Data Integrity in 2025: A Non-Negotiable Pillar of Medical Device Compliance

Data integrity has always been fundamental to regulatory compliance, but in 2025 it is squarely in the spotlight. Regulators around the world, led by the FDA and European authorities, are intensifying scrutiny of how companies generate, record, store, and use data. Whether it is design documentation, manufacturing batch records, clinical trial data, or post-market surveillance reports, regulators demand assurance that the data is accurate, consistent, and trustworthy.

The concept is often captured by the acronym ALCOA+: data must be Attributable, Legible, Contemporaneous, Original, Accurate, and also Complete, Consistent, Enduring, and Available. These principles apply not only to paper records but to the increasingly digital world of electronic quality systems, cloud-based clinical trial platforms, and AI-enabled devices.

Failures in data integrity are one of the fastest ways to lose regulator trust. In recent years, FDA warning letters and EU Notified Body nonconformities have cited missing audit trails, backdated entries, incomplete records, and lack of system validation. The consequences range from delayed approvals to suspended certificates and, in severe cases, import bans or market withdrawals.

This blog explores the regulatory expectations for data integrity, common pitfalls, case examples, and what executives should do to embed a culture of integrity across the organization.

Regulatory Context

United States

The FDA has made data integrity a key enforcement priority. Inspections now routinely include detailed reviews of electronic systems, audit trails, and backup records. Investigators look for signs of “data manipulation,” such as overwriting results or delayed recording. In its quality system inspections, FDA has cited firms for failing to validate software used in quality processes, for incomplete complaint records, and for backdating entries in CAPA logs.

FDA’s alignment with ISO 13485 under the upcoming QMSR further reinforces the expectation that data integrity is integrated into quality management. Manufacturers must demonstrate that all records are accurate, contemporaneous, and retrievable for review.

European Union

The EU MDR and IVDR require robust documentation across technical files, clinical evaluation reports, and post-market surveillance. Notified Bodies have increased scrutiny of whether evidence provided is traceable and verifiable. They now expect to see audit trails for electronic systems, documented procedures for record corrections, and consistent data across submissions and PMS reports. Discrepancies are often grounds for nonconformities.

Other Markets

Global regulators from ANVISA in Brazil to NMPA in China are converging on similar expectations. Many align with PIC/S guidance and ICH principles for data integrity in clinical and manufacturing contexts. Companies operating globally must assume that data integrity issues identified in one market could quickly undermine confidence in others.

Why Data Integrity Matters

Data integrity is not a paperwork issue. It is about trust. Regulators make decisions about patient safety based on the data companies provide. If that data is unreliable, regulators cannot trust the device is safe and effective. Investors, partners, and patients also expect assurance that the evidence supporting a product is sound.

For companies, poor data integrity has tangible costs:

• Re-inspections and remediation costs.

• Delayed submissions or rejections.

• Suspended CE certificates or FDA import alerts.

• Reputational harm and lost customer trust.

Strong data integrity, on the other hand, can streamline approvals, reduce inspection risk, and provide confidence to stakeholders.

Practical Implementation Steps

1. Validate Electronic Systems

Electronic systems used for quality, manufacturing, or clinical processes must be validated to ensure they function as intended. This includes:

• eQMS platforms for document control, CAPA, and training.

• ERP or MES systems for production records.

• Clinical trial data management systems.Validation should confirm accuracy, security, and reliability of data entry, storage, and retrieval.

2. Maintain Audit Trails

Audit trails must be enabled and reviewed. Regulators expect systems to automatically capture who entered or changed data, when, and why. Turning off audit trails or failing to review them regularly is a major compliance red flag.

3. Enforce Contemporaneous Recording

Data should be recorded at the time the activity occurs, not afterward. Backdating entries or filling in records later undermines credibility. Training and oversight are essential to enforce this principle.

4. Secure Original Records

Maintain original records, whether electronic or paper. If transcribed or transferred, ensure the original is preserved and the copy is verified. Regulators expect clear traceability back to the source.

5. Standardize Corrections

Develop SOPs for making corrections. For electronic systems, ensure corrected entries retain visibility of the original. For paper, corrections should be single-line cross-outs with initials and dates, never erasures.

6. Train Staff on ALCOA+ Principles

Staff at every level must understand the importance of accurate, timely, and complete data. Training should emphasize real-world examples of integrity failures and their consequences.

Common Pitfalls and Case Examples

• Incomplete Records: A manufacturer was cited by FDA for complaint files that lacked investigations or conclusions. Regulators considered the records misleading, even though the device itself was compliant.

  
  

• System Not Validated: A company implemented a new eQMS without validating it. During inspection, FDA found discrepancies in training records and issued a 483.

  
  

• Audit Trails Disabled: An EU Notified Body suspended a CE certificate after discovering that audit trails on a manufacturing system were turned off to “save storage space.”

  
  

• Backdating: In one case, investigators discovered multiple CAPAs had been closed on the same day, all retroactively dated to months earlier. This was interpreted as falsification, leading to a warning letter.

  
  

These examples show how even seemingly small lapses can escalate into major findings.

Strategic Implications for Executives

For CEOs, data integrity is a reputational and strategic issue. A single finding of falsified or unreliable data can undermine investor confidence and jeopardize partnerships. Executives should ensure data integrity is included in enterprise risk management frameworks.

For QA/RA leaders, data integrity must be a focus of audits, training, and culture. Leaders should establish metrics such as audit trail reviews completed, record error rates, and training compliance.

For operations and IT leaders, collaboration is critical. Quality cannot ensure data integrity alone. IT must implement secure systems, and operations must enforce contemporaneous recording on the shop floor.

Building a Culture of Integrity

Policies and systems are necessary but not sufficient. True data integrity requires culture. Employees must feel empowered to report errors honestly and discouraged from “fixing” data after the fact. Executives should model transparency, praising those who surface problems rather than punishing them.

Embedding integrity into values, training, and performance evaluations helps prevent lapses. A strong culture ensures compliance even when regulators are not looking.

Final Thoughts

In 2025, regulators are crystal clear: data integrity is a non-negotiable pillar of compliance. Firms that fail to maintain accurate, consistent, and trustworthy records risk losing market access and reputational standing. Firms that succeed will find inspections smoother, submissions faster, and stakeholder confidence stronger.

How PRP Compliance Can Help: We provide data integrity audits, system validation support, and training programs that embed ALCOA+ principles across your organization. Contact PRP Compliance to safeguard your compliance and strengthen the trustworthiness of your data.