Electronic health records research: easy evidence, harder oversight.

Electronic health record (EHR) research networks have accelerated clinical research by making it possible to study large and diverse patient populations over time. However, the quality and resources required for these studies vary widely, depending on whether they rely on simple aggregate outputs or a more rigorous patient-level data, a central but occasionally unstated distinction in EHR-based research. Patient-level data can support a more reproducible cohort construction, time-anchored exposure definitions, and more precise outcome ascertainment, whereas aggregate approaches limit rigour regarding the precise definition of index dates, the completeness of covariate capture, the accurate characterization of medication exposure windows, and the validity of outcome ascertainment. These limitations may amplify well-known threats to causal inference in observational research, including misclassification, incomplete outcome capture across health systems, residual confounding, and immortal time bias, particularly when comparative effectiveness questions are pursued. In this viewpoint, we outline why 'easy evidence' can yield fragile conclusions when analytic granularity is limited, and we propose pragmatic expectations for reporting and review: explicit declaration of data level (patient vs. aggregate), clear time-zero definitions for all arms, validated case-definition algorithms when available, sensitivity analyses aligned with plausible misclassification, and transparency sufficient to enable reproducibility. Strengthening methodological norms is essential to preserve the credibility of EHR-based evidence as its influence on clinical discourse continues to grow.