Big data research, precision medicine and real‑world evidence in autoimmune and rheumatic diseases

The unprecedented expansion of healthcare data—from electronic health records and insurance claims to biobank repositories and patient‑generated outcomes—has ushered in a new era for autoimmune and rheumatic disease research. Chronic inflammatory conditions such as rheumatoid arthritis and systemic lupus erythematosus exhibit marked interpatient variability in disease activity and treatment response. Research has historically depended on small, single‑center cohorts, limiting generalizability across diverse populations. By leveraging large‑scale, multidimensional datasets and advanced analytical methods, researchers can now uncover population‑level trends, identify subphenotypes, and monitor long‑term safety and efficacy outcomes in real‑world settings. The integration of registry data, wearable device measurements, and patient‑reported outcomes further enriches the evidence base, enabling longitudinal monitoring and the discovery of novel biomarkers. This convergence of big data and clinical inquiry promises to overcome traditional barriers, refine therapeutic strategies, and drive personalized care pathways.

This Research Topic seeks to highlight cutting‑edge methodologies and practical applications of big data analytics in autoimmune and rheumatic diseases. We aim to bridge the gap between data generation and clinical translation by inviting studies that demonstrate how electronic health records (EHR), health insurance claims, genomic and biobank data, patient‑reported outcomes (PROs), and emerging artificial intelligence (AI) techniques can collectively inform robust real‑world evidence (RWE). Contributions should articulate innovative strategies for predictive modeling, risk stratification, bias mitigation, and data integration. Particular emphasis is placed on methods that enhance the validity and reliability of observational findings, address confounding factors, and facilitate the development of precision rheumatology tools capable of optimizing individual patient outcomes.

We welcome Original Research Articles, Systematic Reviews and Meta‑Analyses, Methodology Papers, Brief Communications, and Case Studies that harness big data within the context of autoimmune and rheumatic disorders. Topics of interest include, but are not limited to:

● Comparative effectiveness research using real‑world data

● Predictive modeling and risk stratification in rheumatology

● Integration of omics datasets (genomics, proteomics) with clinical registries

● Machine learning approaches for early diagnosis or treatment optimization

● Methodological challenges such as bias, confounding, and data interoperability

● Federated and global collaborations leveraging networks like TriNetX and OHDSI