OpenSAFELY: a platform for secure, reproducible and transparent analyses by design
Arina Tamborska
(University of Oxford)
Nicholas DeVito
(University of Oxford)
Peter Inglesby
(Bennett Institute of Applied Data Science, Oxford University)
Ben Goldacre
(University of Oxford)
Paper Short Abstract
OpenSAFELY is a research platform designed with transparency, reproducibility and security in mind. It is currently deployed to the UK’s electronic health records. Come and speak to us about the platform’s philosophy, design features, and how it could be used in your work.
Paper Abstract
Electronic health records (EHR) are among the UK's most valued and sensitive data assets. To maintain credibility, research in EHR must be transparent and reproducible, and to preserve trust, it also must adhere to the highest security and privacy standards. OpenSAFELY is an analytic software platform developed to meet these needs. It enables verified researchers to run analytical code transparently and reproducibly against millions of linked health records, and returns aggregated and disclosure-proof outputs, preserving the privacy of individuals’ data.
Features that support OpenSAFELY’s reproducibility include: standardised data preparation workflows, implementation of the same computational environment across all users, a universal query language to generate analysis-ready datasets, and a library of reproducible actions. For transparency, researchers can only run analyses by sharing their code on GitHub and all analyses conducted on the secure server are logged in public. To preserve privacy, researchers do not have access to individual-level data. The analysis is prepared against the dummy data, and can only be released with disclosure controls after review by output checkers.
Our poster presents the OpenSAFELY design and how we are working to change the EHR research lanscape. We invite you to learn more about the platform's features and philosophy, engage with the OpenSAFELY researchers and developers, ask 1:1 questions, and learn how OpenSAFELY could be useful in your work.
Accepted Poster
Paper Short Abstract
Paper Abstract
Electronic health records (EHR) are among the UK's most valued and sensitive data assets. To maintain credibility, research in EHR must be transparent and reproducible, and to preserve trust, it also must adhere to the highest security and privacy standards. OpenSAFELY is an analytic software platform developed to meet these needs. It enables verified researchers to run analytical code transparently and reproducibly against millions of linked health records, and returns aggregated and disclosure-proof outputs, preserving the privacy of individuals’ data.
Features that support OpenSAFELY’s reproducibility include: standardised data preparation workflows, implementation of the same computational environment across all users, a universal query language to generate analysis-ready datasets, and a library of reproducible actions. For transparency, researchers can only run analyses by sharing their code on GitHub and all analyses conducted on the secure server are logged in public. To preserve privacy, researchers do not have access to individual-level data. The analysis is prepared against the dummy data, and can only be released with disclosure controls after review by output checkers.
Our poster presents the OpenSAFELY design and how we are working to change the EHR research lanscape. We invite you to learn more about the platform's features and philosophy, engage with the OpenSAFELY researchers and developers, ask 1:1 questions, and learn how OpenSAFELY could be useful in your work.
Poster session
Session 1 Tuesday 1 July, 2025, -