Navigating real-world data sources in rheumatology: opportunities, pitfalls, and practical guidance.

Abstract

Rheumatology research increasingly relies on diverse real-world data sources to complement insights from randomised controlled trials. Real-world evidence or observational data derived from disease or treatment registries, administrative claims datasets, electronic health records, and distributed data networks can enable large-scale analyses of treatment effectiveness, safety, and healthcare utilisation that can improve patient care and outcomes. This review provides a structured overview of the key real-world data sources currently used in rheumatology, highlighting their strengths, limitations, and opportunities. While no single dataset is without limitations, aligning the right source to the right clinical research question requires careful attention to data provenance, data quality, generalisability, and reproducibility. We outline 10 key considerations to guide both healthcare professionals and researchers who work with observational data to critically appraise real-world studies and design robust, fit-for-purpose research. With the increasing use of artificial intelligence and machine learning being applied to health data, the review provides timely guidance on data considerations to reduce potential training and algorithmic biases. Recognising the trade-offs of different data sources and applying rigorous, transparent methods are essential to generate evidence that not only withstands scientific scrutiny but also meaningfully advances patient care and rheumatology research.