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Real-World Evidence in the Cloud: How
Technology is Revealing the Big Picture in Pharma
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that is deployable in minutes from AWS ser vice
catalog, makes it possible to create secure, easily
searchable data lakes in minutes, as opposed to
the potentially long lead times to procure and set
up on-premises IT infrastructure.
The company-wide data access enabled by cloud-
based data lakes is the first step to empowering
teams to seize opportunities created by RWE. The
second step is to enable workers of all levels of
technical know-how to glean insights from data.
Collating and mining real-world data for evidence
was traditionally a technically demanding task. This
is changing, though.
Celgene has created self-ser vice, point-and-click
applications to enable non-coding staff to perform
analyses.
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With the associated powerful data analytics
in the cloud, Celgene reduced patient cohort analysis
time from months to minutes. Using these applications,
anyone can question data and produce interactive
visualizations, cutting the time and resources it
takes to generate insights.
The flexibility afforded by building a data lake on
AWS means you can ingest data in any format and
can perform data analysis on-demand, regardless
of what new types of data may be available from
pharma patients in the future. And hosted in the
highly secure cloud infrastructure with programs to
help organizations meet local and regional regulatory
rules for data protection and provisioning, including
HIPAA, HITRUST, and GDPR requirements.
A 2017 Deloitte survey
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suggests companies that realize
these benefits by adopting cloud systems are happier
1 Invent 2017: Real World Evidence Platforms to Enable Therapeutic
Innovation (LFS302). (2017). http://paperpile.com/b/xoZLH3/4WNp
2 Getting real with real-world evidence. Available at: https://www2.deloitte.
com/content/dam/Deloitte/us/Documents/life-sciences-health-care/us-ls-
2017-real-world-evidence-survey-031617.pdf. (Accessed: 26th June 2018)
with their RWE setups than peers that use on-premises
infrastructure. e survey found more than half of leading
pharma companies are investing to significantly improve
RWE capabilities, typically due to dissatisfaction with
their existing on-premises systems.
In contrast, companies with cloud-based RWE systems
are far less likely to feel the need to significantly
improve their capabilities, with 80% stating they are
content with their RWE capabilities. This satisfaction
gap reflects the fact that the scalability, security, and
speed of the cloud make it a better fit for the RWE
needs of modern pharma companies.
HARNESSING ARTIFICIAL INTELLIGENCE
TO ACCELERATE ANALYSIS
Easy-to-use, real-world data analysis tools are being
supercharged by artificial intelligence and machine
learning. Today, vast quantities of data are available
but the size of the repositories and complexity of
the information makes manual, human-powered
analysis impractical, if not impossible. Fortunately,
artificial intelligence and machine learning can sift
through data far quicker than manual inspection,
making it possible to generate insights within a
fraction of the time it would take a human.
At the same time, AWS is lowering the barriers to
entry to machine learning to expand what companies
can learn from their data.
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GE Healthcare is using
services such as Amazon SageMaker to streamline the
development and deployment of machine learning
models to help radiologists improve health outcomes
while reducing errors.
S i m i l a r l y, a p h a r m a c o m p a ny c o u l d m i n e
reimbursement claims data, historic study site
3 Partnering for AI Innovation. WSJ (2018). Available at: https://partners.
wsj.com/aws/partnering-for-ai-innovation. (Accessed: 24th July 2018)
