[Whitepaper] Tackling Clinical Trial Data Overload with Data Lakes and Machine Learning

Clinical trial data come from many different clinical (i.e. EDC, eCOA, lab, ePro, EMR/EHR, biomarker, mHealth / IoT), and operational (project management, eTMF, regulatory, financial, employee) sources and formats. Ingesting, aggregating and standardizing these data is challenging, inhibiting real-time or near-real-time access, increasing risk and driving up costs. As clinical trial complexity increases, trial sizes grow, and data variety and volume explode, this problem is only growing worse.

For sponsors and CROs who are experiencing this challenge, within and across studies, a clinical data and analytics hub built on a big data, data lake architecture offers great promise. This whitepaper explores how a data lake, enabled by AI and ML, can be used to ingest, aggregate, standardize and provide secured data access and the value it can deliver in reducing risk and driving efficiency, speed and cost savings.

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