Technology is slowly but surely penetrating the healthcare industry in general and the clinical trials sector in particular. New and advanced solutions offer a variety of possibilities aimed to both improving existing processes and creating new and more efficient ones. And on top of all stands the desire to make clinical trials more patient centric. In all of this, even though some of the technologies have yet to mature enough to meet the high quality standards necessary, it is important to know them and begin imagining the promise they hold for clinical trials.

2. Introduction - Why are we talking about this?

Big data and Real World Evidence

Artificial Intelligence

Conclusions

Agenda

3. • Clinical trials’ length and costs have increased over the last decade
• Alternatively, technology is becoming more mature and adoption rates are growing
• Although some of the technologies are not mature enough at the moment to fully
penetrate the clinical trials sector, it is important to understand them and the great
promise they hold for processes improvement
Introduction

4. What is big data
- Very large scale data base
- Data can be structured / unstructured
- Requires advanced analysis methods
- Can reveal new hidden patterns and
correlations

5. Big data & Real World Evidence
• RWE is the big data of clinical
trials
• Derived from non clinically
sourced data (or secondary
resources)
Reflecting the patient’s experience using the drug in a real
world setting
Source: http://www.appliedclinicaltrialsonline.com/role-big-data-clinical-trials

6. Potential sources for big data in clinical trials

7. RWD VS. RWE

8. For whom?
Are there more
populations excluded
from the trials who can
benefit from the drug?
At what cost?
Is it cost effective?
In what context?
How does it best work t in the
patient’s everyday routine –
behaviors, comorbidities,
additional drugs they take and so
on.
What works?
In the real world and the
uncontrolled setting
Answering the “hard questions” with RWE
Source: https://www2.deloitte.com/content/dam/insights/us/articles/4354_Real-World-Evidence/DI_Real-World-Evidence.pdf

9. Gaining health economics
metrics as well as a signal they
are fulfilling the correct and
unmet need of their population
Evaluate and monitor a product’s
outcomes & performance in real
world setting prior to or post
regulatory approval
Be better prepared for regulatory
inquiries, combine insights back
into R&D to develop a more
accurate product
The value of “meeting” your market sooner
in the process

10. Drug performance in
real life & health
economics
Personalized medicine
Identify subsets of patients
who can most benefit from a
particular medicine
Interim evidence for
preliminary decisions
Accelerating reimbursement for life
saving therapies
Assist clinical decision making
Basing clinical decisions on a
richer and more patient specific
information
Long term risk-benefit
profile
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Uses of RWE
Scalable phase 4
Advanced BD and
R&D
Identifying new drug
targets or patient
populations
Synthetic control arm
Source: Getting real with real-world evidence benchmark survey. Deloitte, 2018

11. Patients are selected to
fit inclusion/exclusion
criteria and are receiving
the standard of care
Reducing enrollment burden
and costs
A “prospective
retrospective” tool
A control group
composed of
anonymized real patients’
data selected from a pull
of historical patients’ data
A synthetic control arm

12. Real world use case: The PROSPECT
study (Novartis)
Professor of Dermatology and Director of Research, Dalhousie University, Halifax, Nova Scotia, Canada.
Dr. Richard G.B. Langley MD, RPC(C)
"For both psoriasis patients and doctors, these data confirm that Cosentyx clinical data profile translates
into real-world benefits. In the everyday management of psoriasis, this provides added reassurance that with
Cosentyx, patients achieve and maintain high levels of skin clearance and improved quality of life."
L
Patients
2,002
Weeks
24
Disease had minimal to no effect on quality of life59%
Continued using Cosentyx up to 12 months87%
Source: https://www.novartis.com/news/media-releases/novartis-real-world-evidence-confirms-efficacy-and-safety-benefits-cosentyx-daily-life-psoriasis-patients

13. Lack of access to external data
Obstacles
implementing
a RWE
program
Accessing the right data
Absence of internal expertise
Uncertainty of how to apply RWE
Unclear ROI
Source: Getting real with real-world evidence benchmark survey. Deloitte, 2018

14. Barriers accessing the right data for RWE
Data quality
Lack of maturity
of data cleaning
methods and
acceptance for
statistical validity
Cost
Costs of
collecting and
maintaining this
kind of data are
still unclear
Patient
protection
Cyber attacks
and security
breaches may
concern patients,
driving them to
decline consent
for sharing their
data
Analysis
Limited
generalizability
and ability to
only determine
association and
not causality
Source: https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/real-world-evidence-from-activity-to-impact-in-healthcare-decision-making

15. Rolling out the use of
RWE

16. Agenda
Introduction - Why are we talking about this?

Big data and Real World Evidence

Conclusions

AI


18. Artificial Intelligence - AI
Create intelligent
machines, mimicking
the human decision
making processes.
AI makes it possible for
machines to learn from
experience, adjust to new
inputs and perform human-
like tasks

19. How is this being done?  Generally,
by ML& NLP
Algorithms and statistical models that computer
systems use to progressively improve their
performance on a specific task and simulate the
human learning process
Machine Learning
ML
programming computers so they will be able to
process and analyze large amounts of natural
language data
Natural Language Processing
NLP

20. AI use cases in clinical trials
Patient
Recruitment
Better match patients to
clinical trials based on
specified criteria
Trial Design
Optimization
manage workflows, predict
and manage risks
Patient Adherence
Predict which patients are at
risk of dropping out of
clinical trials to prevent
threats to trial validity

21. Patient recruitment
AI based platform crossing between
patient’s data and trials’
inclusion/exclusion criteria to successfully
match patient and trials and expedite
recruitment
Analyzing various sources including
patients records, doctors’ notes and more
building an extensive structured patient
data base to be easily matched with trials
criteria
AI in clinical trials Patient recruitment Trial Design optimization Patient Adherence
The pain
- Patients and physicians aren't always aware of
ALL available & relevant recruiting clinical trials
- When finding an option, a large volume of data
needs to be reviewed to determine if there’s a
match
- Current solution focus on:
- Locating relevant available trials
- Processing inclusion/exclusion criteria along with
the specific patient’s data
- Providing a list of trials the patient has the
potential to successfully match
Solution examples

22. Trial design optimization
Help manage more efficient workflows.
Algorithms are trained on billions of data points
from past clinical trials, medical journals, and
real-world sources to identify risk factors and
provide recommendations for optimizations.
Identify relationships and correlations to:
• Discover novel drug targets
• Find niche patient populations where the drug
is likely to be more effective
• Identify combinations of drugs likely to provide
synergistic effects
AI in clinical trials Patient recruitment Trial Design optimization Patient Adherence
The pain
- Clinical trials’ costs and length has
significantly increased
- Errors along the way can be translated to
considerable amounts of money
- Understanding the market and combining
the insights back into the development
process can improve efficiency and
decrease costs
Solution examples

23. Patient engagement & adherence
An AI based platform helping to confirm
medication ingestion in clinical trials and
high-risk populations. Its software identifies
the patient and medication and captures
evidence of medication ingestion
A medication management home robot.
Using face recognition and data analysis to
recognize each member of the house and
supply their prescribed medication. Alerts
and contact a third party when missed.
AI in clinical trials Patient recruitment Trial Design optimization Patient Adherence
The pain
- Patient engagement and adherence are a
very hot and painful issue
- Its significance rises when considering
virtual trials and digital health solutions
- A successful process can be translated
into shorter start up periods, less site visits
and a higher satisfaction rate. This means
lower costs and a decreased trial’s length.
- Current solutions are aiming to harness
technology to make patients more involved
and feel like a part of the process.
Solution examples

24. Study results showed 80%
increase in enrollment for
breast cancer study using Watson
Clinical Trial matching system
Watson uses AI to analyze
unstructured information
and pull out insights from
the data
The system generated a ranked
list of relevant trials for each
patient without making
clinicians read through EMRs
or long lists of eligibility criteria
AI case study: IBM Watson & Mayo clinic
Source: https://www.mobihealthnews.com/content/mayo-clinic-finds-ibm-watson-increases-enrollment-clinical-trials

25. Barriers -
Data is not
standardized Patient information is
not standardized
across institutions
Sources don’t
communicate with
each other
Systems and data
sharing methods differ
across institutions
Source: https://www.cbinsights.com/research/clinical-trials-ai-tech-disruption/#find

26. AI adoption in
the actual
clinical trial
process is still
in its early
stages
Overcoming
inertia is
necessary to
overhaul current
processes that no
longer work
AI benefits
and
limitations
are still
unclear
There’s a need
for digitization that
precedes the need for AI
The still common
handwritten clinical notes pose
unique challenges for natural
language processing algorithms to
extract information
There’s still a long way to go

27. • AI, big data and RWE hold the
potential to revolutionize the way
clinical trials are being conducted
today
• However, they are not fully matured
yet. There is still a long way to go until
they can be routinely embedded in
current processes
• Nevertheless, the upcoming years are
going to be very exciting in terms of
technology maturity and adoption,
and clinical trials as we know them
today will not remain as it is