Dave Rushton
Founder
of Paradigm Shift Consulting Limited.
Dave Rushton is Founder of Paradigm Shift Consulting Limited, a Good Distribution Practice Consultancy.
Data Integrity Compliance in GDP
Data Integrity – Compliance in GDP
Data integrity is the degree to which data is complete, consistent, accurate, trustworthy and reliable, and that these characteristics of the data are maintained throughout the data lifecycle.
Data integrity is essential for ensuring the quality, safety, and efficacy of medicinal products, as well as for protecting public health and maintaining trust in the pharmaceutical industry. It includes not only digital data, but also all paper-based systems in all forms.
Good Distribution Practice (GDP) is the part of quality assurance that ensures that medicinal products are consistently stored, transported and handled under suitable conditions as required by the marketing authorisation (MA) or product specification. GDP applies to all parties involved in the distribution of medicinal products, from manufacturers to wholesalers, distributors, brokers and suppliers.
Data Integrity Disciplines
- Ownership: The roles and responsibilities of data owners, custodians and users should be clearly defined and documented. Data custodians (RPs and Management) are responsible for the storage, maintenance, and protection of the data they manage. Data users are responsible for accessing, using and interpreting the data they need correctly.
- Quality: The data quality attributes (such as accuracy, completeness, consistency, reliability, timeliness and traceability) should be defined and measured. Data quality indicators and metrics should be established and monitored to ensure that the data meet the predefined quality criteria.
- Risk assessment: The potential risks to data integrity should be identified and assessed for each type of data. The risk assessment should consider the data lifecycle stages, sources, formats, processing methods, storage locations, retention periods, access rights and restrictions, and the security measures.
- Validation: The methods and criteria for verifying and validating the generated data should be specified and documented. Data validation should ensure that the data are fit for their intended purpose and comply with the applicable regulations and standards.
- Documentation: The documentation of the data should provide a clear and complete record of the data origin, history, content, format, structure, meaning, quality and usage. Data documentation should include metadata, standard operating procedures (SOPs), audit trails (logs of data actions), change controls (records of data modifications), error reports (records of data anomalies) and corrective actions (measures taken to resolve data issues).
- Training: The personnel involved in GDP should receive appropriate training on the principles and practices of data integrity. It should also include awareness of the ethical and legal implications of data integrity breaches. This includes simple form-filling, managing shipping paperwork, customer services records, maintenance and cleaning records, and digital input of data wherever it occurs.
- Data audit: The performance and effectiveness of the data governance system should be periodically reviewed and evaluated by internal or external auditors. Data audit should verify that the data governance system is implemented correctly and consistently across all parties involved in GDP. It should also identify any gaps or weaknesses in the data governance system and recommend improvements or corrective actions.
All primary and secondary data records and logs must be retained for a minimum of 5 years.
Primary Data Systems, Secondary Data Systems & Meta-data.
- A primary data system is a system that generates or collects data directly from the source, such as a manufacturing process, a laboratory test, or a clinical trial.
- A secondary data system is a system that receives, processes or stores data from a primary data system or another secondary data system, such as a database, a spreadsheet or a report.
- Metadata means “data about data”. Metadata is defined as the data providing information about one or more aspects of the data; it is used to summarize basic information about data that can make tracking and working with specific data easier. Some examples include: Means of creation, purpose, equipment used, and other associated information, but not data content itself.
For example: An original photograph is the primary data. A scan of the photo committed to a digital system is the secondary data. The metadata is the text footnote accompanying the digital copy of the image that indicates the camera used, who took the photo and when.
Both primary and secondary data systems should be designed, validated, maintained and controlled according to the principles of good distribution practice (GDP) and relevant standards, such as ISO 13485 for quality management systems.
Data should be recorded, stored and transferred in a secure and traceable manner, using appropriate formats, methods and technologies. The data should also be reviewed, verified and approved by authorised personnel before being used for decision making or reporting.
The role of the Responsible Person
The Responsible Person (RP) is a key role in ensuring data integrity in an organization. The RP is responsible for overseeing the data quality processes and ensuring that the data is accurate, complete, consistent, and reliable across the data lifecycle. The RP also ensures that the data is compliant with GDP standards, and the Quality Management System.
Some of the duties of the RP include:
- Implementing GDP training in data quality policies and procedures.
- Establishing and monitoring data quality metrics and indicators.
- Performing data quality audits and assessments.
- Identifying and resolving data quality issues and anomalies by implementing CAPAs and Change Control procedures.
What could go wrong?
There are various ways in which the integrity of your data could be compromised or harmed.
Some examples of data integrity issues are:
- Intentional data falsification or manipulation, such as altering or deleting data to hide errors or discrepancies.
- Poor documentation practices that impact the reliability of the data, such as incomplete, inaccurate, or illegible records.
- Lack of control related to software, computerized systems or instruments, such as using unvalidated or unauthorized systems, or failing to protect data from unauthorized access or modification.
- Lack of a review process to ensure detectability of any data integrity gaps, such as not verifying the accuracy and completeness of data before using it for decision making or reporting.
- Human error, such as accidentally deleting a row of data in a spreadsheet.
- Inconsistencies across format, such as a set of data in Microsoft Excel that relies on cell referencing may not be accurate in a different format that doesn’t allow those cells to be referenced.
- Lack of record uniqueness, such as having multiple records for the same entity in different databases due to poor data integration or matching.
- Lack of relationship constraints, such as having orphan records or missing keys in relational databases.
- Lack of referential integrity, such as having inconsistent or invalid values across related tables in relational databases.
- Lack of physical integrity, such as losing data due to hardware failures or environmental factors.
What if there is a breach of Data?
Data integrity breaches are serious incidents that can have negative consequences for both the organisations and the individuals involved. Some of the possible consequences are:
- Financial loss: Data breaches can result in direct and indirect costs for the organisations, such as compensating affected customers, paying regulatory fines, investing in new security measures, and losing business opportunities.
- Reputation damage: Data breaches can erode the trust and confidence that customers, partners, and stakeholders have in the organisations. This can affect customer loyalty, and market share of the organisation.
- Legal liability: Data breaches can expose the organisations to legal risks and liabilities, such as lawsuits, investigations, or sanctions from regulators or authorities.
- Operational disruption: Data breaches can disrupt the normal functioning of the organisations’ systems, processes, or services. This can affect the productivity, efficiency, or quality of the organisations’ operations.
- Security vulnerability: Data breaches can expose weaknesses or gaps in the organisation’s security policies, procedures, or practices. This can make them more susceptible to future attacks or threats from hackers or malicious actors.
Conclusion
Poor data integrity is like the “pea under the mattress”: You cannot see it, but it’s there and will eventually bring discomfort!
The importance of Data Integrity in Good Distribution Practice and it’s relevance to the Quality Management System cannot be overstated. Ad-hoc improvisations to the data, “tweaks” made to the records to ensure it looks more agreeable to what you believe an inspector wants to see, or management insisting on using underpowered and old data systems to avoid expense, can all add to risk which can become not only serious, but an administrative nightmare to correct, not to mention the potential risk to patient safety.
Without Data Integrity, you do not have a working system. Your data management system must be validated and qualified as fit for purpose.
Getting help with GDP Compliance
It is recommended to have an approved and registered consultancy train your staff in all aspects of Good Distribution Practice.
Paradigm Shift Consulting Ltd have a comprehensive team of specialists with extensive experience within their fields who can support you in your compliance needs. If you’re getting started as a new business in any avenue of pharmaceutical medicines handling, or feel that you need to sharpen your procedures to prepare for an inspection, we can assist and advise.
Contact us using the form below to request more information.
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