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MIND-SET CHANGE In a data integrity-focused audit, the emphasis has moved away from providing information solely based upon a technical and scientific context, towards providing evidence that the final analytical results are not false. This holistic approach, based on the end-result, may require a different mind-set for certain organisations and requires a focused effort to prepare for this new approach. As regulators increase their focus on data integrity and reliability, auditors are examining with multiple regulations and standards in mind. These may include Pharmaceutical Quality/Current Good Manufacturing Processes2

(CGMP), Good

Laboratory Practices (GLP), Good Automated Manufacturing Practice (GAMP3

), Good Clinical

Practices (GCP) and the Application Integrity Policy (AIP) in addition to US FDA-recognised consensus standards. According to the FDA, source data needs

to be ‘attributable, legible, contemporaneous, original, and accurate’ (ALCOA) and must meet the regulatory requirements for recordkeeping. ALCOA+ refers to additional terms included by the European Medicines Agency on electronic data in clinical trials (Table 2). It is highly recommended to use this concept.

INFORMATICS DATA JOURNEY Data-intensive science is becoming far more mainstream in laboratories. It is best to take a pragmatic approach to what this means for laboratory operations. When samples are being analysed, several types of scientific data are being created in the laboratory. They can be categorised in three different classes (Figure 1).

RAW data Automating the capture of metadata is a very

effective way of maintaining data integrity. Self-documenting processes capture metadata automatically, without human interaction. They eliminate transcription errors and avoid unnecessary retyping of data. In a recent survey, 32 per cent of respondents stated that data integration in a paperless laboratory will eliminate manual entries and data transfer. Automating this process will also eliminate hybrid systems – a combination of a paper and electronic system. Hybrid systems significantly increase integrity challenges, since both systems need to be synchronised in a consistent way. Hybrid systems are no longer recommended.

SINGLE POINT OF TRUTH FOR (META) DATA To avoid challenges to the integrity of data, it is crucial to have one copy (the master copy) of

DATA SOURCE (Instrument/Sensor) Meta data

Secondary data

the data or information. Master data is a single source of data used across multiple systems, applications, and processes. To achieve a single point of ‘truth’, it is necessary to understand the key differences between spreadsheets and databases. The perception that a spreadsheet can act as a database is fundamentally wrong. The primary function of a spreadsheet is to manipulate, calculate, and visualise data, while a database’s primary function is to store and retrieve data in a structured manner. A spreadsheet has serious drawbacks

when used for data storage: it cannot enforce relationships; there are no multi- user capabillities; and it offers neither data validation nor protection against data corruption. There is a risk of misuse of spreadsheets in the laboratory. The worst nightmares are caused by the sort function and

Table 2: Terms associated with ALCOA+ A Attributable

B Legible

O Original A Accurate + Complete + Consistent + Enduring + Available

Who performed an action and when? If a record is changed, who did it and why? Link to the source data

Data must be recorded permanently in a durable medium and be readable

C Contemporaneous The data should be recorded at the time the work is performed and date & time stamps should follow in order

Is the information the original record or a certified true copy? No errors or editing performed without documented amendments All data including repeat or reanalysis performed on the sample Consistent application of data time stamps in the expected sequence Recorded on controlled worksheets, laboratory notebooks or electronic media Available / accessible for review / audit for the life time of the record

File stock CRC Check Archive

Embedded device info Data characteristics Security

Capture process Units

Date/time Batch #

Experiment reference Project Location Origin Owner

Retention Search

Conformity Processed Version # Reporting trail Audit trail Archive

Release parameters Workflow history Calculations Access control

RESULT – Consolidation Figure 1: Different classes of data are created in the laboratory 6 |

When a smartphone captures a photo or film, it automatically includes systematic metadata with the object. Examples include capture and storage of GPS location, weather conditions, personal condition such as heartbeat – without adding an additional manual entry to the data object. Similar developments are expected to be introduced in the scientific community. Modern balances may automatically include temperature and humidity when transmitting the weight.


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