From Master Data Management (MDM)… to Big Data · From Master Data Management (MDM)… to Big...
Transcript of From Master Data Management (MDM)… to Big Data · From Master Data Management (MDM)… to Big...
© 2014 Cognizant © 2014 Cognizant
13th November 2014
From Master Data Management (MDM)… to Big Data
A Cognizant Insight from Sowmya Srinivasan
© 2014 Cognizant
Introduction Sowmya Srinivasan, R&D Solutions Lead, Cognizant Life Sciences Bringing innovative solution to Life Sciences
organizations across Drug Discovery, Clinical
Development, Pharmacovigilance and Regulatory
Compliance.
+15 years of experience in R&D . Responsible for building capabilities in the emerging transformation areas including Real World Evidence, Biomarkers/Translational Medicine & NGS among other use cases. As a part of his responsibility he also builds and manages an ecosystem of partners in the area of R&D informatics
Prior to Cognizant, was part of the management team in a research informatics company, Strand Life Sciences focused on product development across bio and chem informatics.
Actively involved in Pistoia Alliance and tranSMART Foundation
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Cognizant works with:
• 9 of the top 10 Life Sciences Companies
• 12 Of Global Top 20 Med Tech Companies
• 28 Of the Global Top 30 Pharma Companies
• 75 Global Delivery Centers
• 1,000 Healthcare Clinical Experts
• 15,500 Global Life Sciences Associates
www.cognizant.com/life-sciences
Cognizant (NASDAQ:CTSH) is a leading provider
of information technology, consulting, and
business process outsourcing services.
• 187,400 employees globally
• $8.843bn Revenues in FY2013
• 1242 active Customers
• 26% of revenue in Life Sciences
© 2014 Cognizant
Agenda
Key Drivers of
Clinical
Transformation
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Leveraging Big
Data for Patient
Centric Clinical
Trials
2
R&D Big Data
Use Cases
3
MDM as an
Enabler for Big
Data Use Cases
4
Real World
Evidence
5
Getting Started
6
Conclusions
7
3
© 2014 Cognizant
Key Drivers For Clinical Transformation
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RnD Pressures
Cost Pressure
• Trials not completed on time and budget
• Personalized medicine leading to smaller
patient population
Outcome Focus • Pressure to generate evidence at time of
launch
• Scientific study outcomes differ from real
world outcomes
Patient Experience
• Complex medical literature for trials
• No consistency in patient experience
Cost Pressure • Healthcare payers imposing new cost
constraints on providers and scrutinizing
the value of medicines more carefully
Outcome Focus
• Focus on Real World Evidence for new
treatments to make sure they offer more
value than competing therapies
Patient Experience
• Image of pharma as aggressive pushers of
their products (not about patient wellness)
• Lack of brand differentiation in customer’s
mind
SERVICE
Differentiate product
to payers and providers
By Owning the Disease
And Generating Evidence
for
Pill + Service model
Real World Pressures
© 2014 Cognizant
There is a new ecosystem of clinical information about patients (Big Data)
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“Quantify me data”
Body Vitals
Health surveys
Risk Assessments
Social interactions
Health Reported
Outcomes
“Information of
things” Device data
App data
Web data
Sensor data
Wearables data
“Information of me
from others”
EMR / EHR Data
Adherence
Care planning &
management
© 2014 Cognizant
Leverage this new Big Data to enable Patient Centric Clinical Trials
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Pharma
Investigator Site
Patient
SPONSOR VALUE PROPOSITION
• Standard procedures and ICFs
• Better relationships with IRBs
• Predictive analytics
• Continuous improvements based on patient feedback
• Measure and evaluate site effectiveness
INVESTIGATOR / SITE VALUE PROPOSITION
• Easy scheduling of appointments
• Self-reported information from patients for better
diagnosis
• Personalized instructions
• Increase patient adherence and retention to
produce better health outcomes
• Detect non-eligibility/drop-out rate, earlier in the
trial
• Improve trial conduct
• Generate better health outcomes
PATIENT VALUE PROPOSITION
• Reminders and adherence tracking for
appointments and dosage
• Instant communication of
symptoms/adverse event to site
• Patient education
• ICF education
• Patient’s Voice through feedback on site,
procedures and ICF
© 2014 Cognizant 7
R&D Big Data Use Cases
DRUG
DISCOVERY
CLINICAL
DEVELOPMENT
DRUG
SAFETY REGULATORY
R&D Business
Development
New Market
Identification
Competitor-
Compound
Profiling
Genomic Technologies
Site and Investigator
Selection
Patient Selection
Safety Reporting
from
Social Media
Real World Data/Evidence
Regulatory
Monitoring
Predictive Sciences
Translational Medicine
Drug Repositioning
Biosensors and Imaging
Disease & Mechanism
of Action
Patient Centric
Drug Design
Post
Launch
Support
Patient Engagement Services
© 2014 Cognizant 8
Leveraging Data to Deliver RWE Use Cases Across the Spectrum
Potential to identify new biomarkers , track therapeutic area
specific biomarkers in various phases of trials
Support Biomarker
Identification
Integrates (Public) Genomic/
Genetic Study Data
Patient outcome insights
Enable Cross-Study Analysis
Investigator selection and
profiling
Virtual Clinical trails
Optimizing Study design
(patient size and cohorts)
Off-target (AE) identification
and validation
Contextualization of Real World
drug use through social
Listening
Gain insight from multiple clinical trials to improve
other studies and therapies
Identify and recruit right set of Investigators for a given
therapeutic areas
Disease / Patient stratification, translational medicine
Leverage Large datasets of patient population to build
simulation models.
Leverage existing non-clinical data and other external (EHR)
data to increase the clinical trial efficiency
Utilizing data and literature from the clinical and non-clinical
data sources to conclude a hypothesis related to human risk
assessment
To harness the power of structured and unstructured data to
improve the patient outcomes and reduce costs
Effectively use social media sources to conduct post-
marketing surveillance will greatly enhance understanding of
the safety & efficacy of their medicines in the Real World
RWE
Platform
Final study reports
Project protocols
Submission dossiers
Risk management
plans
Global and local
Medical Affairs Plans
Status information on
studies in progress
Approved abstracts
Value dossiers
Final study protocols
Statistical analyses
plans
e.g. EHR/EMR,
Patient registries
…….
Internal Data
Sources
CTMS,
Observational
Studies
……..
Commercial
Data Sources
e.g. Truven
……. HC data
External Data
Sources
Real World Data/Evidence
© 2014 Cognizant
Epidemiology Analytics and Patient Cohort Analysis
Client: Global Top Pharma
De-identified patient data is provided by third party data providers
Datasets can range from 500 GB to 2-3 TB
SAS analysis can take more than 10 hours due to the complexity of the processing.
Preparation of the control and analytic datasets can take up to several days
Business Need
Hadoop-based solution developed to leverage its parallel processing capabilities
Pig used for converting the datasets from multiple providers into a common format
Python used for applying the algorithms for the cohort analysis
Analysis results stored in Hive for querying and analysis using SAS
Use of HBase and Solr for fast search
Solution
Understanding of prevalence of secondary conditions
Better understanding of disease market
Improved trial design
Real time search of over million records in 2.5 seconds
Reduced processing time of Epidemiology analytics to 20 minutes
Benefits
Technology Landscape
MarketScan I3 Invision DataMart
Epidemiology
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© 2014 Cognizant
Type 2 Diabetes Research using Semantic Technology
Mayo Clinic used Semantic Web technologies to develop a framework for high throughput phenotyping using EHRs to analyze multifactorial phenotypes
Mapped Clinical Database to Ontology Model
Find All FDA-approved T2D Drugs; Find All Patients Administered these Drugs
RxNorm DailyMed Clinical DB
Find Which of these Patients are having a Side Effect of Prandin
RxNorm SIDER Clinical DB
1
2
3
Reprinted with permission from Jyotishman Pathak, Ph.D., Mayo Clinic
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5
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Find Genes or Biomarkers associated with T2D, as Published in the Literature
Diseasome DBPedia ChemBL
Selected Genes have Strong Correlation to T2D. Find All Patients Administered Drugs that Target those Genes.
Diseasome RxNorm ChemBL DrugBank Clinical DB
Find All Patients that are on Sulfonylureas, Metformin, Metglitinides, and Thiazolinediones, or combinations of them
Diseasome RxNorm ChemBL DrugBank
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Patient Selection
© 2014 Cognizant 11
Enabling Biomarker Focused Approaches Using Genomics Data
Use Case What Dataset? Key Observation
UC 1
Survival
Analysis
TCGA GBM
Level 1
- Survival Time
-Tumor Stage
Question? “What is the survival probability
between the two categories of patient
population”
Analysis: Create a Kaplan Meier Plot to
identify patient time to death between
Primary and Secondary Stage Tumor
progression Patients
Inference: Patient Stratification based on
survival time
UC 2
Differential
Expression
TCGA GBM
Level 2
- Normalized
Gene Expression
Question? “What are the potential
biological markers that are differentially
expressed between the two Subsets?”
Analysis: Create a Volcano Plot to identify
significant changing genes (up regulation or
down regulation)
Inference: A list of significant changing
genes between the two patient population
Gene Name Fold Change
EGR1 -0.919420489
GAP43 -0.931186136
SERPINA3 -0.989892738
Select the Cohorts ……. Cohort Explorer
Visualize ….. Spotfire
1
2 Analyze …… R scripts (pre-configured
by Cognizant for Differential Expression
and Survival analysis)
3
Observe 4
4 Step Process….
Spotfire integration:
Seamless transition.
No User Selection
Genomic Data
© 2014 Cognizant
Client: Pilot Project for Top 10 Global Pharma
Building a KOL Network
Build a network of high performing investigators and partners to improve trial performance and establish thought leadership
Be on the cutting edge of science and identify new focus areas
Early to market
Business Need
Semantic integration of data from external and internal sources
Manual curation and delivered as actionable insights
Monitors new trends and provides alerts and dashboards
Assign a confidence level to each of the elements being tracked
Data mart that will enable complex analytics and visualization
Solution
Plan new market entry
Identify partners for rare diseases in new/existing markets
Quick start clinical trials with a master list of investigators
Track and profile new/existing partners
Benefits
Cloud
Technology Landscape
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Site and Investigator Selection
© 2014 Cognizant
Emerging Countries
BRICS
China
Key Opinion
Leader
Warning Letters
Rare Diseases
Disease of Interest
Identify Patient Population
Patent
Geography
Unmet Need
Peer Reviews
Expert? (based on
confidence)
Identify Patient
Population
Collaboration
Unmet Need
Research Focus
Geography
Clinical Trials
Publication
Social Media
Journal
Conferences
Investigators
Therapeutic Areas
Research Focus Clinical Trials
Current
Collaboration
Working with
competitors?
Academia/Pharma/
Biotech?
Performance
Metrics
Inspection
Sentiment
Building a KOL Network KOLs working on DPP IV inhibitors, based in emerging markets with positive performance metrics and publications in journals, conferences and social media
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Site and Investigator Selection
© 2014 Cognizant
Positive Sentiment Based on FDA Inspections
Negative Positive
Building a KOL Network
Geographical Spread of KOL’s KOL’s in DPP IV Inhibitors
No.
of Publications
Key Opinion Leader
Geographical spread of KOLs and focus on state with maximum KOLs
Number of publications in journals, social media and conferences
Publications in Media
KOL’s with highest number of publications
Charts highlighting publications in various media for KOL with an
overall sentiment
Identifying KOL’s with positive FDA investigation report
Site and Investigator Selection
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© 2014 Cognizant 15
Digital Recruitment & Digital Site Selection
DIGITAL
RECRUITMENT &
DIGITAL SITE
SELECTION
INDUSTRY
TRENDS
Platform
Single sign on (SSO) for
seamless investigator experience.
Investigators Quality, streamline processes,
regulatory compliance, capacity
Costs related to:
• Training
• Document exchange
• Support & maintain
• Help desk
Startup time
Streamlined electronic
audit process, insight into
trial, harmonized
information model
Productivity (via reduced
redundant tasks & streamlined
processes), access to information
Study startup time, redundant
training
Target Outcomes
Launch of a common investigator portal, with early capabilities including:
• Investigator training, Site Feasibility Surveys, Document Exchange, Management of facility and investigator
information
Single technology platform for investigators to interact with multiple sponsors
Enhanced user experience
SHARED INVESTIGATOR PORTAL
Site and Investigator Selection
© 2014 Cognizant 16
Digital Recruitment & Digital Site Selection – Leveraging MDM
DIGITAL
RECRUITMENT &
DIGITAL SITE
SELECTION
INDUSTRY
TRENDS
Source: Forecasted for 2016 according to Frost & Sullivan
IMPLICATIONS FOR DIGITAL STRATEGY
Master Data Management (MDM) –
Unique Site ID (and Investigator
ID)
• Updates to Clinical Systems & Processes
• Greater Transparency on Investigator: Sponsor
relationship
Single set of Standard Clinical
Documents & Templates
• Updates to Clinical Systems & Processes
• Opportunity to standardise CRO outputs to drive
reduced risk to the sponsor organisation
Decommissioning of Existing
Investigator Portals • Reduced TCO through flexible pay-as-you go model
• Need to integrate with SaaS model
Site and Investigator Selection
© 2014 Cognizant 17
Master Data Management in Clinical Trials Summarized
Approve Protocol
Select Investigator
Enroll Subject Select Site Conduct Study Analyze &
Report
The clinical trial process entails a complex set of regulated processes involving multiple participants from heterogeneous domains. We have
identified the following pain points in the process which can be optimized to drive more value to the entire drug lifecycle
X Absence of investigator knowledge resulting in dropouts and termination
X Absence of site data repository resulting in penalties due to inaccurate audit
reporting
X Manual site selection process is inefficient and cause delays
X Study and API relationship is not stored optimally for Statistical analysis
X Subject enrolment takes longer due to lack of optimized process
X Multiple points of Study creation resulting in ambiguity and absence of an Unique
Study ID
Study
API
Investigator Site
Study –Drug Relation
Investigator Selection Site Selection
Process Pain Points Clinical MDM Entities
MDM
© 2014 Cognizant 18
Using a BYOH strategy in Clinical Studies
OUR
POV ON
BIOSENSORS
Combine, Correlate, and draw inference from Clinical & Device Data streams
Patient
BT Inhaler
Device
BT enabled
Spirometer
• Dosage Time/
Date logs
• Inhalation flow
profile
Data
Aggregator • Sensor data and
adherence insight
Health Coach Patient App
• Personalized
Communication
Collect & Transmit
Collect & Transmit
Evaluate
Engage Intervene
Physician
• Real time pulmonary
functions (FEV /
PEF etc.)
Pattern detection, by linking the
behavior of biosignals to
known phenomenon that occur
within the body
1
Clinical decision support
for intelligent intervention
2 Real World Evidence & Evidence
based medicine
3
Dynamically reconfigure study based
on patient characteristics
4
Pharma
Biosensors and Imaging
© 2014 Cognizant 19
Using a BYOH strategy in Clinical Studies
OUR
POV ON
BIOSENSORS
CONNECTING TECHNOLOGY WITH HUMAN TOUCH
Hi-Touch
Remo
te
Nurs
e
Virtual
Coach
Hi-Tech
Hi Tech
Behavioral
Change Tools
PATIENT
CARE
Healt
h
coac
h
Remote
Nurse
Health
Coach
Virtual
Coach
• Appt. Reminders
• Goal Setting
• Patient Follow-Up
• Drug Reminders
• Tips & Challenges
• Patient Education
• Real-time Messaging
Patient
Education
Medication
Reminders
Appointment
Reminders
Virtual
Coach Gamification Feedback
&
Surveys
Biosensors and Imaging
© 2014 Cognizant 20
Adopting Big Data requires a new model for experimental evaluation
New Opportunity
New Data Sources
New Technologies
New Stakeholders
New Processes
• Generate idea
• Enumerate opportunity
• Technical assessment
• Refine opportunities as
needed
• Review Design Concept
• Go/No Go Decision
• Pilot created
• Users informed
• Review Design Concept
• Go/No Go Decision
• Pilot created
• Users informed
• Review scale up potential
• Production project formed
• Performance optimization
• Additional requirements
• Business process redesign, if
needed
• Training and roll out
Data Sources
© 2014 Cognizant 21
Conclusions
LEVERAGING BIG
DATA FOR
CLINICAL
TRANSFORMATION
• Clinical is moving towards an health ecosystem leveraging new types of
data.
• The implications of this shift would be
• Need to integrate device data
• Collaboration with partners for site and investigator selection
• Patient selection and stratification leveraging genomics data
• Pharma can get started with an experimental approach and iteratively
build the platform