Learn how to build analytics on real-world evidence at scale to accelerate drug development and improve the delivery of healthcare
Learn how to build analytics on real-world evidence at scale to accelerate drug development and improve the delivery of healthcare
Big data has proven as revolutionary to medicine 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 at healthcare and the life sciences organizations. By analyzing large real-world datasets, such as electronic medical records / EHR data, medical claims data, disease registries, etc., researchers and clinicians at healthcare systems can now spot trends that were previously not visible in smaller studies.
The findings from these real-world evidence based studies can be applied to a broad set of use cases, such as clinical research, trial design, the delivery of health care, regulatory decision making, and more. With the potential benefits including everything from the early detection of chronic disease and new treatments to improved medical products, patient outcomes and healthcare decision making, there is much room for optimism.
Despite the promise and potential benefits 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 pharmaceutical company or healthcare system may not know how to apply machine learning to the analysis of 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 clinical and research use cases.
In this ebook, you will learn:
Download the eBook, Analyzing Real-World Evidence at Scale, to learn more.
The findings from these real-world evidence based studies can be applied to a broad set of use cases, such as clinical research, trial design, the delivery of health care, regulatory decision making, and more. With the potential benefits including everything from the early detection of chronic disease and new treatments to improved medical products, patient outcomes and healthcare decision making, there is much room for optimism.
Despite the promise and potential benefits 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 pharmaceutical company or healthcare system may not know how to apply machine learning to the analysis of 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 clinical and research use cases.
In this ebook, you will learn:
- 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 patient populations
Download the eBook, Analyzing Real-World Evidence at Scale, to learn more.