Data Integrity Practical Applications
Transcript of Data Integrity Practical Applications
Slide 1 © PharmOut 2016
Data Integrity –Practical Applications
Marc Fini
12th July 2016
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Data Integrity – Why Bother?
• What’s all the fuss about?
• What could really go wrong?
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Data Integrity and People
• There are plenty of ways to think about Data Integrity and to implement strategies to improve data integrity in your organisation.
• Both paper based & electronic data should be considered.
• Everything ultimately uses a human.
• Automation amplifies the contribution of people:
• Fewer people = more impact per person
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Man vs. Machine
Ma
n - Pattern recognition
- Simultaneous bottom up and top down reasoning
- “Look and feel” comparisons (both visual and tactile)
- Contextual reasoning (extraneous clues)
Ma
ch
ine - We have data
transcription error rates around 2 – 10%
- Verification by a single person catches about 40% of errors
- Experienced people think they are better – data shows they are not
Smart design uses people and machines where each provides the best benefit
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Data Integrity vs Data Quality
Data Integrity
Maintaining and assuring the accuracy
and consistency of data over its entire life-cycle
ALCOA+
MOM
PPP
Data Quality
Data quality ensures clear understanding of the meaning, context, and intent of the data
Accessibility, Accuracy, Consistency,
Comprehensiveness, Currency, Granularity, Definition, Relevancy,
and Timeliness
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Recent Enforcement Actions
Data Integrity problems were cited in at least 13, or 30% of the US FDA Warning Letters in the 2015 financial year.
• In the 2014 financial year, there were 8 or 17% of Warning Letters
In 2010 two Warning Letters had a major focus on Data Integrity, in 2014 there were 10.
In 2015, Data Integrity failures were observed in almost all Warning Letters to API manufacturers.
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Issues Regulators Focus On
The past several years have brought increased concern and level of regulatory attention to issues surrounding:
Access controls to electronic systems
Audit trail reviews
Back up of data
Supplier quality
management
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Top deficiencies relating to data integrity found by the US FDA in 2015
Failure to include
complete data
Audit Trail
Control of Data
Password Sharing
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MHRA Data Integrity Definitions and Guidance for Industry (March 2015)
Data governance should address
Data ownership throughout the
lifecycle
Staff training in the importance of data integrity principles
Design, operation and monitoring of processes /
systems
Creation of a working environment that
encourages and open reporting culture
Control over intentional and unintentional changes to information
Systems and procedures to
minimise the potential risk to data integrity, and identifying the
residual risk.
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Motive, Opportunity and Means (MOM)
• MOM – how do we control these three key factors?
• Motive is a factor controlled by leadership and culture.
• Opportunity and means can be controlled to a certain extent.
• People still need access to do their work.
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Means
Document Control
• Issuance of paper forms
• Library access processes
Physical Controls
• Identification of critical records
• Review of critical records in source (especially dynamic records)
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Means – Access Management
Logical Controls
• Enhanced access accounts
• Conflicts of interest
• Inadequate system design (e.g. data files)
Physical Controls
• Access to business documents and unsecured system
• Key for locked samples and paperwork
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Opportunity
Access
• Electronic systems, especially enhanced access
• Personal computer
• Workstation in sparsely used area of facility
• Access to paper documents (test results, batch records, deviations)
Time
• If improper activity is accepted / promoted, people have all day to create falsified data
• Overtime or non-standard time (if a lone actor)
• Isolated work area
• Personnel rotation through job positions
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PPP – People, Paper, Programs
People is strong leadership, management and close supervision. This fits in to the Plan, Do, Check, Act (PDCA) or similar management frameworks.
• Activities like batch document review, data or testing review and checking and by association, training are becoming increasingly significant.
Paper is great technical writing – things such as document design, information mapping, allowing adequate space – all play a part.
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PPP – People, Paper, Programs
• Programs relates to the software, in the design of your broader and then more specific systems:
• Passwords
• All users have a unique password
• Passwords expire within a set amount of time or uses
• No shared passwords
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PPP – People, Paper, Programs
• Signatures
• Signatures and initials are kept in a register
• All users understand what their electronic signature represents.
• Software
• Remove and ban all snipping tools
• Disable any ability to edit pdf files (e.g. Adobe Professional)
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PPP – People, Paper, Programs
• Audit Trails
• Ensure that review of audit trail is a documented, regular process within your organisation.
• Back Up and Restore
• Have a documented back up and restore process within your organisation.
• Have a disaster recovery plan in place for data.
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Designing DI into your systems
AttributableLegible/
PermanentContempora
neousOriginal Accurate
Initials & Signature registers
Active directory, e-sig, audit trails, metadata
Control of blank forms, pen policy/white out
Documents available in right place at right time, +/-time limits
Verified ‘true copy’, scans
Reflective of the observation data checking
Data annotation tools, audit
trail
System clock, sync., transaction window
Metadata, data about the data that permits reconstruction
Data capture, manual data entry
Paper-based
Electronic
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Thank you for your time.Questions?
Marc Fini
Lead Consultant
www.pharmout.net