Healthcare and Life Sciences organizations are using data to generate knowledge that helps them provide better patient care, enhances biopharma research and development, and streamlines operations across the product innovation and care delivery continuum. Next-Gen business intelligence (BI) solutions can help organizations reduce time-to-insight by aggregating and analyzing structured and unstructured data sets in real or near-real time. AWS and AWS Partner Network (APN) Partners offer technology solutions to help you gain data-driven insights to improve care, fuel innovation, and enhance business performance.

1. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Next-Gen Business
Intelligence (BI) for Healthcare
and Life Sciences
March 21, 2018 | 10:00 AM PT
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Oxana K. Pickeral, PhD, MBA, Global Segment Leader,
Healthcare and Life Sciences, AWS Partner Network, Amazon
Web Services, Inc.
Dan Housman, Consulting Managing Director,
ConvergeHEALTH, Deloitte Consulting, LLP
Patrick Loerch, Senior Director, Data Sciences, Celgene
Corporation
Brad Bostic, CEO, hc1.com
Michele Koester, Core Lab Operations Supervisor, and Patti
Today’s speakers
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• An overview of Next-Gen BI solutions for Healthcare and Life
Sciences (HCLS) with Amazon Web Services (AWS)
• An introduction to AWS Partner Network (APN) Partner solutions:
Deloitte and hc1.com
• Case studies: Celgene Corporation and North Memorial Health
• Q&A / Discussion
Today’s agenda
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Learning objectives
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• How cloud-based analytics can be used to fuel innovation in
biopharmaceutical product development and patient care
• How AWS services can help inform business decisions in HCLS
organizations
• How to get started with Next-Gen BI on AWS with help from APN
Partners

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Modern decision support tools on
AWS for Healthcare & Life Sciences
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Patient-
targeted
R&D
Lower-friction
interfaces
Healthcare data
interoperability
Patient
Improved
physician support
tools
Many factors drive better patient outcomes
Integrated, data-driven action

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Data and cloud growth in HCLS by the numbers
65% Percentage of Healthcare organizations that are
already using cloud-based services, according to a
2017 HIMSS study.
$50B+
Expected IT spend by Life Sciences organizations
by 2019.
$200B+
Projected size of the global digital health market
by 2020.
88% Percentage of those organizations using SaaS
(software-as-a-service) solutions.

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HCLS organizations are using data to drive
innovation
Enhanced
operational
efficiency
Improved
R&D with
faster time-
to-insight
Optimized
patient
therapy
Automated
compliance
at scale

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AWS offers a native stack of decision support solutions
Amazon QuickSight Amazon Macie Amazon SageMaker
Business
analytics and
data visualization
Automated
data security
Automated
machine
learning (ML)

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APN Partners can help you with:
Real-world evidence (RWE) analytics
Interoperability and care coordination

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© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Deloitte ConvergeHEALTH Miner:
A real-world evidence (RWE) platform for life
sciences organizations
Dan Housman, Consulting Managing Director,
ConvergeHEALTH,
Deloitte Consulting, LLP
Patrick Loerch, Senior Director, Data Sciences, Celgene
Corporation

12. Copyright © 2017 Deloitte Development LLC. All rights reserved. 12
We believe life sciences companies need new solutions to “connect” and “converge” with
the broader health care ecosystem to sustain market leadership and deliver healthier
outcomes.
Patient Connect™
Create meaningful engagement with patients
in your ecosystem
Safety™
Streamline insights
to improve patient outcomes
Miner™
Enable insight-drive decision making from
R&D to Commercialization
CONNECT
Life sciences innovators have the opportunity to connect and engage with a
health care ecosystem that is demanding a new level of connectivity.
Leveraging cloud based, digital health platforms to engage with the healthcare
ecosystem in new ways (patients, providers, payers, regulators), can fill
evidence gaps to create a virtuous learning healthcare system.
CONVERGE
The proliferation of health care data has created the opportunity for life
sciences innovators to drive insights across the value chain, but requires a
convergence of capabilities from new technology platforms to external
partnerships to new operating models. Success will become dependent on the
ability to utilize evidence as a critical asset to inform decision-making across
the value chain.
Leverage insights to fill evidence gaps, inform engagement model,
and support decision-making
Capture new kind of data through connection with a broad set of
stakeholders enabled by digital transformation

13. Copyright © 2017 Deloitte Development LLC. All rights reserved. 13
Celgene iKU - The Power of Partnership

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Build Foundation
Enable Business
Realize Value
● Learn
● Collaborate
● Pioneer
● Network of Strategic Partners
● Organizational Expertise
● Pioneering Mindset● Connective Technology
(Synapse platform)
● Data Harmonization
(Governance & taxonomy)
iKU: Transforming How We Think, Act, and Lead with Data

15. Copyright © 2017 Deloitte Development LLC. All rights reserved. 15
Research & Early
Development
Market Access
& HEOR
Commercial &
Regulatory
Ph I Ph II Ph III
Early Research
Clinical
Global Clinical Trials Operations
Regulatory
Translational
HEOR
Medical Affairs
Commercial Ops
Market Access
Pharmacoepidemiology
Patient Data Drives Decisions Across the Pipeline

16. Copyright © 2017 Deloitte Development LLC. All rights reserved. 16
Research & Early
Development
Market Access
& HEOR
Commercial &
Regulatory
Ph I Ph II Ph III
Persistent
Sales People
1000’s of
siloed employees
Countless,
redundant data
siloes
Then: The Rise of the External Data Vendor

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Dataset Refresh Rate Population Coverage Data Type Coverage
Safety Data Daily (1 day lag) Subset of Patients Safety Data
Patient Engagement Weekly (no lag) Small Subset of Patients Limited Sales Data
Sales Operations #1 Weekly (no lag) Small Subset of Patients Sales Data
Sales Operations #2 Weekly (1 week lag) Subset of Providers Limited Sales Data
Sales Operations #2 Weekly (3-4 day lag) Subset of Providers Sales Data
Prescriber Network Weekly (no lag) Reference Data Reference Data
Shipments Data Weekly Subset of Providers Individual Product
Demand Data Monthly (1 month lag) Subset of Providers Individual Product
Healthcare Org Services Data Monthly ( 1 month lag) Subset of Providers Reference Data
Partner Data #1 Monthly (1 month lag) Disease Population EMR Data
Claims Data #1 Quarterly (4 month lag) US Population Claims Data
Claims Data #2 Quarterly (9 month lag) US Population Claims Data
Claims Data #2 (OMOP) Quarterly (9 month lag) US Population Claims Data
Claims Data #3 Quarterly (9 month lag) US Population Claims Data
Claims Data #3 (OMOP) Quarterly (9 month lag) US Population Claims Data
Provider Network Data #1 Twice per Year (6 month lag) US Providers Channel Affinity
Provider Network Data #2 Twice per Year (6 month lag) US Providers HCP Accessibility
Partner Data #2 Monthly Disease Population EMR, Genomics & Lab Data
Partner Data #3 Quarterly European Populations EMR, Genomics & Lab Data
Partner Data #4 Monthly Disease Patients EMR, Genomics & Lab Data
CommOps
HEOR
BrandTeams
Narrow Coverage Broad Coverage
Slow Refresh Rapid Refresh
Market Research Data
Market Research Data #1 Monthly Subset of Providers Chart Abstraction
Market Research Data #2 Monthly Subset of Providers Chart Abstraction
Real World Partnerships
*to be Ingested
GCRDO
R&ED
Safety
Medical
Affairs
Market
Access
Today: What is our data footprint?

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Understanding Our Data Gaps

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Cultivating a Network of Data Partners
• Cultivating clinical and genomic data on
previously uncharacterized patients to drive
insights across the value chain
• Leverage emerging technologies and
skill-sets to augment internal capabilities
• Risk/balance portfolio approach with near,
mid and long-term value creation
• Partnerships aligned with Celgene’s
strategic direction
• Deal structures tailored to aligned
interests with partners
• Active engagement through business
development and alliance management
Value
Approach

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Data Lake
& CDMs
Analysis
Tools
Code &
Cohort
Sharing
Visualization
Self
Serve
Applications
Summary Statistics
• 388 RWE databases tracked
• 24 DBs ingested & regularly
refreshed
• ~120-150M patients per
claims DB
Features
• “Big Data” Analytics
• Data governance
• Code governance
• Interactive visualizations
• Self-serve applications (non-
coders)
Data
Catalog &
Search
Knowledge
Management
Platform

21. Copyright © 2017 Deloitte Development LLC. All rights reserved. 21
Introduction
• Treatment for Crohn’s Disease (CD) has advanced over the past 20 years with the
introduction of biologics.
• Despite the availability of biologics, patients may not be optimally managed.
Objective
• The aim of this study is to identify and visualize CD treatment pathways to gain insight into
real-world treatment patterns.
Methods
• The MarketScan Commercial and Medicare Databases were used to assess treatment
pathways in a large US insured population.
‒ Patients had ≥2 consecutive health claims for CD* or UC† ≥30 days apart, with
≥1 occurrence of NDC/HCPCS codes for CD or UC medications from January 1, 2008, to March 31, 2016.
‒ Required ≥3 (1 pre-diagnosis + 2 post-diagnosis) years of continuous enrollment
Example: Real World Treatment Pathways in Crohn’s Disease

22. Copyright © 2017 Deloitte Development LLC. All rights reserved. 22
5-ASA=5-aminosalicylic acid; IST=immunosuppressant (i.e., immunomodulator).
42%
35%
7%
Corticosteroids
5-ASA
5-ASA+Corticosteroids
IST
Surgery
Biologic
Other_Combo_NonBio
Other_Combo_Bio
Biologic+IST
5-ASA+Corticosteroids
Corticosteroids
5-ASA
Other_Combo_NonBio
IST
Other_Combo_Bio
Biologic
Surgery
Biologic+IST
Corticosteroids
5-ASA
5-ASA+Corticosteroids
Other_Combo_NonBio
IST
Biologic
Surgery
Biologic+IST
Other_Combo_Bio
N=16,260
Crohn’s Diagnosis
Example: Real World Treatment Pathways in Crohn’s Disease

23. Copyright © 2017 Deloitte Development LLC. All rights reserved. 23
Take Away
• The changing healthcare landscape requires pharmaceutical companies to
increasingly become data-driven organizations
• Data needs to be proactively cultivated and evolve within the context of current and
upcoming medicines in the discovery and development pipeline
• As in other industries, IT and information platforms need to encourage data,
knowledge, and code sharing… with the appropriate access controls
• Through working with Deloitte, and leveraging AWS, Celgene has developed an
industry-leading, global platform spanning from data ingestion to knowledge sharing

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© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
hc1.com:
A customized CRM platform for healthcare
organizations
Brad Bostic, CEO, hc1.com
Michele Koester, Core Lab Operations Supervisor, North Memorial
Health (MN)
Patti Smith, Laboratory System Director, North Memorial Health (MN)

25. Proprietary and Confidential © | Personalizing the Healthcare Experience ®
Unlocking Answers to
Healthcare’s
Critical Challenges

26. Today’s Biggest Challenges for Healthcare Providers
Providers must deliver better quality care at a lower cost
Downward
pressure on
reimbursements
New mandates by payors
for quality tracking &
performance metrics
Increasing competition from
existing and new entrants

27. LIS #2
LIS #2
REHAB
BILLING
EMR
ADT
1
The #1 Obstacle Faced by Healthcare Entities

28. The Answer: Holistic Profiles & Live Analytics
Tasks
Phone Calls
Contact Info
Automated
Workflows
Patients
Cases
Tasks
Provider
Accessions
Results
Panels
Specialties
Ordering
Locations
Blood Products
Ordering
Diversions
Holistic Provider Profiles Real-Time Dashboards

29. Real-World Challenge: Utilization
$1.5 – $2.5 billion wasted per year on
inappropriate & unnecessary blood
transfusions
$2.1 – $2.5 billion of healthcare costs
spent each year on unnecessary lab
testing
Over Utilization is Costing Healthcare

30. Test Utilization in Action

31. Data Rich, Insight Poor
31

32. Previous State:
Strained Resources, Outdated Results & Diminished Service
32
Static Reports and
Spreadsheets
Business Analyst
Business Analyst
Business Analyst
Old Data
Old Data
Old Data

33. Current State:
Efficient Processes, Real-time Visibility & Better Outcomes
33
Real-Time Data Visualization
and Drill Down Insights
Real-Time Data
Real-Time Data
Real-Time Data

34. Five Categories of Test Utilization
34

35. 35
Use Case #1:
High Cost & Unreimbursed Testing

36. Homocysteine, Plasma
36

37. 37
Use Case #2:
Obsolete or Unproven Test Ordering

38. 38
The Problem
Free T4 vs. Total T4
Free T4 is a better indication of the clinical
picture

39. 39
Communication Challenge
1st Round of Communication
2nd Round of Communication
Pathologists Contacted Providers• Emailed all providers to order Free T4 test instead
of Total T4
• Saw an initial decline then volume increased again
(using hc1 for order volume visibility)
• Sent another email to all providers
• Saw another decline in ordering practices
• Drilled down to find specific provider
groups

40. 40
Total T4

41. 41
Test Utilization Successes with Next-Gen BI
Relationship
Building
Sustainability
• Achieved a 50% reduction in
high-cost unreimbursed test
ordering
• Generated 127% increase in
new test ordering over obsolete
tests
• Enhanced client
communication
• Faster issue resolution &
greater client satisfaction
• Ordering practice is now
mainstream
• Proactively identifying
trends to uncover
meaningful, actionable data
Key Takeaways

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Q & A

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Learn more about Deloitte and hc1.com on AWS
• www.hc1.com
• www2.deloitte.com/us/en/pages/consulting/solutions/clinical-
performance-data.html
Learn more about Next-Gen BI for Healthcare and Life Sciences on AWS
• aws.amazon.com/health/featured-partner-HCLS-Next-Gen-BI/
Try AWS for free:
• aws.amazon.com/free/
Next steps and further information:
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Thank you!
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