But there is evidence to suggest laboratory personnel may not be following the practices outlined in these regulations. Regulators have turned up clearly unacceptable practices when they compared results on paper or manually recorded to the complete results digitally logged by the measurement instruments. The electronic records have revealed cases of testing a sample multiple times to obtain the “right answer,” or adjusting the meta data (sample weight, dilution factor, volume) in a calculation to ensure that a specification is met.
Regulatory Oversight and Enforcement
As with any new regulation, including the Food Safety Modernization Act of 2011, agencies tend to focus their regulatory attention on the most urgent risks. The FDA only ramped up its focus on data integrity in the pharmaceutical industry following some high-profile cases in which test laboratories, such as New Jersey’s Able Laboratories, were found to be deliberately falsifying records supporting pharmaceutical products.
Today, global pharmaceutical regulators are inspecting both the quality systems and laboratory records. They’re comparing paper records to the raw, electronic data to search for suspicious or anomalous test results that may not have been reported in official documentation.
Data integrity in the food industry is complicated by overlapping areas of oversight between the FDA, which regulates most processed food, and the USDA, which regulates meat, poultry, and egg production. In January 2018, the two agencies announced an agreement to work together to “increase clarity, efficiency, and potentially reduce the number of establishments subject to the dual regulatory requirements of the USDA and the FDA.” The increased coordination between the agencies will increase the focus on data integrity in the laboratory. One area that deserves further exploration is how to securely share the original electronic data from testing food products and ingredients. This would boost confidence in the authenticity of quality data shared during “business-to-business food ingredient transactions.”
When humans create the data, calculate results, and then transcribe the “final results” into the record, there is always opportunity for errors to occur, but seamless and automated data creation and transfer can minimize accidental errors. Be wise to always remember when the analytical data are too good to be true, they probably are.
Longden is the senior marketing manager for Informatics Regulatory Compliance at Waters Corp. Reach her at email@example.com.