What You Need to Know About Data Integrity
Credit to Author: Keith Chambers| Date: Tue, 06 Jun 2017 09:55:08 +0000
Over the past year, I’ve noticed a growing concern over data integrity in my conversations with life sciences executives. From 2010 to 2012 the FDA cited just five drug manufacturers for data integrity violations and in 2015 that number grew to 18 warning letters related to data integrity. Last year data integrity issues triggered more than one third of all global regulatory actions.
Of the 136 cGMP noncompliance issues 43 were FDA warning letters, 36 were FDA import alerts, 16 were both import alert and warning letters and 25 were for European Union (EU) noncompliance. These issues spanned a total of 19 countries with 35% in China, 27% in India and 17% in USA. Violations are very costly for the offending companies, resulting in banned products, fines and a loss of brand reputation.
In this blog post I will provide a brief overview of data integrity regulations as well as the benefits of compliance.
To learn more about the importance of data integrity, download our whitepaper “Rising Global Concern for Data Integrity”
What is Data Integrity?
Data integrity within a Good Manufacturing Practice (GMP) environment can be defined as generating, transforming, maintaining and assuring the accuracy, completeness and consistency of data over its entire lifecycle in compliance with regulations. Although recent FDA audits and warning letters have focused on laboratory records, the data could also include manufacturing or clinical data.
Data integrity can apply to electronic and paper records, but regulators’ recent focus has been primarily on electronic records. Examples of electronic records include product and raw material specifications, bills of material, recipes, SOPs, laboratory instrument data, master production and batch records, equipment set up configurations and maintenance logs.
In the United States, the governing regulation is the FDA’s 21 CFR Part 11, while in the EU it is Annex 11. While there are differences in scope and application, both Part 11 and Annex 11 share the common intent of ensuring the integrity of electronic records.
As part of industry Good Documentation Practices (GDP) the Pharmaceutical Industry has long used ALCOA as a framework: Attributable, Legible, Contemporaneous, Original, and Accurate – ALCOA. This framework has a different application when applied to electronic records instead of paper and is at the core of data integrity.
- Attributable: The FDA expects data to be linked to its source. It should be attributable to the individual who observed and recorded the data, as well as traceable to the source. Example FDA audit observation: “Same user ID & PSWD shared between ‘users’ for the same computerized system; Disabling of Audit Trail.”
- Legible: Quality data must also be legible if it is to be considered fit for use. Example observation from an FDA audit: “Archived data not retrievable.”
- Contemporaneous: This refers to the timing of data collection with respect to the time of the observation. In short, the more promptly an observation is recorded, the better the quality. Example FDA audit observation: “Not entering values on the Batch Record at the time activities where performed; Lack of controls to changes to the date/time on computerized system.”
- Original: Original data is considered to be the first and therefore the most accurate and reliable recording of data. The terms “source data” or “raw data” embody this concept and are used interchangeably. Example FDA audit observation: “Failure to report OOS; incomplete data: failure to retain electronic raw data (including related metadata), only printouts are available”
- Accurate: Accuracy is an implied element of data quality under the GxP regulations. The definition of accurate is: conforming exactly to truth or to a standard; EXACT and able to give an accurate result. Example FDA audit observation: “unvalidated systems being used (or not qualified for their intended use); failure to have procedures in place (including change control).”
Why is Data Integrity Important?
Recent violations are putting data integrity in the cross hairs for regulators. This has resulted in an increased focus on data integrity programs and assessment of current systems and processes to build the infrastructure necessary to comply with current and future uses of electronic record keeping.
Data integrity protects from regulatory violations by ensuring:
- Records cannot be altered
- Data is readily available for review during the required retention time
- Records are kept electronically, in paper or in a hybrid format
Implementing solutions to digitize data management, integrate workflow and automate processes helps ensure data integrity. Digitalizing data capture and processes improves data accuracy, eliminates issues associated with paper-based tracking, improves efficiency of data record review and approval processes, maintains security and ensures regulatory compliance with Part 11 requirements. To better understand data integrity and the solutions available, read our whitepaper “Rising Global Concern for Data Integrity”.
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