See presentation slides from SCOPE Summit 2020. Artificial Intelligence (AI) has made its way into the realm of clinical trials and is reshaping how studies are conducted. This presentation looks at the practical ways AI and process automation are being used effectively today to optimize trial design and execution. See this presentation for a look into how technology is revolutionizing the clinical operations landscape – from the smallest biotech to big pharma.

1. Automation and AI In Clinical
Trials
From Big Sky to Practical Application
SCOPE 2020
Jim Reilly – VP, R&D Strategy

2. 2Copyright © Veeva Systems 2020
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Artificial Intelligence
Predictive Analytics
Natural Language Processing
Machine Learning
Big Data
Robotic Process Automation
Intelligent Automation
Adaptive Algorithms

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In today’s world, we have grown accustomed to…
Choice Suggestions
Medical CRM
SMART technologies
Why should clinical trial management be ANY DIFFERENT?

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AI is Certainly Newsworthy

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“There is a disconnect between the conferences I attend, the journals
and blogs I read, and the reality of medical practice on the frontlines of
healthcare delivery…
“So while there is a booming innovation industry – a new startup being
created every day, a new app being launched every minute – the actual
experience of delivering or receiving care is changing scarcely.”
.
Sachin Jain, former CMIO at Merck; CEO CareMore
Health
The Health Care Innovation Bubble
Healthcare, Volume 5, Issue 4, December 2017
Sciencedirect.com

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The Challenge with AI in Clinical Trials
Overhype. Under delivery.
• Big Data Analytics
• Predictive
Analytics
• Simulations
• Automatic Decision
Making
Shiny Object Layer
• Natural Language
Processing
• Machine Learning
• Automation
• Metric-driven
Suggestions
Reality Layer

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AI, But First…Data
Podcast: The Science and Business of Innovative Medicines -January 13, 2019
With Vas Narasimhan, Jorge Conde, Vijay Pand, and Sonal Chokshi
“The first thing we’ve learned is the importance of having outstanding
data to actually base your ML on. In our own shop, we’ve been working on
a few big projects, and we’ve had to spend most of the time just cleaning
the data sets before you can even run the algorithm. That’s taken us years
just to clean the datasets. I think people underestimate how little clean
data there is out there, and how hard it is to clean and link the data.”
Vas Narasimhan
CEO, Novartis

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First, Get Your Data House in Order
But how?
Better
Transactional
Systems
Simplify IT:
Fewer
Integrations &
Standalone Apps
Improve Data
Quality

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And Then: AI and Machine Learning in Clinical
Operations
An eye toward the practical, using your own clean data
Study Start-up
Document
Management
Monitoring

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AI and Machine Learning in Clinical Operations
An eye toward the practical, using your own clean data
Study Start-up

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The State of Study Start-up
6
Weeks from
Survey Creation
to Site Selection
4
Weeks Slower
Than Ten
Years Ago
8
Months from
Site ID to
Activation
Source:s
Lamberti, MJ, Chakravarthy, R, Getz, KA. Assessing Practices & Inefficiencies with Site Selection, Study Start-Up, and Site Activation. Applied Clinical Trials, August 2016
Tufts CSDD
http://www.appliedclinicaltrialsonline.com/need-and-opportunity-new-paradigm-clinical-trial-execution
2x
Increase in Time to
Negotiate
Contracts
In ten years, we haven’t seen improvement

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Study Start-Up
Shared goal is simple: Improve site activation cycle time
That Site Is Great!
“Med Affairs tells me that Dr. Smith
is a key opinion leader, and we’ve
worked with them on another
study”
Dr. Smith’s Site Takes:
• 8 months to contract
• 4 months to EC approval
• ….then never enrolls!
• Use Individual Activation “trackers”
• Subscribe to Investigator database
• Rely on anecdote à that site “takes
long to contract”
• Not centralized, not really measuring
• Not performance data; not measuring for
my organization
• Not scientific!
REALITY

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First, We Need to Measure
Cycle Times
• Site Selected to Financial Documents
Complete
• Site Selected to Ethics Committee
Approval
• Site Selected to Ready to Receive IP
(Greenlighting)
Site Selected
Site
Contracts
Executed
Mean: 72 days
United States: Local IRB
Site Selected
Site
Contracts
Executed
Mean: 45 days
United States: Central IRB
Track/Slice by:
• Country
• TA/study type
• Institution
• IRB/EC

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Use Data for Prediction and Prescribed Action
Upstream – Drive
Better Selection
Calculate Base
Plan
Risk-based Site
Activation
Critical Path –
Know Where to
Spend Time

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Intelligent Planning
Days Site Selected to
Site Greenlight
Days Site Selected to
IRB/EC Approval
Days Site Selected to
Contracts Executed
Days Site Selected to
Essential Docs Collected
Industry My Org This Study This Site

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Intelligent Planning
Risk Identification &
Proactive Alerting
Benchmarks to
project milestone
finish dates

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AI and Machine Learning in Clinical Operations
An eye toward the practical, using your own clean data
Document
Management

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A Typical Phase III Trial
Assumes one study spanning 4 years and 200 sites
Does not include site source documents and signed informed consent forms
1,000 parcel
shipments
96,000
documents
240,000
pages
100s of Documents Per Day/1000s of Doc Filing Options

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TMF Document Management Challenges
Duplication
Poor Compliance; Low Visibility; Higher Costs
QualityClassification ManualSecurity

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How Machine Learning Could Practically Help
Site
Investigator
Country
Classification
Date
Study
… document metadata
could be auto-populated
… eTMF autofiles or
suggest where to file
… minimize document
issues
… prevent inadvertent sharing
of sensitive information

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AI and Machine Learning in Clinical Operations
An eye toward the practical, using your own clean data
Monitoring

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Monitoring Complexity
Variability Discrepancies and
Query Resolution
KPI’s, KQI’s,
KRI’s
Protocol
Complexity
Multiple
Data
Sources
Multiple
Monitoring
Methods
protocol
adverse
event
SDV
Missing
data variance
KPI

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Intelligent Monitoring with Remediation Workflows
Continuous
Monitoring
Suggests (or automates)
issue / deviation
identification
Threshold
Exceeded
Triggers
Remediation
Workflow
Notifies CRA
Automates visit workflow
and visit deliverables

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Artificial Intelligence
• Artificial intelligence needs to be
unpacked and focused on the
practical. No more hyperbole.
• To get to the practical, we first
need high-quality data.
• There are many practical
applications in clinical operations
from study start-up to monitoring.
• The key to unlocking value is not to
run to the hype of AI.

25. Questions
• For more information, please contact james.reilly@veeva.com

26. Thank you