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

2

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

http://www.cognizant.com/

© 2014 Cognizant

Agenda

Key Drivers of

Clinical

Transformation

1

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

4

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)

5

“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

6

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

9

© 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

4

5

6

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

10

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

12

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

13


© 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


14


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



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


http://www.sensorsmag.com/specialty-markets/medical/strong-growth-predicted-biosensors-market-7640


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


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



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



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


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