High-validity real-world evidence at scale
Verantos has built the only platform in the industry that generates high-validity RWE at scale for all major therapeutic areas. The Verantos Evidence Platform uses proprietary deep phenotyping technology to generate high-validity RWE and leverages a federated data network to achieve the scale required to support evidence needs.
The platform generates data sets that represent cohorts of thousands of study subjects. Real-world data is sourced from academic medical centers and community health systems and linked to other sources for completeness. It scales to meet the needs of specific indications using technology as opposed to manual data curation, while maintaining high-validity.
Going beyond traditional RWE
High-validity RWE is characterized by accuracy, completeness, and traceability and informs important clinical, R&D, and post-marketing workflows, including observational studies, external control arms, clinical trial recruitment, regulatory and reimbursement, and value-based contracting.
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Components
The Verantos Evidence Platform generates high-validity RWE at scale from the entire patient record. High-validity RWE informs important clinical, R&D, and post-marketing workflows including observational studies, external control arms, clinical trial recruitment, regulatory and reimbursement, and value-based contracting. The Verantos Evidence Platform consists of four components:
Verantos Research Network
The Verantos Research Network is a federated network consisting of academic medical centers and community health systems that provides access to structured and unstructured EHR data, pharmacy, institutional, and professional claims, and registry data to provide a complete view of study subjects.
Artificial intelligence (AI) engine
The AI engine is trained using Verantos’ deep phenotyping methodology to ensure accurate extraction of patient characteristics. Models are trained and managed centrally and tuned locally to ensure the best results.
Data processing
The data processing layer normalizes and standardizes information collected from disparate data sources to enable uniform analysis of the data and maintains traceability to source data.
Research analytics
Research analytics contains a library of measures including clinical and healthcare resource utilization variables. Additionally, custom variables can be authored to answer study questions. Cohort data sets are also provided to end users for analysis in the OMOP CDM format.
Advanced approaches
support high validity
Review our most recent publication on data validity
How high-validity RWE is achieved
By engaging health systems directly, we go to the source for the highest quality phenotype data. By linking with claims and registries, we understand clinical outcomes and resource utilization.
