Real‐world evidence in the cloud: Tutorial on developing an end‐to‐end data and analytics pipeline using Amazon Web Services resources

In the rapidly evolving landscape of healthcare and drug development, the ability to efficiently collect, process, and analyze large volumes of real‐world data (RWD) is critical for advancing drug development. This article provides a blueprint for establishing an end‐to‐end data and analytics pipeli...

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Bibliographic Details
Published in:Clinical and translational science 2024-12, Vol.17 (12), p.e70078-n/a
Main Authors: Anderson, Wes, Bhatnagar, Roopal, Scollick, Keith, Schito, Marco, Walls, Ramona, Podichetty, Jagdeep T.
Format: Article
Language:English
Subjects:
Artificial intelligence
Case reports
Cloud Computing
Data analysis
Data encryption
Data integrity
Databases, Factual - statistics & numerical data
Decision making
Disease
Drug development
Drug Development - methods
Humans
Infections
Internet
Machine Learning
Respiratory distress syndrome
Software
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Summary:In the rapidly evolving landscape of healthcare and drug development, the ability to efficiently collect, process, and analyze large volumes of real‐world data (RWD) is critical for advancing drug development. This article provides a blueprint for establishing an end‐to‐end data and analytics pipeline in a cloud‐based environment. The pipeline presented here includes four major components, including data ingestion, transformation, visualization, and analytics, each supported by a suite of Amazon Web Services (AWS) tools. The pipeline is exemplified through the CURE ID platform, a collaborative tool designed to capture and analyze real‐world, off‐label treatment administrations. By using services such as AWS Lambda, Amazon Relational Database Service (RDS), Amazon QuickSight, and Amazon SageMaker, the pipeline facilitates the ingestion of diverse data sources, the transformation of raw data into structured formats, the creation of interactive dashboards for data visualization, and the application of advanced machine learning models for data analytics. The described architecture not only supports the needs of the CURE ID platform, but also offers a scalable and adaptable framework that can be applied across various domains to enhance data‐driven decision making beyond drug repurposing.
ISSN:1752-8054
1752-8062
1752-8062
DOI:10.1111/cts.70078