Learn how to accelerate drug development and improve healthcare delivery
with a unified approach to data, analytics and AI
Big data has proven as revolutionary as scientific breakthroughs like the microscope and x-ray. Over the last few years, real world data (RWD) providers have enabled access to population- scale health data for researchers across healthcare and the life sciences. By analyzing large real-world datasets, researchers and clinicians can now spot trends that were previously not visible in smaller studies. The findings from these studies can be applied to a broad set of use cases including efficient trial design, early detection of chronic disease and much more.
Despite the promise of RWD, most organizations struggle to extract value from these massive multi-terabyte datasets. Often times this is due to the challenges of scaling biostatistical analyses with legacy tools. Alternatively, a company may not know how to apply machine learning to RWD in a reproducible manner. With the Databricks Unified Data Analytics Platform, healthcare and life sciences companies can overcome these issues to deliver on innovative use cases.
In this ebook, you will learn:
Download the eBook, Analyzing Real-World Evidence at Scale, to learn more.
- The top analytics and machine learning 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 machine learning to RWD in a unified environment
- Why popular open-source technologies - such as Apache Spark, Delta Lake, and MLflow - are key to streamlining the end-to-end analysis of RWD
- How Livongo, a leading healthcare IT company, is using RWD to deliver real-time health recommendations to diabetic patients