Convergence: Harmonising Security, Privacy, Federation, and Common Data Models in Medical Research

Introduction The advent of Trusted Research Environments (TREs) is transforming medical research by balancing data security and privacy. This abstract explores the convergence of security, privacy, federation, and common data models within TREs. By promoting TRE adoption, we advocate for the highest standards of data protection while facilitating comprehensive medical analyses. Methods TREs are secure enclaves where sensitive data is processed confidentially, adhering to the Five Safes principles safe people, safe projects, safe settings, safe data, and safe outputs. These principles ensure that patient information is protected, addressing privacy concerns and fostering trust among stakeholders. Federation allows analyses across multiple TREs seamlessly, enabling researchers to access and analyse dispersed data without centralising it. This approach preserves data sovereignty and reduces risks associated with large-scale data transfers. The integration of the Observational Medical Outcomes Partnership (OMOP) common data model standardises data formats across sources, allowing precise and consistent complex queries. This harmonisation accelerates medical discoveries by providing a unified framework for data interpretation and analysis. Results This ongoing work aims to establish a comprehensive national TRE framework, promoting widespread TRE adoption across the country. The goal is to achieve a standardised agreement among data custodians at both state and federal levels. Discussion In summary, the convergence of security, privacy, federation, and common data models within TREs represents a paradigm shift in medical research. This integrated approach maintains data integrity and privacy while empowering researchers with advanced tools for collaborative analysis.