.
Funding
$26.7M 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.
MAFounders
Product
Problem
Healthcare organizations face challenges in integrating and harmonizing diverse clinical data types, including structured and unstructured data, which are often stored in disparate systems. This lack of interoperability hinders real-world evidence (RWE) generation and multicenter studies, limiting the ability to derive meaningful insights from comprehensive patient data.
Solution
The company offers a federated data platform designed for healthcare and life science organizations that integrates and harmonizes structured and unstructured clinical data. The platform leverages NLP to enrich data and maps it to the OMOP common data model (CDM), facilitating real-world evidence generation and multicenter studies. By ensuring data quality and compliance with privacy regulations, the platform accelerates research and enables deeper insights into oncology, cardiology, and other therapeutic areas. The platform can be deployed on any cloud or on-premise via Kubernetes clusters.
Target Audience
The primary customers are healthcare organizations and life science companies seeking to leverage real-world data for research, clinical decision support, and improving patient outcomes.
Features
- Federated architecture enabling data integration across multiple healthcare organizations while maintaining data privacy.
- NLP pipeline trained on over 50 million patient records for extracting and structuring information from unstructured clinical text.
- Mapping to the OMOP common data model (CDM) to standardize data representation and facilitate interoperability.
- Quality control steps throughout the data value chain, from source to insight, ensuring data accuracy and reliability.
- Support for multicenter real-world evidence (RWE) studies in oncology (immuno-oncology, breast cancer, lung cancer, multiple myeloma, CLL), cardiology (heart failure, ATTR-CM), and mental health.
- Compliance with international privacy regulations in both the EU and the USA.