Data integrity is fundamental in ensuring medicines are of the required quality. Over the past number of years, the FDA, European regulatory agencies and others have increased their focus on the quality of data produced in regulated industries and have uncovered a number of areas of concern. Regulatory requirements for data integrity apply equally to manual (paper) and electronic data.
The regulatory viewpoint is that if data are not valid and trustworthy it is a sign that an entire operation or facility is out of control and cannot assure the quality of its products.
This lesson explains why data integrity is so critical, the key areas of concern to regulators, what regulators expect, and the ways in which data integrity can be built into a company Quality Management System.
The goal of the lesson is to communicate and reinforce the criticality of correct recording, storage and traceability of data produced in GxP environments. There is a particular focus on personal responsibility in ensuring data integrity.
Having taken the lesson, users should be able to:
- Explain the concept of data integrity and why it is critical in evaluating product quality.
- Describe some of the implications of data integrity issues.
- Explain key terms associated with data integrity e.g. raw data, metadata, audit trails, data lifecycle.
- Provide examples of actual data integrity issues uncovered by regulatory investigators during inspections.
- Describe the ALCOA approach to evaluating data integrity.
- Describe practical measures for integrating data integrity into a Quality Management System.
The following key messages are communicated:
- There are common citations related to data integrity that arise over and over again and they can be avoided provided proper procedures and safeguards are in place.
- Data must be recorded using the ALCOA method where ALCOA stands for Attributable, Legible, Contemporaneous, Original, Accurate.
- Computer Systems that generate electronic data are governed by industry regulations such as 21 CFR Part 11.
- Data Integrity should be integrated into Quality Management Systems.
- Personal responsibility is critical in maintaining a culture of data integrity within a regulated company.