Building Business Processes that

Optimize Accurate and Reliable Data

2nd FDA/PQRI Conference on

Advancing Product Quality

5-7 October 2015

N. Bethesda, MD, US

Presentation by:

Monica J. Cahilly, M.S.

Green Mountain Quality Assurance, LLC

mcahilly@GMQA.net

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Copyright of slide materials marked with the symbol (c) 2007-2015 GMQA LLC

is held by Green Mountain Quality Assurance LLC. Use of copyright materials

is restricted and these materials shall not be reproduced or distributed to anyone

not attending the training workshop without contractual licensing of the

materials or other express written permission of Green Mountain Quality

Assurance LLC.

(c) 2007-2015 GMQA LLC

OUTLINE

• What is “Data Integrity”?

• Good Documentation Practices

• Data Life Cycle

• Quality Management Systems

• Management Governance

• Data Integrity in a Global Context

• Q&A

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External

Environment:

Economic, Societal,

Political, Legal, etc.

Internal Environment:

Quality Management

System ,

Management Culture

& Governance

Data Life Cycle

Good

Documentation

Practices

Data Integrity—A Growing

Concern

• Data Integrity (IEEE)* = “The degree to which a collection of data is complete, consistent, and accurate.”

* FDA Glossary of Computer Systems Software Development

Terminology (8/95)

FDA.gov 5

Data Integrity—A Growing

Concern

• Data Integrity (MHRA)* = The extent to

which all data are complete, consistent and

accurate throughout the data lifecycle.

– Data integrity arrangements must ensure that

the accuracy, completeness, content and

meaning of data is retained throughout the

data lifecycle.

*MHRA Data Integrity Definitions and Guidance March 2015

GOV.UK 6

What assures Data Integrity?

• Data Integrity (IEEE)* = “The degree to

which a collection of data is complete,

consistent, and accurate.”

– Good Documentation Practices are followed

– Content of Data is trustworthy and reliable,

i.e., the data is ‘valid’

• Achieved through: process validation and control,

data review practices, etc.

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Creation & Processing of Data

• Good Documentation Practices

– A = Attributable

– L = Legible

– C = Contemporaneous

– O = Original

• or Certified Copy of Original

– A = Accurate

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Good Documentation Practices

• Legible• Contemporaneous• Permanent

– Ink, preferably black– No pencil or white out

• Attributable• Traceable• Date Stamped• Changes with single line cross-out to retain original

entry, reason, initials, date• Accurate

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21 CFR Part 11 = Good Documentation Practices for Electronic Data

• Legible• Contemporaneous• Permanent

– Ink, preferably black– No pencil or white out

• Attributable• Traceable• Time/Date Stamped• Changes with single line cross-out to retain original

entry, reason, initials, time/date• Accurate

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Audit Trails

Time-date stamps

Metadata, Corroborating Paper Records, etc.

Log-on (e.g., User ID / password), E-Sigs

Annotation tools

Time-date stamps, late entry notation

Enforce saving of processing

Validation

No over-writing, Enforce saving of steps

The Paradigm Shift• How do we think about “data” and how do we

design our business processes?

• How do we validate systems that generate source

data with direct impact on patient safety, product

quality, application integrity…?

• How do we manage risks across the entire DLC?

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External

Environment:

Economic, Societal,

Political, Legal, etc.

Internal Environment:

Quality Management

System ,

Management Culture

& Governance

Data Life Cycle

Good

Documentation

Practices

Data Life CycleIn the Broader Context of QbD / Pharma Product Life Cycle

• Do I have all my data?

– Design of data collection: protocol, process, method

– Data Life Cycle controls for data + metadata

• Has my data been objectively processed?

– Controls to Prevent & Detect Testing Toward Outcome

• Am I reviewing all my data?

– Printouts versus Source Electronic Records

– Review of Audit Trails

• Am I reporting all my data?

– Controls to Prevent & Detect Selective Reporting

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Data Life Cycle

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•Source eData

•Re-processing events

•Failures

•Objective Reporting

•Transparency in Failures

•Tracking & Trending Failures

•Objective Processing

•Handling Failures

•Design of Data Collection

•Transfers of Data + Metadata

Data Collection

Data Processing

Data Review

Data Reporting

What is the relevance of

Data Integrity?

Impact on:

• Patient Safety

• Product Quality

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Risk Management

Methodology

• Risk Management Programs—ICH Q9

– Risk Assessment

– Risk Mitigation and Control

– Risk Communication

– Risk Review

Risk Management

Risk Assessment

Risk Mitigation

and Control

Risk Communi-

cation

Risk Review

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Create Data

Process Data

Review Data

Report Data

Create Data

Transfer Data

Store Data

Retrieve Data

Data Life Cycle =

Business Process + Data Flow

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Create Data

Process Data

Review Data

Report Data

Create Data

Transfer Data

Store Data

Retrieve Data

Data Life Cycle =

Business Process + Data Flow

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Data Life Cycle Risk Analysis

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Instrument Data

Excel LIMS

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Create Data

Process Data

Review Data

Report Data

Create Data

Transfer Data

Store Data

Retrieve Data

Data Life Cycle =

Business Process + Data Flow

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We Consider Printouts to be the

“Raw Data” / the “Official Record”

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Risk-Based Approach to

SLC and DLC

• “Critical” Thinking Skills for Data Reviewer

– What are the reviewer’s “blind spots”?

– Only looking at what you EXPECT to see?

– Or asking, WHAT COULD HAVE GONE WRONG?

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Risk-Based Approach to

SLC and DLC

• “Critical” Thinking Skills for Data Reviewer

– What about ERROR PATTERNS?

• Frequency

• Pattern

• Determinate or Indeterminate

• Failure Effect

• Impact

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Create Data

Process Data

Review Data

Report Data

Create Data

Transfer Data

Store Data

Retrieve Data

Data Life Cycle =

Business Process + Data Flow

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Preserving Relationships between

Data + Data Attributes (‘Metadata’)

throughout Data Life Cycle

6.5

What?

When?

Why?

Who?

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Risk Management of

Data Transfer and Storage

Instrument Data

File ShareStorage System

M A

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Framework for Optimizing

Data Life Cycle• Subjective vs Objective Control

• Complex vs Simple

• Manual vs Automated

• Open-ended vs Well-defined

• Inconsistent vs Consistent

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What is Goal of

Data Process Mapping?

• Process Understanding.

• Knowledge Management.

• Quality Risk Management.

• Business Process Improvements:

– Efficiencies, Effectiveness, Cost Reductions

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Deming: “Quality costs less not more.”

Population of GxP Data:

Meaningful Decision-Making

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External

Environment:

Economic, Societal,

Political, Legal, etc.

Internal Environment:

Quality Management

System ,

Management Culture

& Governance

Data Life Cycle

Good

Documentation

Practices

Quality Management Systems

Key to Data Integrity

• Training Program

• Risk Assessment & Management

• Validation (Computer, Method, Process)

• Data Life Cycle

• Investigations Program

• Data Review Program

– Critical Thinking Skills

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Quality Management Systems

Key to Data Integrity

• Quality Audits & Inspections

• Vendor/Contractor Management

– Agreements

– Monitoring

• Management Culture & Controls

– Transparency & Accountability

– Tracking and Trending

– Risk Profiling

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Creating a Management Culture to

Assure Data Integrity• Transparency

• Accountability

• Lead by Example

• Staying Continuously & Actively Involved

• Process Ownership / Personal Responsibility

• Set Realistic Expectations

• Fair and Just Consequences & Rewards

• Collaboration and Team Camaraderie

• Staying “Current” with the “C” in CGxPs

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Global Community for Healthcare:

Medicines for All

• Safe

• Effective

• Quality

• Affordable

• Accessible

• Ethically Produced

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Summary:

Data Integrity Compliance Goals• Accuracy, reliable design, consistent intended

performance of record systems, both paper document systems and computerized systems

• Data Controls (both paper and electronic) to ensure authenticity, integrity, confidentiality, readily retrievable, accuracy, consistency, completeness throughout Data Life Cycle

• Signature Controls (both hand-written and electronic) to ensure legally-binding

• Quality Systems and Management Governance in place to assure data integrity

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Questions / Comments

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Monica J. Cahilly, M.S.

Green Mountain Quality Assurance, LLC

mcahilly@GMQA.net

www.greenmountainQA.com