Real World Evidence – Project Volunteer Board
Welcome! |
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Interested in joining one of the Real World Evidence Working Group Projects? The projects below welcome new volunteers. |
Project Scope: The goal of this project is to develop a comprehensive, cross-disciplinary resource to support individuals and organisations in applying advanced privacy-preserving techniques to real-world data (RWD). Given the evolving landscape of privacy regulations and the increasing complexity of data sources – including integrating machine learning (ML), artificial intelligence (AI) and large language models (LLMs) into healthcare pipelines – this project will serve as a critical foundation for advancing privacy-preserving data integration frameworks for RWD. | Regular Project Meeting Day/Time:
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Project Scope: We aim to publish a white paper which shall explore multiple models available for missing data imputation and share with the wider group the potential of each model and its efficiency in dealing with different kinds of missing data. The model efficiency is compared using a single open-source simulated dataset. | Regular Project Meeting Day/Time:
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Project Scope: The scope of the project includes developing awareness of data standards specific to real-world data to support regulatory purposes. Common data models (CDMs) and taxonomies that are specific to the most commonly used real-world data sources by industry, member companies and regulatory bodies across the globe will be in scope, such as electronic health record sources, patient reported outcomes, widely accepted claims data sources, and other commonly used observational data. OMOP and the OHDSI community will be the focus, and will be evaluated against other CDMs and standards (such as PCORnet, Sentinel and CDISC). | Regular Project Meeting Day/Time:
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