Curated, Clinically True Data Modules to Power Research
Quality Data Informing Quality Insights
Qdata® by Verana Health are disease-specific, fit-for-purpose data modules designed to confidently drive business insights and rigorously inform research outcomes.
Qdata spans three therapeutic areas—ophthalmology, neurology, and urology—and reflects deep patient journeys across robust demographics. Qdata helps to unlock quality research insights along the entire drug and medical device development lifecycle, from clinical trial site and subject identification through to post-market evidence generation and opportunity analysis.
Qdata modules are curated data sets ready for research use to inform real-world evidence (RWE). Qdata is powered by Verana Health’s population health data engine, VeraQ®, which contains rich medical specialty data exclusively found here.
Here is a list of current Qdata modules and those in development, which represent a wide-range of indications across Urology, Ophthalmology, and Neurology.
*Verana Health can work with Life Sciences companies to create Qdata modules in ophthalmology, urology, and neurology that are fit for specific research purposes on select diseases.
How Qdata Works
Qdata stems from more than seven years of real-world data contributed by more than 20,000 healthcare providers across ophthalmology, urology, and neurology. The data modules start with structured and unstructured electronic health records (EHRs) from more than 90 million de-identified patients and 70 EHR systems. Then, data are harmonized, tokenized, and linked with other sources, such as claims and images in Verana Health’s VeraQ engine.
Finally, the data are curated with a clinician-first approach, complemented by natural language processing (NLP) and machine learning. The result is Qdata: research ready data to enable observational studies on a multitude of diseases.
Qdata for Drug Lifecycle Management
Qdata is powered by Verana Health’s population health data engine, VeraQ®. Qdata is well-suited to help answer complex research questions that require observational inputs, including those found in physician notes, which may include critical information such as symptoms, diagnoses, or outcomes. Thanks to VeraQ’s NLP-guided curation coupled with the human touch of physician-informed context, millions of records with free-form text become meaningful and can be distilled into usable and valuable Qdata variables.