Data integrity PIC S

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Data IntegrityThe degree to which data are complete, consistent, accurate, trustworthy, reliable andthat these characteristics of the data are maintained throughout the data life cycle.The data should be collected and maintained in a secure manner, so that they areattributable, legible, contemporaneously recorded, original (or a true copy) andaccurate. Assuring data integrity requires appropriate quality and risk managementsystems, including adherence to sound scientific principles and good documentationpractices. The data should comply with ALCOA+ principles.Data LifecycleAll phases in the life of the data (including raw data) from initial generation andrecording through processing (including transformation or migration), use, dataretention, archive / retrieval and destruction.Data QualityThe assurance that data produced is exactly what was intended to be produced and fitfor its intended purpose. This incorporates ALCOA + principles. 14Data OwnershipThe allocation of responsibilities for control of data to a specific process owner.Companies should implement systems to ensure that responsibilities for systems andtheir data are appropriately allocated and responsibilities undertaken.Dynamic RecordRecords, such as electronic records, that allow an interactive relationship between theuser and the record content. 13Exception ReportA validated search tool that identifies and documents predetermined ‘abnormal’ dataor actions, which require further attention or investigation by the data reviewer.Good Documentation Practices (GdocP)Those measures that collectively and individually ensure documentation, whetherpaper or electronic, meet data management and integrity principles, e.g. ALCOA+.Hybrid SystemsA system for the management and control of data that typically consists of an electronicsystem generating electronic data, supplemented by a defined manual system thattypically generate a paper-based record. The complete data set from a hybrid systemtherefore consists of both electronic and paper data together. Hybrid systems rely onthe effective management of both sub-systems for correct operation.Master DocumentAn original approved document from which controlled copies for distribution or use canbe made.14 ‘GXP’ Data Integrity Guidance and Definitions, MHRA, March 2018PI 041-1 62 of 63 1 July 2021
MetadataIn-file data that describes the attributes of other data, and provides context andmeaning.Typically, these are data that describe the structure, data elements, inter-relationshipsand other characteristics of data e.g. audit trails. Metadata also permit data to beattributable to an individual (or if automatically generated, to the original data source).Metadata form an integral part of the original record. Without the context provided bymetadata the data has no meaning.Quality UnitThe department within the regulated entity responsible for oversight of qualityincluding in particular the design, effective implementation, monitoring andmaintenance of the Pharmaceutical Quality System.Raw DataRaw data is defined as the original record (data) which can be described as the firstcaptureof information, whether recorded on paper or electronically. Information that isoriginally captured in a dynamic state should remain available in that state. 14Static RecordA record format, such as a paper or electronic record, that is fixed and allows little orno interaction between the user and the record content. 14Supply ChainThe sum total of arrangements between manufacturing sites, wholesale anddistribution sites that ensure that the quality of medicines in ensured throughoutproduction and distribution to the point of sale or use.System AdministratorA person who manages the operation of a computerised system or particular electroniccommunication service.14 REVISION HISTORYDate Version Number Reasons for revisionPI 041-1 63 of 63 1 July 2021
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PHARMACEUTICAL INSPECTION CONVENTIO
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9.10 Management of Hybrid Systems .
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3.5 The principles of data manageme
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o automation; oro the use of techno
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process consistency (e.g. biologica
Page 11 and 12: should clearly demonstrate that rep
Page 13 and 14: Staying continuously and actively i
Page 15 and 16: 6.7 Dealing with data integrity iss
Page 17 and 18: Data IntegrityAttributeRequirementt
Page 19 and 20: Specific elements that should be ch
Page 21 and 22: generation of working copies of doc
Page 23 and 24: 4. ExpectationDocuments should be s
Page 25 and 26: 8.5.2 Records should be appropriate
Page 27 and 28: Where appropriate, the reason for t
Page 29 and 30: 8.9 Direct print-outs from electron
Page 31 and 32: 8.11.2 A record/register should be
Page 33 and 34: 9.2.2 Validation alone does not nec
Page 35 and 36: osystems used in the decision proce
Page 37 and 38: Check that end-user testing include
Page 39 and 40: read by the new software. Where nec
Page 41 and 42: - User access controls should ensur
Page 43 and 44: potential security weaknesses) and
Page 45 and 46: 9.6 Audit trails for computerised s
Page 47 and 48: - in the case of changes or modific
Page 49 and 50: 9.8 Review of data within computeri
Page 51 and 52: to access electronically stored dat
Page 53 and 54: Appropriate quality risk management
Page 55 and 56: Area for reviewComparison of analyt
Page 57 and 58: Accurate [4.1], [6.17] [5.40], [5.4
Page 59 and 60: 12 REMEDIATION OF DATA INTEGRITY FA
Page 61: 12.2.1.2 Evidence of open communica
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Data integrity PIC S

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