Mapping TriNetX-Based Real-World Evidence Publications by Clinical Domain and Study Purpose, 2018-2025: A...

Background/Objectives: Federated electronic health record networks are increasingly used for real-world evidence generation, but publication-use patterns for TriNetX remain incompletely characterized. This study mapped Web of Science-indexed articles that explicitly mentioned TriNetX and examined their distribution across clinical domains and study purposes. Methods: We searched the Web of Science Core Collection for articles published during 2018-2025 with "TriNetX" in the title, abstract, or author keywords. Bibliometric indicators, journal sources, citation impact, author-keyword co-occurrence, and trend topics were analyzed using R, bibliometrix, Biblioshiny, and VOSviewer. A reproducible rule-based dictionary classified clinical domains and study purposes, and a supplementary Scopus analysis assessed pattern-level reproducibility. Results: The corpus included 1573 articles. Annual output increased from 1 article in 2018 to 871 in 2025. Corresponding-author output was concentrated in the United States and Taiwan. Highly cited studies were dominated by COVID-19 research. Keyword analyses suggested a shift from pandemic-related topics toward mortality, cardiometabolic disease, and medication-related outcomes. Domain-purpose mapping showed that risk/prognosis studies were most common, whereas effectiveness, health services, and method/validation studies were less frequent. Scopus validation reproduced the main annual, source-level, and domain-purpose patterns. Conclusions: TriNetX-based publications expanded rapidly and diversified across clinical fields. Domain-purpose mapping shows where research has concentrated and where it remains sparse; lower frequency, however, should not be read as a definitive clinical evidence gap, since some areas may be less suited to the platform. Where the network is well suited, future work could give greater attention to comparative effectiveness, long-term safety, care pathways, and phenotype validation.