Reconstructing Clinical-Trial EDC Workflows from Audit Trails: A Critical Review of Event-Log Design and Interpretation

Audit trails in clinical-trial electronic data capture (EDC) systems record who created or changed trial data; when; and, where applicable, why. Process-mining methods could use these time-stamped records to reconstruct data-management workflows. This structured critical narrative review draws on process-mining methodology, healthcare and electronic health record (EHR) audit-log research, and regulatory and industry sources to examine how event-log design constrains what reconstructed EDC workflows can show. Documented PubMed and Crossref searches were run on 28 October 2026. Audit trails are not designed as event logs: event definition, case notion, timestamp semantics, human and system actions, configuration changes, extraction cut-off, and system boundaries all shape the reconstructed process. The searches identified no peer-reviewed study that evaluated process mining of EDC audit trails against independently established quality outcomes, and most transferable evidence is analogical. The review proposes an interpretive framework that separates process observations, process signals, contextual interpretation, independent verification, and verification outcomes; illustrates it with a synthetic event log; and outlines an evaluation design and proposed reporting items. Audit-trail process analysis could support targeted human review within risk-based quality management, but its usefulness remains to be established, and alone it cannot establish data-quality failure, noncompliance, or poor site performance.