Real World Data
Unlocking the Power of Diverse, High-Quality Data to Inform Critical Decisions.
Our commitment to leveraging both structured and unstructured data ensures that no valuable information is overlooked, empowering pharmacuitical and biotech companies to make data-driven decisions with confidence.
To support your evidence generation strategy in the U.S. market, NouStarX provides access to a de-identified, structured EHR data network that represents the true diversity and scale of the American healthcare landscape. This resource allows pharmaceutical and biotech partners to move beyond fragmented data snapshots, offering a definitive, longitudinal view of the patient journey for regulatory-grade Real-World Evidence (RWE).
Electronic Health Records (EHRs) from major medical centers across different regions in the US
| Location | No. of Institutes/Networks | Population Covered |
|---|---|---|
| Northeast | 1 | 7M |
| South | 5 | 43M |
| Midwest | 3 | 34M |
| West | 1 | 55M |
Our Capabilities
Leveraging advanced AI and proprietary algorithms to transform fragmented clinical data into actionable evidence.
Comprehensive Data Extraction
We process a vast array of unstructured data types, including progress notes, operative reports, and discharge summaries, to capture the complete clinical context.
Advanced NLP and Machine Learning
Our proprietary algorithms accurately interpret and standardize medical terminologies, ensuring precise data normalization.
Integration with Structured Data
By combining insights from unstructured and structured data, we provide a holistic view of patient health, enhancing the depth and quality of our analyses.
Key Benefits
Enhanced Clinical Insights
Unlocking information from unstructured data allows for a more comprehensive understanding of patient conditions and treatment outcomes
Improved Decision-Making
Access to detailed clinical narratives supports more informed decisions in clinical development and patient care strategies
Accelerated Research
Automated data extraction and normalization streamline the research process, reducing time and resource requirements