Data Integrity is Not About Fraud Prevention

Patient Impact of Data Integrity Failures

Data integrity failures can have direct and potentially fatal consequences for patients. For example, if data related to the manufacturing of anticancer drugs is lost or becomes unreliable, production may be forced to halt, severely disrupting patient treatment. For patients with rare diseases or advanced cancers where alternative treatment options are limited, such situations represent a loss of therapeutic opportunity and pose serious problems directly affecting life prognosis.

When pharmaceutical products cannot be properly supplied due to data integrity issues, patients may face several consequences: treatment interruption or delay, side effects or efficacy variations associated with switching to alternative medications, and in some cases, complete loss of treatment options. These circumstances can significantly diminish patients’ quality of life (QOL) and may even affect their survival outcomes.

Critical Impact on Corporate Operations

Data integrity issues can have severe consequences that threaten a company’s very existence. Loss of trust from regulatory authorities can lead to product approval revocations or rejection of new drug applications. Regulatory agencies such as the U.S. FDA (Food and Drug Administration), European EMA (European Medicines Agency), and Japan’s PMDA (Pharmaceuticals and Medical Devices Agency) continue to strengthen data integrity requirements and take strict enforcement actions against violating companies.

Specifically, these measures include issuance of Warning Letters, Import Alerts, suspension or revocation of manufacturing and marketing approvals, and even criminal prosecution in some cases. These actions pose direct threats to business continuity.

Economic Losses from Data Integrity Issues

The economic impact can be devastating. Direct costs include product recall expenses, complete inventory disposal, comprehensive overhaul of manufacturing systems and computerized systems, external audit fees, and consultant costs. These expenses can reach hundreds of millions to billions of yen in many cases.

Additionally, indirect losses are severe and include decreased sales due to product supply interruptions, market share decline, stock price drops, brand value damage, and loss of trust from business partners and investors. Particularly, recovering trust once lost from regulatory authorities and the market is extremely difficult and affects corporate value over the long term.

In actual cases, there have been instances where companies were unable to obtain approval for new products for several years due to data integrity violations, and cases where manufacturing and sales of flagship products were suspended, resulting in significant deterioration of corporate performance.

Ripple Effects Across the Industry

Data integrity issues affect not only the company in question but can also impact the credibility of the entire pharmaceutical industry. When a company’s violation case is reported in the media, public trust in the entire industry may be damaged, potentially leading to stricter regulations. Therefore, it is essential for all pharmaceutical companies to ensure data integrity across all business processes.

Modern Regulatory Environment and Data Integrity Requirements

Regulatory requirements for data integrity are increasingly harmonized internationally. Key regulatory documents and guidelines include:

Major Regulatory Documents

• FDA “Data Integrity and Compliance With Drug CGMP” (revised 2018)
• EMA “Annex 11: Computerised Systems” (EU GMP Annex 11)
• MHRA (UK Medicines and Healthcare products Regulatory Agency) “GXP Data Integrity Guidance”
• PIC/S (Pharmaceutical Inspection Co-operation Scheme) “Good Practices for Data Management and Integrity in Regulated GMP/GDP Environments” (PI 041-1, 2021)
• Japan’s PMDA “Basic Approach to Pharmaceutical Data Integrity”

These guidelines are based on the ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, Accurate + Complete, Consistent, Enduring, Available) and require management throughout the entire data lifecycle.

Addressing Computerized Systems

In modern pharmaceutical companies, all operations—including manufacturing management, quality control, and laboratory management—are supported by computerized systems. Therefore, compliance with regulations specific to computerized systems, such as 21 CFR Part 11 (regulations on electronic records and electronic signatures) and EU GMP Annex 11, is mandatory.

This includes ensuring audit trails, access control management, data backup and disaster recovery, system validation, and cybersecurity measures.

The True Cause Threatening Data Integrity: Focus on Human Error

Approximately 80% of events threatening data integrity are reported to originate from human error. This is an extremely important point that many people misunderstand. This figure, derived from regulatory inspection reports and industry surveys, demonstrates that routine errors rather than intentional fraud are the primary risk factors.

Data integrity is not about fraud prevention; rather, it centers on addressing human errors that occur in daily operations. This recognition is crucial for building an effective data integrity program.

Types of Human Errors and Their Occurrence Mechanisms

Human errors take various forms: transcription errors, input errors, calculation errors, inadvertent mistakes, assumptions, misunderstandings, procedure omissions, insufficient verification, and decreased attention due to fatigue. While these are not intentional fraud, they can have serious impacts on patient safety and product quality.

From a psychological perspective, human errors are more likely to occur in the following situations:

When work is complex, under high time pressure, when fatigue has accumulated, when work is monotonous and attention easily wanders, and when procedures are unclear or overly complex. Errors also occur more frequently when training is insufficient or when multiple tasks are being processed simultaneously.

Severity of Errors: Fraud and Human Error are Equivalent

Whether intentional fraud or unintentional human error, both have equal severity regarding patient safety and product quality. Regulatory authorities focus on the resulting lack of data reliability, regardless of whether the cause was intentional or accidental, and require appropriate preventive measures and quality assurance systems.

For example, if a product outside specifications is shipped due to a transcription error in analytical data, it poses the same potential health risk to patients whether the cause was intentional falsification or a simple transcription error.

Distinguishing Between Fraud and Human Error: Where Responsibility Lies

A particularly noteworthy point is that intentional fraud is primarily committed by executives and managers in positions of responsibility, not by general employees. Analysis of past serious data integrity violation cases shows that most systematic fraud is caused by instructions from, acquiescence by, or inappropriate pressure from executive management or managers.

This includes instructions to falsify manufacturing records to meet sales targets, concealment of data in preparation for regulatory inspections, and omission of tests or fabrication of results for cost reduction. These are organizational problems that cannot be solved by individual morality alone.

What is Required of Employees: Prevention of Human Error

On the other hand, what is required of general employees is not the prevention of intentional fraud but the prevention of human errors in daily operations. This includes:

Accurate adherence to procedures, implementation of double-checking, ensuring contemporaneousness of records (recording data when it occurs rather than recording from memory afterward), an attitude of seeking confirmation when uncertain, and appropriate reporting when abnormalities or deviations are discovered.

This shift in recognition is the first step in effective data integrity measures. Rather than monitoring employees as “potential wrongdoers,” it is important to support them as “humans who can make errors” and establish systems and processes to prevent errors.

Building Effective Data Integrity Measures

Effective data integrity measures that assume human error require a multi-layered approach including:

Systematic Measures: Process simplification and standardization, implementation of automatic checking functions in systems, fail-safe design to prevent errors, utilization of electronic record systems to reduce transcription errors

Organizational Measures: Clear definition of roles and responsibilities, implementation of appropriate training programs, fostering an open communication culture, establishing an environment where errors are easy to report (non-punitive reporting systems)

Technical Measures: Introduction of computerized systems with audit trail functions, automatic data backup, appropriate management of access rights, establishment of data review mechanisms

Cultivation of Quality Culture: Commitment from management, organization culture prioritizing quality, attitude of continuous improvement, enhanced employee engagement

Conclusion

Data integrity is not merely a regulatory compliance issue but a management challenge directly linked to patient safety and corporate survival. Its essence lies not in fraud prevention but in systematic measures against human errors that can occur in daily operations.

When all stakeholders share this recognition and build robust systems and processes that assume human error, true data integrity can be ensured. This ultimately enables pharmaceutical companies to fulfill their most fundamental mission: reliably delivering safe and effective medicines to patients.