Analyzing Real-World Evidence at Scale

In recent years, real-world data (RWD) providers have enabled access to population-scale health data for healthcare researchers. By analyzing large, real-world datasets, researchers and clinicians can now spot trends that were previously invisible in smaller studies. The findings from these real-world studies can be applied to a broad set of use cases, such as clinical research, trial design, and the delivery of health care. Despite the promise of RWD, most organizations struggle to extract value from massive, multi-terabyte datasets. This is because scaling statistical analyses with legacy tools are challenging and healthcare organizations may not know how to apply ML to the analysis of RWD in a scalable way. With the Databricks Unified Data Analytics Platform, healthcare, and life sciences companies can deliver innovative clinical and research use cases. In this ebook, you will learn: The top analytics and ML use cases for real-world evidence Why legacy architectures for storing and analyzing clinical data make it a challenge to analyze RWD at scale How to easily and reproducibly scale analytics and apply ML to RWD Why popular open-source technologies – such as Delta Lake and MLflow – are key to streamlining the end-to-end RWD analysis How Livongo, a leading healthcare IT company, is using RWD to deliver real-time health recommendations to diabetic patient populations.

 

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