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Transcript of Quintiles_Risk-based Approach to Monitoring.pdf
7/28/2019 Quintiles_Risk-based Approach to Monitoring.pdf
http://slidepdf.com/reader/full/quintilesrisk-based-approach-to-monitoringpdf 1/13
Copyright © 2013 Quintiles
Lessons learned from risk-based
monitoring deployments
Using on-demand data tooptimize trial execution
Dan WhiteVP, Global Operations
Amanda Sax
Sr. Director, IPT
Quintiles
Copyright © 2013 Quintiles
7/28/2019 Quintiles_Risk-based Approach to Monitoring.pdf
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2
The New Health demands change
• Trial complexity• Regulatory scrutiny
• Development cost
• Competition for subjects
• Post approval
commitments
• Reimbursement• Pipeline of compounds
• Physician pool
• R&D spend
• ROI
• Probability of success
I N C R E A S I N G
D E C R E A S I N G
Data-driven Trial Execution
7/28/2019 Quintiles_Risk-based Approach to Monitoring.pdf
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3Data-driven Trial Execution
Who’s Ready for RBM?
From recent market research:
• Risk-based monitoring (RBM) has a high level of awareness among key decision
makers in the biopharma industry (79%), up from 65% one year ago.
> Half have already implemented some aspect of RBM
• The major benefit is the promise of reduced costs (78%).
• Triggered monitoring (monitoring that responds to
operational/data signals) is the most
commonly-adopted aspect of RBM
• About 60% of non-users plan to implement RBM in a clinical trial
in the next 2 years. Within the next three years,
81% of non-users expect to be using RBM.
Moving through Early Adoption Phase
7/28/2019 Quintiles_Risk-based Approach to Monitoring.pdf
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4Data-driven Trial Execution
So why isn’t everyone using RBM?
While both users and non-users agree that RBM contributes to quality control
and data accuracy (73%), there is also consensus that RBM involves at least
some sort of trade-off between risk & quality (76%).
Potential Barriers
For users, the major barriers to RBM
implementation are concerns over • investigator compliance if not visited
every 6 weeks (49%), and
• the lack of face time between on-site CRA
and investigator (44%).
For non-users, the biggest barrier is theorganization’s corp orate cultu re (52%) .
• concern over being an early adopter /
guinea-pig (40%).
7/28/2019 Quintiles_Risk-based Approach to Monitoring.pdf
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5Data-driven Trial Execution
Experiences Speak Loudly
This presentation provides insights into experiences in developing, refining,
and implementing risk-based monitoring, and the role of such monitoring in
data-driven trial execution.
From 10+ years of RBM deployments
These represent lessons learned from
more than a decade's employment of risk-based monitoring strategies
including:
• the selection of a core monitoring triggers,
• the articulation and continuing re-articulation
of the thresholds employed,
• the role of technology and automation, and• achieving the optimal balance of
standardization and customization on a trial-
by-trial basis
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6Data-driven Trial Execution
rSDV in Late Phase Outcomes
• The monitoring option chosen involved conducting SDV of a subset of selected subject visits only, chosen at random prior to onsite visit.
• Some potential quality issues and trends went undetected.
• SDV strategy was revised and centralized trend analysis introduced utilizing
aggregated data.
Lessons Learned:
• Random sample SDV by visit alone doesn’t allow for onsite monitors to
effectively detect quality trends at a site level.
• Sites' compliance monitored through aggregated data, has now become a
core component of RBM.
Use Case #1: Trend Detection
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Study Quality
Benefits
• Identify sites with quality risks and implement mitigation strategies
• Early signaling of site compliance to the protocol, GCP compliance
• Early signaling of study risks in trends; potential protocol adjustments can be identified
• Early signaling of under-reporting
Solution
Protocol Deviations
Data-driven Trial Execution
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Deploying Triggers
• Assumption of high number of triggers ensures you won’t miss quality issues.
• Triggers lead to excessive noise and loss of operational efficiency
• Lessons learned were threefold:
> Focus on a few core triggers to drive quality and efficiency
> Conduct upfront risk assessment to identify focused custom triggers
> Find ways to harness technology, i.e., automating and aggregating data in a modelwas likely to be the optimal approach
• Outcome:
> obtains the correct balance between the standardization, or industrialization, of the
process and the customization needed for each study on a trial-by-trial basis.
> allows for laser focused modifications to be made as needed in a particular study or
development program.
Use Case #2: Development program across a single compound
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Getting Smarter
Next generation of study requirements:
• a 'smarter' approach
> Automatic triggering of onsite visits
> Carefully chosen, critical set of thresholds
> Deployment of resources solely dependent on trigger tool
• SDV backlog grew at excessive rates!
Lessons learned
> Technology can be utilized effectively if set up appropriately
> Automated triggers still require some manual oversight
> Reassessment of trigger thresholds is required with preference for assumption to bebased on a statistical foundation
Use Case #3: Large morbidity and mortality outcomes study
Data-driven Trial Execution
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Data-driven Trial Execution
Data-driven Trial Execution
P O W E R E D B Y
Q u i n t i l e s I n f o s a r i o
Data surveillance
allows us to optimize
and adapt
monitoring
throughout the trial,
re-assessing risk
and applying the
right action at the
right time.
We use the right type of monitoring at the
right time (on-site, remote, centralized),
monitoring sites, data, patients and events
that require more attention and focus.
Data-driven Trial
Execution begins
with an in-depth risk
assessment, where
our team of experts
evaluates thescientific and
operational risk of
each protocol.
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New Monitoring ModelFocus resources based on risk
Informed Consent Process & IP
On-Site Relationship
Source Document
On-site Monitoring
Medical and Data Monitoring
Virtual Relationship
Site Progression Management
Centralized Monitoring
Protocol
High Medium Low
Responsive Action
Based on Level of Site Risk
Site Risk
Balanced relationship between on-site and centralized monitoring improves
delivery & quality, leading to better trial performance
Data-driven Trial Execution
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The Benefits
Data-driven Trial Execution
Efficiency &
Productivity
A data-driven,
near real-timeview into data
Transparency
Knowledge-
Driven Trials
Based on the
experience andscientific know-
how of Quintiles
Better, Faster
Decisions
Immediate
access andinterpretation of
trial, patient and
other relevant
data
Maintains
quality, patientsafety and
regulatory
compliance
Quality
Reducing the
resources, time
or cost required
Efficiency &
Productivity
Data-driven Trial Execution enables better, faster decisions,
increased transparency, improved patient safety and quality, and more efficient trial management.
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Questions & Wrap-up
• Real Case Studies – what are your experiences?
• What elements of RBM have you implemented?
• What approach is optimal for what types of studies?
Thank you for your interest today!
Data driven Trial Execution