Big Data in Internal Medicine: Sources and Challenges

Big data has become an important research resource in internal medicine. Major data sources in Korea include national claims databases, health screening records, registry data, mortality statistics, survey data, hospital electronic health records, and emerging biobig data resources. These datasets provide new opportunities for disease surveillance, outcome prediction, and evaluation of real-world practice across a wide range of chronic diseases. Nevertheless, large-scale data do not guarantee valid evidence. Diagnostic misclassification, limited clinical details, incomplete medication information, residual confounding, and difficulties in data linkage and standardization remain major challenges. Therefore, the value of big data in internal medicine depends not only on data volume but also on data quality, appropriate linkage, validated definitions, and rigorous study design. Big data should be recognized as a complementary tool that can strengthen, but not replace, clinical reasoning and conventional clinical research.