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

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.

来自疾病或治疗登记处、行政索赔数据集、电子健康记录和分布式数据网络的真实世界证据或观察数据，可以进行大规模的治疗效果、安全性和医疗利用分析，从而改善患者护理和结果。

This review provides a structured overview of the key real-world data sources currently used in rheumatology, highlighting their strengths, limitations, and opportunities.

本综述提供了当前在风湿病学中使用的现实世界数据关键来源的结构化概述，突出了它们的优势、局限性和机会。

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.

尽管没有任何单一数据集是完美无缺的，但将正确的来源与正确的临床研究问题相匹配需要仔细关注数据来源、数据质量、普遍性和可重复性。

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.

我们概述了10个关键考虑因素，以指导使用观察数据的医疗专业人员和研究人员批判性地评估现实世界的研究，并设计出健壮、适合目的的研究。

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.

随着人工智能和机器学习越来越多地应用于健康数据，本综述提供了及时的指导，以减少潜在的训练和算法偏见的数据考虑。

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.

认识到不同数据源的权衡，并应用严格、透明的方法是生成不仅能够经受科学审查，而且能够真正推进患者护理和风湿病研究的证据的关键。