Overview of transforming observational health data to OMOP
High-quality data is essential for generating real-world insights and reliable clinical research. This webinar explores the best practices for data quality assurance using OMOP Common Data Model and explores how the OHDSI community tackles these sources.
We will provide a technical look at how integrating tools like the Data Quality Dashboard (DQD) and Achilles into ETL pipelines can systematically check your data to determine whether or not your observational data is fit for large-scale analytics. Our goal is to enhance the reproducibility of real-world data and provide you with strategies to strengthen your ETL process and data pipelines.
This session is for data engineers, data scientists, and ETL developers responsible for managing observational health data and ensuring its integrity for analysis.
