The Future of Real World Evidence Analytics

Copyright © 2017, Saama Technologies
The Future of
Real World Evidence
Dr. Dee Amanze
Director, Clinical Consulting

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1
TOPICS
Analytics Advantage
Current state of affairs
Looking ahead
The future is now
Part-1
Part-2
Part-3

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33

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4
Two options: Make the square wheel go faster or accept radical change?

55
5
$98B
Sales and
Marketing
Spend
$66B
R&D Spend
$127B
2016 R&D
Spend
Total Market Spend
$148B
2020 R&D
Spend5-6%
Cost of RCTs
Cost Pressures
Avg. cost of RCTs
+1 day to a trial
+1 submission delay
$1B
$1M
$10M
72%
Studies are 1
mo. behind
70%
Of studies
experience
patient
Enrollment
delays
20%
Never recruit a
single subject
30%
Of time spent
on patient
recruitment
$86M
PIII Study
9mo. – 2yrs
to complete
enrollment
$10B+/yr.
Wasted due to poor
site selection
(SSD-SSU)
Common Industry Pain Points:
Increase in study complexity
Age old enrollment challenges
Timeline overrun
Out of control budget
10% product approval
51% post approval success
Silos mentality

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6
What does the future
hold for
Real World Data
Analytics?

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7
Data collected at the
point of service,
processed and
available for analysis
in real time.
Wi-Fi enabled
devices
Wearable devices
Cloud
computing

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Real World Evidence
applied at the
point of care.
Therapy based on
genomics e.g. PD-1 and
PDL-1 in NSCLC
Most effective therapy based
on RWD
Discontinuation of
therapy with patient-
reported adverse
experiences
RWEBED
SIDE
RWD

99
9
Querying big data by voice command
Vast amounts of valuable insights that could be derived from Healthcare data are locked
behind bars in several data silos.
Many potential beneficiaries are unable to derive Real World Evidence from ‘Big Data’
because of limited or no programming and data mining skills.
An interactive Voice Command System that uses natural language to query Real World
Patient Data.
(brief demo)
Raise Real World Big Data Analytics to the power of Voice Command. Set Big Data
free for ALL!

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Genomics
Internal, M&A, External,
Syndicated
Wearable Devices
High
Variety, Volume, &
Velocity Data
Analysis
Organization
Ingestion
Automated
Data Wrangling & Deep Data Science
Business Aware
Data Analytics Services Oriented
Architecture
Harmonization
Integration
Analysis
Organized Storage
Provisioning
Aggregation
Modern Technologies
Going Forward
Configurable
Analytic
Applications
Business
Outcomes

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11
Clinical Data
Assets
Longitudinal Real
World Data
Published
Literature
Social
Data
OMICs
Data
Patient Level
Data Pipelines
Text Analytics
Pipelines
Foundational
Capabilities
Workflow Designer
Advanced Analytics
Data Resolver
Business Rules
Editor
Cohort Builder
Business Specific
Applications
Clinical Development
Clinical Development
Feasibility
Safety Signals
Business &
Science Driven
Actionable Risk
Assessment,
CRO Oversight
Study Monitoring
Safety/Efficacy
Quantify Study
Feasibility, Patient
Enrollment, Protocol
Design and Site/PI
Selection
Automated Signal
Detection over Global
Data
Ad-Hoc Analytics
(e.g. drug differentiation,
biomarker CDx)
Outcomes
Semantically
Organized
Information
Meta Data
Security
Governance
Glossary
Centralizing with Purpose – the Frontier

1212
Unstruc
tured
Data
Unstructured Data
Structured Data
Syndicated Data
Transactional
System
Social Media Data
Streaming Data
API
Source
Data
Unstructured Data
Operating Systems
Windows Linux
On - Premise
Cloud
AWS MS AZURE Google Cloud
Hadoop Distributions
Amazon EMR
Unstructured
Data
Landing Zone
Unstructured
Data
Connectors
API
JDBC
SDK
Unstructured
Data
Change Data Capture
Unstructured
Data
Data Profiler, Rule Engine
Unstructured
Data
Rules Repo,
Workbench Repo
Postgresql
Unstructured
Data
Common Data Model
Unstructured
Data
Data Transformation
Pig
(Hortonworks Stack)
Impala
(Cloudera & MapR
Stack)
Unstructured
Data
Aggregated Entities
Impala
(Cloudera & MapR
Stack)
(Hortonworks Stack)
Unstructured
Data
Search
(Default)
Unstructured
Data
Web Service
RESTful
Unstructured Data
Security
Authentication
LDAP
Kerberos
SAML
Authorization
RBAC
ACL(Service Level, Directory
Level, Column Level)
Encryption
SSL
Transparent
Encryption
Encryption Zones
AES 256
Unstructured Data
Data Governance
(Hortonworks Stack)
(Cloudera Stack)
Technology Reference Architecture

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Ingest &
Rules Apply
Standardize
(CODM)
Apply Metrics
Analytic
Ready Data
Clinical Operations
KPI/Metrics Pipeline
Ingest &
Rules Apply
Standardize
SDTM
Convert to
ADaM
Analytic
Ready Data
Clinical Sciences
Patient Analytics Pipeline
CTMS
Project
Manage
CRO
EDC
Labs
Biomarker
Clinical
Development
Analytics
Data Management – Data Pipelines

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Who is Saama?
Based in Silicon Valley, California with a global foot print, serving Pharma and
Life Sciences clients for 20 years.
Our specialties include Technology-based solutions, Big Data Analytics
Platforms, Consulting Services and Partnerships.
Proud sponsor of the Data and Analytics for Pharma Summit.
Visit us at Booth #8 for a friendly chat.

The Future of Real World Evidence Analytics

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