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In today’s highly regulated laboratory environment, maintaining data integrity is not just a matter of good practice—it’s essential for compliance, reliability, and the overall success of lab operations. For lab managers and IT support staff, ensuring data integrity means safeguarding data’s accuracy, completeness, and consistency throughout its lifecycle.
Most off-the-shelf lab informatics systems include robust data integrity controls, providing labs a headstart on data integrity. However, when labs add instruments and equipment to their workflows, they often transfer information between this hardware and their existing software systems via files. When they do this, they may unintentionally weaken data integrity and even become non-compliant.
Many labs use these file-based data instrument integrations, storing files on a shared network server with inadequate access or data integrity controls. This can lead to files being updated without authorization, files out of sync with the lab informatics system, or the wrong file used during a workflow.
Labs need to know the key principles of data integrity and which U.S. Food and Drug Administration (FDA) rules might apply. You’ll also want to understand how the ALCOA (Attributable, Legible, Contemporaneous, Original, Accurate) framework and its iterations (ALCOA+ and ALCOA++) can help your lab achieve high data integrity standards for compliance, and other steps you can take to maintain your business reputation and trust.
Data integrity refers to the assurance that data is accurate, complete, and reliable. It encompasses measures to prevent unauthorized access and ensure data remains unaltered from creation to archiving. If updates are necessary, they should be transparent and traceable. In a laboratory setting, data integrity is essential for regulatory compliance, reproducibility, and decision-making.
Regulatory compliance requires that data integrity is maintained throughout a workflow. Regulatory bodies, such as the FDA, have stringent requirements for data integrity, especially in labs in the pharmaceutical, biotechnology, and clinical research sectors.
Key FDA standards that might apply to your lab include:
Labs seeking Clinical Laboratory Improvement Amendments (CLIA) certification or College of American Pathologists (CAP) accreditation also need to ensure data integrity.
Data integrity is also important for reproducibility, the cornerstone of scientific research. Labs—including those with an open science mandate—must ensure data is accurate and reliable to facilitate peer review and to enable other researchers to validate and replicate experiments. By maintaining high data integrity standards, your lab can uphold scientific rigor, drive innovation, and contribute to advancements in your field.
Reliable data also underpins sound business decision-making. Whether your lab is investing in product development, wanting to enhance quality control, making regulatory submissions, allocating resources, or making choices about research direction, you’ll need to be sure your decisions are based on accurate data-driven insights. Data integrity controls can provide confidence that decisions stem from trustworthy information.
All lab software solutions should be designed with data integrity in mind. However, labs adding new instruments, like automated liquid handlers, should add data integrity as a documented requirement, whether using a file-based or API-based integration solution.
For existing equipment integrations, labs also need to assess the integrity of their data from end to end. This is particularly important for labs with file-based integrations. A reliance on files for recording and transferring data can be problematic due to how the files are created, where they are stored, and who has access to them.
An external consultant who understands the regulatory needs of your sector and follows software engineering best practices can help with this task. However, before hiring a consultant, confirm that they are familiar with and willing to apply the ALCOA++ data integrity framework. If your internal IT support team will perform the task, they should use the framework, too.
The original ALCOA framework, defined by the FDA, introduced five data integrity principles:
The next iteration, the ALCOA+ framework, introduced four additional data integrity principles:
The most recent update, the ALCOA++ framework, introduced another key principle:
The European Medicines Agency describes each principle in more detail.
While the ALCOA++ framework is a valuable tool, we recommend labs also consider taking several other measures to support data integrity.
Labs that must comply with regulatory requirements, and those that will have to in the coming years, must pay close attention to data integrity. FDA auditors can easily recognize when the integrity of integration data does not comply with regulatory rules or is at risk of non-compliance. Learning about this topic now will enable your lab to fix any potential compliance problems before you are forced to by an auditor.
Furthermore, maintaining data integrity is a cornerstone of effective laboratory management, even for unregulated labs. By understanding and implementing ALCOA++ principles, labs can ensure their data remains accurate, reliable, and trustworthy. It also fosters a culture of quality and integrity within the lab, ultimately contributing to the advancement of scientific research and development, and improving lives.
The journey toward impeccable data integrity is one of continuous improvement, vigilance, and adherence to best practices. If your lab needs to assess existing file-based interfaces or is adding new equipment that requires integration with your lab informatics system, contact our team today.
