Proper Methods for Correcting Source Data

When joining a pharmaceutical or medical device company, one of the first procedures employees learn is the proper method for correcting source data.

When source data must be corrected, the original data must be crossed out with a single line in a manner that ensures it remains legible. The correction must be accompanied by the reason for the correction, the corrector’s signature, and the date of correction.

In the past, double lines were commonly used for strikethroughs in Japan. However, international standards, particularly those followed by the FDA (U.S. Food and Drug Administration) and European regulatory authorities, predominantly use single lines to ensure better legibility. The current international best practice, aligned with global harmonization efforts, is to use a single line for corrections. This practice is essential for maintaining data integrity and ensuring that corrections are clear and traceable during audits and inspections.

Why Is This Procedure Necessary?

One might reasonably ask: why not simply overwrite the data if the correction is accurate?

The answer lies in the possibility that the correction itself may be erroneous.

Even when correctors believe they have made accurate modifications, misunderstandings or assumptions can occur, meaning the original data may have been correct after all. This principle is fundamental to the ALCOA+ framework (Attributable, Legible, Contemporaneous, Original, Accurate, Complete, Consistent, Enduring, Available) for data integrity, which has been adopted by regulatory authorities worldwide including the FDA, EMA (European Medicines Agency), MHRA (UK Medicines and Healthcare products Regulatory Agency), and PMDA (Japan’s Pharmaceuticals and Medical Devices Agency).

Observations from Quality Audits

As a professional auditor, I conduct numerous audits at pharmaceutical and medical device companies. During these audits, I review manufacturing records and testing records.

I frequently encounter cases where these records contain numerous error corrections.

Correcting errors is not inherently problematic. However, my concerns are: “Were these truly transcription errors?” and “If they were errors, is this really the complete extent of them?”

Understanding the Root Cause

When operators make frequent errors, several underlying issues may be present:

• Carelessness or lack of attention to detail
• Unsuitability for the assigned tasks
• Excessive workload or time pressure
• Insufficient training and education
• Incomplete understanding of Standard Operating Procedures (SOPs)

From a data integrity perspective, according to guidance from PIC/S (Pharmaceutical Inspection Co-operation Scheme) and FDA’s “Data Integrity and Compliance with CGMP” (2018), frequent corrections can indicate systemic issues with data quality assurance processes.

Quality Assurance Department Responsibilities

In most cases, Quality Assurance departments fail to adequately address these issues during release determinations.

When errors are frequent, it is essential to:

1. Thoroughly investigate root causes through systematic failure analysis
2. Issue Corrective and Preventive Actions (CAPA) to minimize future errors
3. Implement comprehensive reviews of other data to verify no additional correction oversights exist
4. Establish robust monitoring systems aligned with data governance principles

Professional Auditor Training

During my training as an auditor, I was taught that records must be carefully scrutinized with particular attention to correction points.

This principle aligns with regulatory expectations across all major jurisdictions. According to current GMP guidelines, corrections in records are often indicators of potential systemic issues requiring deeper investigation. The FDA, EMA, and PMDA guidelines all emphasize that patterns of frequent corrections should trigger quality system reviews and may indicate inadequate data integrity controls.

Modern Data Integrity Requirements

In accordance with current international standards (2024-2025), pharmaceutical and medical device companies must implement comprehensive data integrity programs that include:

Data Integrity Component	Requirement Description
ALCOA+ Principles	All data must meet the expanded criteria: Attributable, Legible, Contemporaneous, Original, Accurate, Complete, Consistent, Enduring, and Available
Audit Trail	Complete electronic audit trails for all data modifications, including who made changes, when, and why
Data Governance	Formal data governance structures with designated Data Integrity Officers or equivalent roles
Risk Assessment	Regular data integrity risk assessments across the entire data lifecycle
Training Programs	Comprehensive and ongoing training on data integrity principles for all personnel
Technical Controls	Implementation of validated computerized systems with appropriate access controls and automated data capture where feasible
Quality Culture	Establishment of a quality culture that emphasizes the importance of accurate, complete, and reliable data

Conclusion

The proper correction of source data is not merely a procedural formality—it is a critical component of ensuring data integrity, regulatory compliance, and ultimately, patient safety. By maintaining rigorous standards for data correction and conducting thorough investigations when patterns of errors emerge, pharmaceutical and medical device companies can demonstrate their commitment to quality and compliance with global regulatory standards.

As emphasized in current regulatory guidance, “If it isn’t documented, it didn’t happen”—but equally important is ensuring that what is documented is accurate, complete, and properly maintained throughout its lifecycle. This fundamental principle underpins all Good Manufacturing Practice (GMP), Good Clinical Practice (GCP), and Good Laboratory Practice (GLP) requirements worldwide.