Automation – Way Forward to Achieve Data Integrity By Implementing Scientific Data Management System (SDMS), Electronic Lab Notebook (ELN) & Document Management System (DMS)

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A Global Laboratory Informatics Company
Making Labs Proficient
Data Integrity Conference - Sept 1, 2016 Mumbai
Presented by : Mukunth Venkatesan (CEO, Agaram Technologies)
Automation – Way Forward to Achieve Data Integrity

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Why is Data Integrity Important?
“Data Integrity is considered as the first and foremost
requirement in a pharmaceutical quality system to ensure
that the medicines are of the required quality”

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What is Data Integrity
Data Integrity is defined as the
“the completeness, consistency, and accuracy of data. Complete,
consistent, and accurate data should be Attributable, Legible,
Contemporaneously recorded, Original or a true copy, and
Accurate.”
This is also known as the ALCOA principles guiding Data Integrity.

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Contemporaneous
Recording Date & Time
Accurate
Data with no errors or
editing
Original
Justifying data is true copy
Legible
Permanent Recording
Consistent
Consistent application
of date & time stamps
in the expected
sequence
Enduring
Recorded in enduring
media
ORIGINAL
75+ FTEs supporting
customers globally
Available
Available , accessible for
review/audit for lifetime
of record
Complete
All data including repeat or
re-analysis
Consistent
Consistent application
of date & time stamps
in the expected
sequence
Attributable
Source of Data & Who
ALCOA De-mystified

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ALCOA – How to achieve?
Attributable Source of data identified
with a process and person
Legible Permanently recorded.
Human Readable.
Contemporaneous Data Identified with a date
and time.
Original or
True Copy
Data certified as correct by
an authorized person
Accurate Data should not have any
errors
Data stored electronically should achieve this
Built in Time Stamp at the time of creation or
modification of data. Also systems for recording
online
Original data and metadata with electronic
signature should satisfy this need
Data captured directly without human
intervention should ensure this
Identifies the data to a source along with
metadata, person & process

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Data
Generation &
Recording
Processing
Method
Accuracy
Result
Data
Application Data System
Authenticated, Secure & Protected
Central
Storage
Automation
Automation Architecture for Data Integrity Assurance

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• An environment for controlled access to any application
• Automatic pushing of data and meta data generated or
modified to server- without user intervention
• Users can still continue to modify or update data
• Modified data is pushed to server automatically
• Full audit trail of all activities to be available at server and
client side
• Review & approval based on server data (true copy)
• Result or decision should be taken from the server data –
Single source of truth
Data Integrity- Functional Requirements

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Risk Mitigation- Data Generation & Recording
• How and where is original data created?
• Created in the local hdd. With a true copy in the server (controlled area)
• How do you ensure that the data is complete, accurate and traceable to meet
ALCOA?
• Automation ensures that (raw, meta, human readable) are all moved to server
• Is it possible to recreate, amend or delete original data and metadata?
• Automation should help in identifying amendments and NO possibility to delete
or obscure data
• How data is transferred to other locations or systems for processing or storage
• Automation can help in transfer of data in a controlled manner for processing or
storage. Any change due to processing needs to be handled by automation
solution.

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Risk Mitigation- Data Accessing & Processing
• How is data processed?
• Method used for processing to be identified as metadata for capture
• Where no external metadata is available for processing, data should contain
relevant metadata or manually record conditions under which data was created
or modified.
• How is data processing recorded?
• Any change in data due to processing should always be captured by the
automation system.
• Does the person processing the data have the ability to influence what
data is reported, or how it is presented?
• Make automation server as the primary source of data with all versions. So that
there could be no point of influence
• Even if a person does trials, all such trials should be captured independently
along with version control

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Risk Mitigation- Completeness, Accuracy of reported data
• Is original data (including the original data format) available for checking?
• Always original data is available in original format at server
• Accuracy or reported data can be cross checked based on original data for data
integrity at any point and time
• Does the data reviewer have visibility and access to all data generated &
processed?
• Reviewer can check all data generated in one go and from a single point of source
(server) without having to look at individual systems.
• Cross-checking can be done by download and re-creation of output using the
original application

11. Data Integrity – Solution

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AUTOMATION – Way Forward to Achieve Data Integrity
By Implementing
Scientific Data Management System (SDMS), Electronic Lab
Notebook (ELN) & Document Management System (DMS)

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•Solution for instrument generated data
b •Control over access & audit trail
c •Control over generated & processed data
d •Automatic data version control
e •Electronic Review & Approval
Scientific Data Management System (SDMS)

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a •Template Based approach
b •Deploy in QC & Production
c •Integrate via RS232 or TCP/IP
d •Contemporaneous with Data Versioning
e •Audit trail & Electronic Review
Electronic Lab Notebook (ELN)

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Electronic Document Management & Issuance control
a
• Manage organisation wide
• Document preparation
• Review, Approval & Release
c • Document Request & Issuance
d • Auto filling of Tags (batch#)
e • PDF Document with Electronic signature
f • Print Control

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Agaram Technologies
76 Nelson Road, Aminjikarai
Chennai 600 029 , India
info@agaramtech.com
www.agaramtech.com
1-800-NOW-LIMS (669-5467)
+91-44-4208-2005
Making Labs Proficient
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