Quick Summary
Quality control labs remain a frequent source of data integrity findings, even with extensive regulatory guidance in circulation. This article distills the problem into four practical steps: build a living data integrity training program, understand your process workflow and data lifecycle, automate manual data handoffs, and review critical data in near real-time. Together they help you uphold ALCOA principles and minimize data integrity risk before issues escalate.
Even with several guidance documents published by various agencies, companies and associated staff continue to struggle with data integrity issues.
Data integrity can be one of the most complex areas of compliance, but a few simple steps can ensure that your company maintains fundamental Attributable, Legible, Contemporaneous, Original, Accurate (ALCOA) principles and minimizes data integrity risk.
USDM can help you with a plan of action.
Why ALCOA matters: Attributable, Legible, Contemporaneous, Original, and Accurate are the foundational expectations behind nearly every data integrity finding. The steps below are organized to operationalize these principles in the day-to-day reality of a QC lab.
1. Data Integrity
Many data integrity issues can be traced back to human error; therefore, ensure that data integrity starts with the user. Your employees should be trained on data integrity, data entry, and fundamental ALCOA principles. An active and evolving training program should be in place based on the operational needs of your employees and business processes. Procedures regarding data (i.e., data entry, review, and approval) should be easy to understand and available for reference. Programs should also be put in place to ensure that system administrators provide the correct level of user access based on training and role.
Disciplined user access and audit-trail controls are also a core expectation of 21 CFR Part 11 compliance, so aligning training and role-based access with electronic records requirements pays off twice.
2. Understand Your Process Workflow and Data Lifecycle
From sample hand-off, to release of results, to archiving data, your laboratory process workflow—and consequently your data flow—should be well understood and documented. A simple data map for a laboratory can help consolidate workflows, highlight potential risks, work as a road map for process improvements, and ultimately mitigate data integrity risks.
You cannot protect what you have not mapped. A clear picture of how data moves through the lab is the foundation of every other data integrity control.
3. Automate Data Workflows
Manual data entry or manual transcription can lead to poor data integrity behavior (for example, data that is jotted down on a sticky note during testing and then transferred to an approved form, or manually entered in an electronic system). The transferred data is not original, possibly duplicated, and potentially inaccurate. A data map can be used to identify areas where these types of risks to data integrity exist. Automated solutions like Electronic Lab Notebook (ELN) and Laboratory Information Management System (LIMS) should be put in place to ensure real-time data and metadata capture, and do away with paper-based or hybrid (paper and electronic) processes.
Standing up and validating these systems efficiently is where a risk-based approach to computer software assurance (CSA) helps you focus testing effort on the functions that carry the most data integrity risk.
4. Review Data for Quality and Completeness
When data errors go unnoticed, there is a greater potential for bigger issues. Reviewing critical data should be conducted by someone with expert knowledge of the operation. Reviews should be done in near real-time to ensure data integrity discrepancies are dealt with before they escalate.
Always a salient issue, the Medicine and Healthcare products Regulatory Agency (MHRA)’s data integrity guidance further highlights data integrity.
The 4-Step Data Integrity Framework at a Glance
- Step 1 — People First: Train users on ALCOA and data entry; enforce role-based access.
- Step 2 — Map the Lifecycle: Document workflow from sample hand-off to archive; build a data map.
- Step 3 — Automate: Replace paper and hybrid processes with ELN/LIMS for real-time capture.
- Step 4 — Review Continuously: Have experts review critical data in near real-time to catch discrepancies early.
Keeping these controls effective over time is an ongoing discipline. A validation lifecycle management approach helps ensure that the systems and processes protecting your data stay in a validated, compliant state as they evolve, while USDM Cloud Assurance can keep cloud-based lab systems continuously compliant.
FAQ: Data Integrity in Quality Control Labs
What are the ALCOA principles?
ALCOA stands for Attributable, Legible, Contemporaneous, Original, and Accurate. These are the fundamental principles that underpin data integrity, and maintaining them is central to minimizing data integrity risk in a QC lab.
Why do data integrity issues persist despite published guidance?
Many data integrity issues can be traced back to human error. Even with extensive guidance from regulatory agencies, gaps in training, unclear procedures, manual data handling, and incomplete data review continue to create risk.
How does automation improve data integrity?
Manual entry and transcription can introduce data that is not original, possibly duplicated, and potentially inaccurate. Automated solutions such as Electronic Lab Notebooks (ELN) and Laboratory Information Management Systems (LIMS) capture data and metadata in real time, removing paper-based and hybrid processes that create risk.
Who should review critical QC data?
Reviewing critical data should be conducted by someone with expert knowledge of the operation, and reviews should be performed in near real-time so that data integrity discrepancies are addressed before they escalate.
What is a laboratory data map and why is it useful?
A data map documents how data flows through your laboratory—from sample hand-off to release of results to archiving. It helps consolidate workflows, highlight potential risks, serve as a road map for process improvements, and ultimately mitigate data integrity risks.
Ready to strengthen data integrity in your lab?
USDM can help you assess your current state and build a practical data integrity plan of action for your quality control lab. Contact us with any questions you may have.