Fill your critical data gaps with ready-to-use real-world data
Enhance your discoveries and market access strategies with lab & EHR data. Pharmaceutical companies often face critical data gaps that hinder both market access strategies and discovery efforts. datma.FED addresses these challenges by providing targeted access to high-quality, ready-to-use, Real-World Data without compromising privacy or security.
Common reasons for data gaps
Missing genomics and molecular data from your current vendor sources
Lack of access to temporal attributes due to privacy or de-identification challenges
Irreconcilable variation in diagnostic interpretation arises from the lack of access to raw data (such as images or samples)
Insurance challenges and inaccurate claims
Why datma.FED is different
datma.FED bridges these gaps by enabling secure access to rich, granular lab data- including genomics and molecular data- without compromising patient privacy or security. Through a series of queries and algorithms processed locally within the custodian’s infrastructure, datma ensures full compliance with privacy regulations while allowing you to fill critical gaps in your current datasets.
Benefits
Enhanced research capabilities
Gain access to more diverse, representative datasets that enhance the precision of your research and accelerate development
Identify market opportunities
Map patient journeys to uncover barriers and missed opportunities in treatment adoption, improving your market access strategies
Timely Evidence Generation
Post launch evidence generation to support engagement with payers for formulary onboarding and coverage
datma.FED Features
Ready-to-Use data
Data is harmonized and prepared for immediate use, saving you valuable preparation time
Targeted and comprehensive datasets
Access disease-specific lab and biomarker data tailored to support your drug development efforts
Real-World insights
Analyze patient data over time to uncover complex clinical events and temporal relationships across datasets
Tokenization & Federated Queries
Extend internal data assets by leveraging tokenization to link to the data in the federated network
Real-World impact, measurable results
We analyzed data from over 90,000 oncology patients, identifying 14,000 NSCLC cases using ICD-10 and CPT codes. Using generative AI, we determined the landscape for each NSCLC segment. 40% of NSCLC patients received molecular testing, exceeding the published benchmark of 22%. The MET 14 exon mutation was found in 2.36% of this population, which is aligned with published data at 3-4%.
Only 17% received the targeted therapy Drug A