Quintiles_Risk-based Approach to Monitoring.pdf

7/28/2019 Quintiles_Risk-based Approach to Monitoring.pdf

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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



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



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




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%).




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




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



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



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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



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






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.



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




The Benefits


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.



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

Documents

Quintiles_Risk-based Approach to Monitoring.pdf