Data Integrity – It’s All About the Data!
In this increasingly digital world, we have to remember data is now available in many different medians, including paper. When we think of clinical trials and the critical data we collect, we have to be very cognizant of how the data is obtained, as well as how data integrity is maintained throughout its lifecycle. This includes all phases in the data lifecycle, from generation and recording through processing (including analysis, transformation, or migration), use, data retention, archive/retrieval, and destruction.
What is Data Integrity?
The Food and Drug Administration (FDA) uses the ALCOA acronym to define expectations for data, indicating data should be:
The Medicines and Healthcare products Regulatory Agency (MHRA) additionally refers to “ALCOA+.” The “+” stands for “complete, consistent, enduring, and available.” The MHRA GXP Data Integrity Guidance and Definitions focuses on promoting a risk-based approach to data management, including data risk, criticality, and lifecycle.
Data integrity is agnostic of the system used and may be collected via electronic means, on paper, a hybrid of the two, or other imaging techniques such as photographs. Instances of data integrity failure can occur at almost any point in data creation or processing in a clinical trial, so you need multi-dimensional strategies to help prevent and mitigate data integrity issues.
Assuring data integrity throughout clinical trials requires appropriate quality and risk management systems, including adherence to sound scientific principles and good documentation practices.
Why Data Integrity is Important
Ensuring data integrity is an important component of the pharmaceutical industry’s responsibility to protect the safety, efficacy, and quality of drugs, as well as a regulatory authority’s ability to protect public health. Data integrity is critical throughout the “data lifecycle,” from the initial creation of the data through any transfers, replication, or reporting stages. Data is critical to decision making, therefore, it is imperative for organizations to have systems and processes in place to assure data integrity.
How to Ensure Data Integrity
Having procedural documents, such as policies, standard operating procedures (SOPs), and risk management plans for data management available is important. However, you also need to have systems and processes in place to ensure everyone knows their role in maintaining data integrity throughout its lifecycle. The World Health Organization (WHO) draft guideline on data integrity recommends having governance control strategies in place using quality risk management principles, which enables error detection, lapses, and omissions of results and data during the data life cycle. Controls may include procedural controls, organizational controls, and functional controls.
These controls include:
- Data risk management plans
- What to assess
- When to assess
- How to assess
- When/how/who to report outcomes to
- Data integrity risk assessments
- Vendor selection and oversight
- Data monitoring
- Use of statistical methods for detecting data issues
- Validation processes and documentation
- Data governance:
- Data ownership and accountability throughout the lifecycle
- Training in the importance of data integrity principles
- Accountability, oversight, and escalation of data integrity concerns; CAPA management
- Ready availability and direct accessibility to agreed upon individuals (e.g., regulatory authorities)
Ensuring data integrity practices are in place is critical to the confidence in the data generated and maintained to ensure participant safety and being able to reconstruct clinical trial activities. It minimizes risk to regulatory authority inspectional observations.