[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.