Q. What are the expectations of Data Integrity for Clinical Research?

A. Data integrity requirements have significantly progressed, and we are seeing greater focus on this during audits and regulatory inspections. Additionally, there is an expectation by authorities that we now review our eSystem audit trails and have a documented process to do so on a periodic basis. Data integrity is defined as the extent to which all data (whether electronic or paper-based) are complete, consistent, accurate, trustworthy, and reliable throughout the data lifecycle- from creation through archival status and their eventual destruction. Many companies are reviewing their applicable processes to ensure that they are meeting similar expectations around data integrity. Several companies have developed their internal data quality standards, implemented Data Integrity Governance or Data Governance Committees to review process, and established standards or review data quality issues. Having a fit-for-purpose data quality strategy defined is one of the foundational step’s companies should consider in ensuring data quality. There are four areas of focus in terms of data integrity; ALCOA++, computerized systems validation, access control, and metadata and audit trails. ALCOA++ means that the data must be attributable, legible, contemporaneous, original, accurate, and complete. This follows the good documentation practices and applies to both paper and electronic records. Make sure to have quality checks in place, as well as verification of data quality. All clinical information and data should be accurately recorded and handled in such a manner it can be reported, interpreted and verified while protecting the confidentiality of records during clinical trials. Additionally, computerized systems used to capture, process, report and/or store data should be developed, validated and maintained in such a way as to ensure the integrity, security and validity of all data. Validation should take a risk-based approach based on the risk assessment. Procedures are necessary to set the process and standards in which a company should follow for software validation, risk assessment, and other key governance processes. Limit the ability to record, change, and delete data to ensure data integrity. Roles must be clearly defined and assigned for each electronic system. Periodic review of the roles must be documented. Access must be removed when a user no longer requires access. Metadata is data that refers to the structure and data elements or other characteristics of the data. The metadata are typically found within the audit trail. The audit trail should capture all changes to the data, showing the old value, the updated value, who made the change, the reason for the change, and the date and time stamp of the change. Audit trail reviews should be performed periodically to identify missing data, inconsistent data, outliers, and other potentially significant errors in data collection and reporting associated with data integrity. It is important to ensure that audit trail reviews are documented.

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