Clinical Documents QC: Source Data Verification Using AI and Rules-Based Approaches

Clinical documents play a critical role in demonstrating the safety, efficacy, and quality of clinical research. Documents often contain information derived from multiple source systems, including Electronic Health Records (EHRs), Electronic Data Capture (EDC) systems, laboratory systems, medical records, and clinical study databases.

A key quality control (QC) challenge is ensuring that the information presented in these documents accurately reflects the underlying source data. Even when a document is well written, complete, and internally consistent, it may still contain errors if the underlying values, dates, clinical events, or interpretations do not match the source records.

Effective clinical document QC must therefore prioritize source data verification (SDV): systematically verifying that information reported in a clinical document is accurate, traceable, and supported by the original source evidence.