Gary D. Kennedy

Founder & CEO

“Big Data” Spells Big Problems

R&D Informatics Track

Molecular Med TRI-CON

February 21, 2012

“Big Data” Spells Big Problems

The data

problems we

have been

struggling with

for years are

only the tip of

the iceberg…

Life Science Progress Is Impeded By Data Problems

• Too many disparate data sources

• Lack or surfeit of data standards

• Multi-site collaboration is essential for

breakthroughs

• Increasingly complex regulatory

landscape

• Inconsistent terminology standards

Why Haven’t We Solved the Data Problem?

• Inherent conflicts in the existing ecosystem

• Step forward causes steps backward in other areas

• Moribund architectures exacerbate the problem

• Disruptive business models have not kicked in

• We have always done it that way

• This is not any easy problem to solve

Start from Common Ground

• Everyone would like to solve the

data problem

• Pattern recognition is essential

for all stakeholders

• Enlightened insight into the

future is desirable

• Competition sharpens all swords

• Status quo is untenable

Registry-Centric Architecture:Preparing For Big Data

Claims Data

Reports/Dashboards

Biospecimen Data

Clinical Trials Management

Quality Improvement

Paper Records

Disease

Associations

National Registries

‘omics Data

Cohort Selection

University Partners

small molecule

research database

Imaging

EDC From Patients

in Trials

Patient Reported

Data

EMR

Principal Investigators

Query &

Reporting

Common

Repository

Master

Ontology

Regulators

A Rising Tide Lifts All Ships…

• Everyone benefits from a single 360° perspective of

all patients, subjects, procedures, or disease

• Early pattern recognition enables competitive

advantage

• Phenotypic data is fully integrated with genotypic

data

• Single, definitive source of truth

• Quality and adverse events see the same data as

reimbursement and clinical affairs

• When all data is presented together everybody wins

“Big Data” Requires “Big Changes”

1. Flexible yet structured data model

2. All tools and applications under the

control of the Master Ontology

3. Modern, browser-based platform

that is fully configurable

4. Pattern recognition through a 360°

view of patients or subjects

5. Registries are automatically linked for cross-disease research

“mind-forged manacles”

- William Blake

Flexible yet Structured Data Model

Level of Flexibility

Str

uctu

red O

nto

logy

EHR

Build From

Scratch Spreadsheets

Low High

Hig

h

EDC

CTMS

RDMSSweetSpot

Tools Are Open and Controlled by the Ontology

Tools Utilize Harmonized Data

Bre

adth

of

the T

ool

Set

Narr

ow

Yes

Wid

e

Open Source

Tools

Build From

Scratch Public

Ontologies

EDC

Tools

EDC

Tools

No

CompetitiveAdvantage

Same Thing Only Different

Personalized To An Organization

Siz

e o

f th

e U

ser

Base

General Specific

Larg

e

EHR

Build From

Scratch

AML

Registry

Oncology

Registry

CTMS

Holy

Grail

Sm

all

Patient Specific Or Population Oriented

Population Centric

360

Vie

w o

f Each P

ati

ent

No Yes

Yes

Patient

Registry

Build From

Scratch Quality

Registry

Outcomes

Registry

Clinical

Registry

Best of BothWorlds

Stand-Alone or Linked for Cross Disease Research

All Registries Linked

Each R

egis

try S

tands

Alo

ne

No Yes

Hig

h

Diabetes

Registry

Build From

Scratch

Excel

Auto-

Immune

National

Registry

PatternRecognition

“Big Data” Spells Big Opportunity• Produces a competitive advantage for those who get

it….and act

• Implement “Little Data” right with infinite flexibility and scalability and you are there

• The first to recognize patterns wins

• Paradigm shifts present disruptive opportunities

• Predictive informatics

better directs your limited

research investment

• Going to collect this data

anyway…might as well

use it

gary@remedyi.com

Happy to take questions via email as well.

Questions?