saama - centralizing data to address imperatives in ... · pdf fileoracle siebel ctms edc flat...

SCDM 2017ANNUAL CONFERENCE

September 24-27 I Orlando

v

Copyright © 2017, Saama Technologies

Centralizing Data to Address Imperatives in Clinical Development

v

The time for Transformation is NOW…

v

Copyright © 2017, Saama TechnologiesCopyright © 2017, Saama Technologies

AGENDA

The Fourth Industrial Revolution

Cognitive Sciences in Medicine

State of Clinical Trials Today

Centralize & Contextualize Data

Next Gen Clinical Data Mgmt

Putting it Together

Speed & Specificity

v


v

v


v

Next wave of transformation

Yesterday

People

Domain

Delivery Excellence

Today

People

IP Assets

Industry Transformations

Tomorrow

People

Cognitive Systems

Robotic Process Automation

outcomes@speed

v


v

Current state of Clinical Trials

Period: 2012-2016

Over

Time

On Time

On Budget Over Budget

Laggards

20%

Perfect

10%

Costly

40%

Help Me

20%

Abandoned

10%

Top-10 Contributors – All

1. Insufficient/low/poor enrollment

2. Lack of efficacy

3. Change in business focus (Sponsor)

4. Serious Adverse Events (SAEs)/death/safety concerns

5. Investigational New Drug withdrawn/discontinued

6. Protocol deviation

7. Terminated by regulatory agency

8. Funding support withdrawn

9. Administrative/ Technical reason/ Business decision

10. Unavailable study personnel

v


v

72%studies are 1 mo.

behind

SCHEDULE

70%of studies

experience

patient

enrollment

delays

20%does not recruit a

single subject

30%of time spent on

patient

recruitment

14%Trials are

abandoned

v


v Discovery Pre-Clinical Phase 1 Phase 2 Phase 3 FDA Post Market

• Siloed Data

• Not Standard & Bad Quality

• Timeliness

• Scalability & Cost

But, there is a Data Problem• Study Design & simulation

• Manage Multiple CRO’s

• Ongoing Risk – Enrollment, Study completion

• Operational Issues – Performance {Site, Study,

Trial}

Trial Management has to stay on top

• Completed On Time

• Within Budget

• Detect failure early

Trials need to

Need for an Integrated

Data SolutionClinical Trials

• Effectiveness and Usage in Real World

• Comparative evidence against competitors

• Safety concerns & Adverse events

• Clinical & Economic value

• Any unmet needs?

• Drug repositioning • Study design & simulation

• Clinical development feasibility

• Variety of Data (Avg 40-50 sources)

• Geography adds to complexity (EU/APAC different than US)

• Siloed & bad quality data

• Extremely expensive & time consuming considering data volumes (Weeks and

M$’s)

Need for an

Integrated Data

Solution

v


v

Clinical Data

management

is ripe for disruption

60%Of trial activities

are data management

related

20%of trial $ for

clinical data sciences

$127B2016 R&D Spend

Total Market Spend

$148B2020 R&D Spend

5-6%

v


AGENDA

The Fourth Industrial Revolution

Cognitive Sciences in Medicine

State of Clinical Trials Today

Centralize & Contextualize Data

Next Gen Clinical Data Mgmt

Putting it Together

Speed & Specificity

v


v

Objectives & Targeted Outcomes

Centralized Clinical

Data Lake

v


v

• Proactive

intervention

• Strategic vendor

collaboration

• Financial forecasting

Operation

• Track progress to

endpoints

• Go/no go on next study

• In-sillico meta-analysis

Innovation

• Ingestion

• Quality check

• Standards

• Transform

• KPI’s

• Biostatistics

• Validation

Automation

Objectives & Targeted Outcomes

Data Mgmt. & Quality

Evidence Generation

Time to Insight

& Cost

Centralized Clinical

Data Lake

v


Clinical Data Pipeline

Data Ingestion Load CMTS, EDC

and Lab data into

Hadoop

ecosystem

Landing Layer Data as-is from

Data

(Rules Engine)

Centralized

of all Business

Persist Layer Raw Data

Standardization

Golden Rule;

Vocabulary

Match & Merge

Data vocab for

TA, etc.

CDM Model Common Data

Operational data

Metrics Calculate

KPIs/KRIs/Metric

Values

Analytical Layer Aggregate data

Match & Merge;

Data Vocabulary

Patient Data

Metrics

Dashboards

Bio-Statistical

Analysis

• Studies

• Site

• Subject

• Study metadata

• Clinical/Audit Records

Parsed XML

data Store

Patient Data Patient data (Clinical

Audit Records) in domain

containers

BRE Engine

Operations Data

Operational

Metrics

Repository

I(HIVE)

Data

Acquisition

Data

AcquisitionLanding

LayerPersist Layer

Common

Data Model

Aggregate/Analytical

Layer

Landing

Layer

Persist

Layer

Patient Data

Container

Patient Data

Model

Oracle Siebel

CTMS

EDC

Flat Files

EDC

External

Lab Data

Source Data

Raw

Data

Standardized

Data

Golden

Rules

DQ

Rules

DQ

Rules

Raw

Data


v


Ingest & Rules

Apply

Standardize

(CODM)Apply Metrics

Analytic

Ready Data

Clinical Operations

KPI/Metrics Pipeline

Ingest & Rules

Apply

Standardize

SDTMConvert to

ADaM

Analytic

Ready Data

Clinical Sciences

Subject Analytics Pipeline

CTMS

Project Manage

CRO

EDC

Labs

Biomarker

Clinical

Development

Analytics

Centralize (and contextualize) data in flight

v


Genomics

Internal, M&A, External,

Syndicated

Wearable Devices

High

Variety, Volume, & Velocity

Data

Analysis

Organization

Ingestion

Automated

Data Wrangling & Deep Data Science

Business AwareData Analytics Services Oriented

Architecture

Harmonization

Integration

Analysis

Organized Storage

Provisioning

Aggregation

Modern Technologies

Configurable

Analytic

Applications

Business

Outcomes

Approach

v


v

Mid-size Pharma Case StudyClinical Digital Integration Hub

Operational

Cost Savings

Better Trial

Outcomes

Improve Risk

Management

v


v

Case Study:Simplicity in Work-flow Management

v


v

Case Study:Analytics on the Fly…

v


v

Clinical Data-as-a-Service (CDaaS)

Enabled AnalyticsPatient & Studies

Analytics

Clinical Study Data

Mart

Clinical Outcomes

Analytics

Drug Safety & Analytics

Safety Outcome &

Reporting Analytics

Trial Management

Analytics

Real World Signal

Detection Analytics

Activity Enablement

Clinical Data

Safety Data

Varied Sources

Syndicated & Large Data

Data Sources Electronic Data Capture

Clinical Trials Management

System

Safety Data Warehouse

Global Safety Data

Warehouse

ARGUS

Clinical Study Reports

Disparate Business Unit Reports

External analyses

Non-Clinical, Pre-Clinical Data &

Reports

Real World Claims Data

Internal Genomics Data

Public Data (Kegg, NCBI, CHEMBL, etc.)

Trials Trove, CT.gov

v


v Unstructur

ed 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 DataWeb Service

RESTful

Unstructured Data

SecurityAuthentication

LDAP

Kerberos

SAML

Authorization

RBACACL(Service Level, Directory

Level, Column Level)

EncryptionSSL

Transparent

Encryption

Encryption Zones

AES 256

Unstructured Data

Data Governance

(Hortonworks Stack)

(Cloudera Stack)

(CDaaS)

v


v

Summary of discussions with Life Sciences leaders...Disruptive thinking is required NOW

Clean

Longitudinal

Data

Bench

to

Bedside

Patient

Centricity

Cognitive

Systems

‘Silicon Valley’

Mind Set

Operational Efficiencies, Trial Effectiveness &

Streamline submission pathways

v


v

Speed AND specificity for disruptive business outcomes


Understands all

your dataBuilt to analyze and

operationalize exabytes

of complex dirty data

streams at new speeds

Starts with what

you haveIncorporate existing

data, analytics and IT,

regardless of analytics

maturity

Continuous, Rapid, Massive Enterprise-class Learning

Combines data in

new waysApply relevant combos of

any data sources, shape

the right data

In-the-moment

businessRapid iteration, apply

instant analysis

anticipate the always

unfolding, success

measured in moments

Continuous impactContinual outcomes, know

when the world has

changed, always stay one

step ahead

saama - centralizing data to address imperatives in ... · pdf fileoracle siebel ctms edc flat...

Documents