Division of Endocrinology and Metabolism, Department of Internal Medicine, Konkuk University Medical Center, Kokuk University School of Medicine, Seoul, Korea
Big data–driven non-randomized studies (NRS) are an increasingly important source of observational evidence in evidence-based medicine, particularly when randomized controlled trials are limited, infeasible, or insufficiently generalizable to routine clinical practice. In Korea, this shift is especially pronounced because a single-payer national health insurance system enables near-complete population coverage, longitudinal follow-up, and linkage of healthcare utilization, prescriptions, and mortality data. These structural advantages, however, also create distinctive interpretative challenges. In big data–based NRS, flexible choices in population definition, exposure classification, index dates, follow-up windows, and outcome specification—together with reimbursement-driven healthcare utilization, frequent policy changes, rapid demographic aging, and evolving standards of care—can render observed associations vulnerable to residual confounding and overinterpretation. Advanced analytic approaches may improve internal validity, but they cannot fully resolve ambiguities related to population specification, temporal structure, or unmeasured contextual factors. This review discusses how to interpret big data–driven NRS using the Korean healthcare system as a representative example. We summarize the strengths that make large-scale observational research indispensable, delineate structural, institutional, and temporal factors that complicate causal inference, and propose practical principles for responsible interpretation. We emphasize the complementary responsibilities of researchers, reviewers and editors, and guideline developers in supporting transparent design, clinically plausible interpretation, and calibrated use of observational evidence in recommendations. A context-aware and proportionate approach is essential to ensure that expanding observational evidence strengthens—rather than distorts—evidence-based clinical and policy decision-making in rapidly evolving healthcare systems with complex institutional incentives.
