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Laboratory Data Integrity Guide
Data Handling, SOP Guidance, Practical Examples
The Laboratory Data Integrity Guide illustrates in various examples of analytical workflows safe ways to achieve data integrity and discusses where data integrity might be at risk. Secure your measuring processes: learn more about data handling, SOP guidance and achieving data integrity.
For laboratories that must comply with GLP, GMP and GAMP regulations it is important to have records or documented evidence of all relevant analyses that can be checked by a second person and are also readily available for audits. Storing the result is not enough, each result set has to be complete and contain all relevant metadata.
In 2016, 80% of the FDA warning letters were issued due to the lack of data integrity. The main reasons were incomplete data, an aspect that can be prevented by using the right solutions. The highest risks, when not working in a compliant manner, lie in import bans, product re-calls or even the closing of production plants.
Laboratory analysts must often follow standard operating procedures (SOP) for each analysis and document the entire process as well as record the results. Many labs have turned toward LIMS and ELN systems which are designed primarily to aggregate result data from an array of analytical tests. Yet these systems are largely leaving out the so called metadata – like instrument service status, user ID and method applied – information that is crucial to put results from analytical instruments into a context and to achieve data integrity. Regulations and standards such as FDA (21 CFR Part 11), EU (Annex 11), GMP, and ISO (ISO 17025) have recognized both the advantages and limits of electronic data systems, and have increasingly established further controls for the use of such systems all the way down to bench top instruments. So reducing errors, simplifying processes, reinforcing compliance, and ultimately achieving data integrity with a system integration has become more challenging.
The Laboratory Data Integrity Guide illustrates in various examples of analytical workflows safe ways to achieve data integrity and discusses where data integrity might be at risk.