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SCDM 2017ANNUAL CONFERENCE
September 24-27 I Orlando
SDV – What Is It Good For? Absolutely Nothing!
MaryAnne Rizk, PhDVP – Global CRO BioPharma PartnershipsOracle Health ScienceFollow: @RizkManagement #SCDM2017
01.SDV – What Is It Good For? Absolutely Nothing!
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TABLE OF CONTENTS
Saying Goodbye to SDV
Methods: Risk Management Planning
Technology: Strategies to Improve Data Quality
Clinical Outsourcing Impactsv
Key Risk Indicators (KRIs) and risk analysis
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Say Goodbye to SDVSource Data Verification – Expensive Comparison
1http://www.kalagathos.com/michaelherschelsheresies/michaelherschelsourcedataverification/
30% Clinical Trial Costs spent on 100% SDV
$7.5Billion
Annual Spent of Clinical Trials
100% = 0 value
$2B spent on 100% SDV has little impact on clinical trials
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Industry Risk Maturity
1http://www.kalagathos.com/michaelherschelsheresies/michaelherschelsourcedataverification/
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Considerations Risk Based Monitoring
1http://www.kalagathos.com/michaelherschelsheresies/michaelherschelsourcedataverification/
• Does your present Electronic Data Capture system have ability to track data
to be verified?
• How are you determining study-level risk?
• How are your risk tools integrated, and who performs maintenance on these
tools?
• Who mediates the decisions for risk mitigation?
• Technology must go hand-in-hand with Business Processes evolution and
Change Management
• Collaboration among all areas is key to success
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Risk Management –
An Intergreated Data Approach
1http://www.kalagathos.com/michaelherschelsheresies/michaelherschelsourcedataverification/
• All sites shouldn’t be treated the same, leveraging data
transparency, do you have a method of blinding site-based
quality performance?
• Use intelligent monitoring, leveraging predicative analytics to guided targeted monitoring and put resources on data quality efforts where errors are likely to occur
• Leverage a single source to centralized Data Management within a Cloud Workbench Solution; allowing better data control for sponsors, provide transparency across clinical collaborators and eliminate data transportation
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Sponsor Approach on RBM Technology
& Clinical Outsourcing CRO Impact
1http://www.kalagathos.com/michaelherschelsheresies/michaelherschelsourcedataverification/
“Clinical teams will be able to access study data through Oracle's single platform cloud service, eliminating the need to send data back and forth to CROs, saving us time and reducing the cost of our clinical studies,” -Rob Goodwin, VP Pfizer
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Key Drivers for RBM through technology
Only 2.4% of all queries generated concerned critical data
Quality focus
• Site and data
• Increasing safety
Advance technology to streamline drug development
• Getting more drugs to market to help patients
R&D pipeline
• Reducing risk for financial investment
• 10 to 40% cost savings needed over the next 3 to 5 years
• Travel cost
• Resource cost
• Site time
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Replace SDV with More Efficient Technology Solutions
• EDC: The eCRF can fill the need of the source document.
While regulatory bodies require that SDV be performed, there is no specification
regarding the amount of data required. The level of SDV is decided by each sponsor
based upon the level of risk to the accuracy of the data they can manage without
impacting safety and timelines, as well as what downstream risk mitigation processes
are in place.”
• CTMS: Sponsors identify fields or CRF modules that have a
high rate of SDV correction.
This may show where additional site training or a redesign of the CRF or source
documents may be needed. It will also focus on where continued high rates of SDV
should be applied. When fields or CRF modules that have lower SDV correction
rates are identified, the sponsor can reduce the level of SDV performed without
incurring an increased risk of errors. This can be applied to current or future
programs, maximizing the reduced SDV benefit.” 2
2 https://www.emc.com/collateral/emc-perspective/h5621-process-optimiz-ep.pdf
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Agency Guidelines are Changing
“One hundred percent SDV, the comparison of each data point on every case report form (CRF) to
subject medical records, may not be appropriate for most large, multi-center trials. Targeted SDV—
the verification of critical trial data, including study endpoints—has the potential to improve safety
oversight, data quality, regulatory compliance, protocol adherence, and overall trial validity while
reducing costs and the time to database lock for large, multi-center trials.”
“The Guidance on Good Clinical Practices (GCP), developed by the International Conference on
Harmonization (ICH), requires that trial monitors have access to and can review source documents.
This guidance, ICH E6, has been adopted by both the Food and Drug Administration (FDA) in the US
Code of Federal Regulations (CFR) under Title 21 and by the European Union (EU) as part of the EU
directive on clinical trials. Guidance ICH E6 and the regulatory authorities that have adopted it, refer
to source documents (i.e., primary health records, in the sections on investigators, sponsors, trial
protocols, and essential documents).”
“FDA guidelines explicitly refer to a representative number of subject records, not all subject records.
The Department of Health and the Medical Research Council in the United Kingdom announced,
"verifiable...does not imply that every item of data recorded must be supported by a source
document or checked."7 The number of subjects, the experience of the clinical site, the clinical
endpoints, and the nature of ancillary data are several of the factors that should be considered when
developing a strategy and protocol for a project-specific SDV plan.” 3
3 http://www.appliedclinicaltrialsonline.com/targeting-source-document-verification
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Considerations for Reducing SDV using a Risk-Based Monitoring (RBM) Strategy
• Does your present Electronic Data Capture (EDC)
system have ability to track data to be verified?
• How are you determining study-level risk?
• How are your risk tools integrated, and who performs
maintenance on these tools?
• Who mediates the decisions for risk mitigation?
• Technology must go hand-in-hand with Business
Processes evolution and Change Management
• Collaboration among all areas is key to success:
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RBM Industry Investments
CROs and sponsors investing millions to implement RBM
• Standardizing data and processes
• Creating Key Risk Indicators (KRIs)
• Operational focused (TransCelerate)
• Infancy in what is working and what is not.
• Introducing new business process and change management
• Business Intelligence (BI) tools
• Integrating systems and data – platform approach
New focus
• Focus on data quality algorithms, not just operational data
• Business process management tools
Future focus – Getting smarter with the data
• Artificial intelligence
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Multi-System Solution Supports All 5 Stages of RBM
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RBM Support – CTMS, Analytics (CDA), & EDC
RACT - CTMS
• Support Risk Assessment and Categorization Tool (RACT) features from TransCelerate within Clinical Trial Management System (CTMS)
• Use of ‘Assessments’ feature in Siebel
• Assessment template applied at various levels
Key Risk Indicators - CDA
• TransCelerate Key Risk Indicators added to Clinical Development Analytics (CDA) data model
• Out of the box dashboards at the Study and Study-Site level
Partial SDV Support – CTMS/EDC• Identify critical visits and pages
• Set partial SDV strategy at various levels
• Define criteria for SDV based on subject statuses/events
• Import SDV rules from InForm
Training Planning & Tracking for Sites - CTMS• Define Training Topics & Plans
• Track Training at Various Levels
Issue Management – CTMS• Record RBM mitigations and actions
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RBM Summary
CROs and sponsors investing millions to implement RBM
• Standardizing data and processes
• Creating Key Risk Indicators (KRIs)
• Operational focused (TransCelerate)
• Infancy in what is working and what is not.
• Introducing new business process and change management
• Business Intelligence (BI) tools
• Integrating systems and data – platform approach
New focus
• Focus on data quality algorithms, not just operational data
• Business process management tools
Future focus – Getting smarter with the data
• Artificial intelligence