The ability to collect data from electronic medical records, medical images, devices, diagnostics, wearables and apps means that more real world data (RWD) is available to be analyzed and derive insights from than ever before. At the same time, data management technologies, AI tools, and bioinformatics platforms are transforming data collection, processing and analysis for greater efficiency. The ability for institutions to share this data has implications for drug development, clinical trial recruitment, companion diagnostics and more.
Aggregated RWD is useful because it can address the diversity shortcomings of randomized clinical trials, which can take up to a year to recruit targeted patients. This enhances the ability of drug developers to model outcomes more accurately in clinical research.
Custodians of RWD, such as health systems and labs have pursued avenues to data monetization where they aim to license
access to the data in return for monetary benefits from data consumers (RWD vendors, pharma companies, etc.). But for that data to be easily shared to facilitate collaboration, it must be aggregated, structured and standardized. It also has to be de-identified for privacy and security reasons. That’s where an effective federated data access model comes into play, according to a new report by datma.
A federated data model enables connections between healthcare data custodians, such as health systems and labs, with potential data consumers, such as pharmaceutical companies and research institutions, by offering greater flexibility, access, and control. Datma’s white paper, A New Paradigm for Healthcare Data Monetization, highlights the evolution of data monetization models, revenues models aligned with custodian interests and more.
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