Advancing Clinical Data Management: From EDC Discrepancy Resolution to Compliance and Data Integrity Across the Trial Lifecycle

Abstract

The clinical trial continuum requires the use of Clinical Data Management (CDM) to facilitate data quality, consistency and adherence to regulatory standards and requirements. Traditional manual data conflict detection and correction may be inefficient and time consuming, which can easily damage the quality, integrity, and timely locking of the database. This paper presents an automated Electronic Data Capture (EDC) discrepancy resolution and compliance verification platform that can be used to enhance data management workflow efficiency and regulatory compliance. The proposed three-stage workflow consists of a Query Detection Layer to identify discrepancies in data, an Automated Classification Engine that applies rules for triaged and automated management of discrepancies and disputes, and a Compliance Verification Module that checks/evaluates the audit trail to ensure that discrepancies resolved meet 21 CFR Part 11 and ICH E6(R3) guidelines. Results from the analysis of datasets, from multi-center clinical trials, indicated that the automated discrepancy resolution process was able to resolve 81.5% of discrepancies identified, reduce the time researchers would need to review discrepancies and differences by 35%-40% per query, and achieve 95% compliance with acceptable standards of accuracy for the verification outputs. Overall, the results of this exploratory investigation indicate that the automated processes embedded within EDC-focused CDM processes could significantly improve data quality, compliance and efficiency, providing an important platform for sustainable, technological enabled data governance throughout the life-cycle of a clinical study.