October 2008

“BIG Health: A 21st Century Biomedical System

demonstrating Personalized Medicine

Ken Buetow

National Cancer Institute

Outline

I. Overview

II. caBIG™

III. BIG-Health™

V. Opportunities

• Goals

• Participants

• Activities

• Challenges

Personalized Medicine:

What We’re Trying to Achieve

• Predictive, Preemptive,

Participatory……

• Unifies clinical research, clinical

care, and discovery (bench-

bedside-bed) into a seamless

continuum

• Results in improved clinical

outcomes

• Accelerates the time from

discovery to patient benefit

• Enables a health care system,

not a disparate “sector”

• Empowers consumers in

managing their health over a

lifetime

“The world we have created today has

problems which cannot be solved by

thinking the way we thought when we

created them.”

- Albert Einstein

Challenges:

The Biomedical Landscape

• Isolated information “islands”

• Information dissemination

uses models recognizable to

Gutenberg

• Pioneered by

London Academy of Science

in the 17th century

• Write manuscripts

• “Publish”

• Exchange information at

meetings

New Model: Link

Discovery > Clinical Research > Clinical Care

The Concept: Connect scientific discovery, clinical research and

clinical care into a seamless continuum that continually builds and

applies knowledge

Pediatric cancer is a successful example of this approach

• Faster, more efficient patient

recruitment for trials

• Improved clinical trials

outcomes due to improved

patient selection

• Faster adoption by the health

care delivery system

• Reduced infrastructure costs

The opportunity for health

care providers:The opportunity for research:

• A pathway to innovation

• A chance for physicians

outside academic medical

centers to participate in clinical

research

• Additional resource source

• A strategy to address clinical

care challenges to improve

outcomes

New Model: Link

Discovery > Clinical Research > Clinical Care

Tremendous improvement in childhood cancer survival since 1975

• Overall reduction of cancer mortality by 50%

• acute lymphoblastic leukemia survival rate has improved from 5% in the 1960’s to more than 85%

• Molecular characterization used to determine treatment

Childhood cancer is treated in a context that blends care delivery and clinical research

• Researchers and practitioners are able to correlate experimental laboratory data with clinical data (treatment, history, pathology, outcome, etc.)

• Clinical data are utilized to continuously evaluate outcomes

• Researchers develop and refine evidence-based strategies at an individualized level

• Care providers improve quality by adherence to care standards

Information flow is critical…

this model cannot be achieved without IT connectivity

Outline: Background on caBIG™

I. Overview

III. BIG-Health™

V. Opportunities

• Goals

• Participants

• Activities

• Challenges

II. caBIG™

The caBIG® Initiative

caBIG® is an a virtual web of interconnected data, individuals, and organizations that redefines how research is conducted, care is provided, and patients/participants interact with the biomedical research enterprise.

caBIG® Vision

• Connect the cancer research community through a shareable,

interoperable infrastructure

• Deploy and extend standard rules and a common language to more

easily share information

• Build or adapt tools for collecting, analyzing, integrating and

disseminating information associated with cancer research and care

NCI is utilizing information technology to

join islands into a community

Current silos are disconnected and can’t communicate

Current Healthcare Infrastructure

Healthcare

Delivery /

Patient Care

Clinical

Research

Environment

Regulatory

Reporting

Environment

Next Generation Infrastructure

HL7v2.x

BRIDG

(HL7 v3)

BRIDG

(HL7v3,

CDISC)

Healthcare

Delivery /

Patient Care

Clinical

Research

Environment

Regulatory

Reporting

Environment

Standards allow information to be exchanged

Next Generation Infrastructure

HL7v2.x

BRIDG

(HL7 v3)

BRIDG

(HL7v3,

CDISC)

Healthcare

Delivery /

Patient Care

Clinical

Research

Environment

Regulatory

Reporting

Environment

caXchange

Patient Registration & Enrollment

Capture of Clinical Lab Data

Scheduling of Treatment

Capture of Adverse Effects

Research

Data

Warehouse

Investigator

Registry,

Results

(Janus)

Outcomes

Warehouse

caMATCH

PHR

Tools provide functionality to

enable a seamless continuum.

caBIG®: an

open SOA

with

shared

community

semantics

caGrid Production Environment

Policy: Analysis Framework

ALL of the following:

- no IP value

- low sensitivity data

- no IRB restrictions

- no sponsor restrictions

ANY of the following:

- moderate IP value

- moderate sensitivity data (e.g., LDS)

- limited institutional or IRB policy

restrictions

- moderate sponsor restrictions

ANY of the following:

- high IP value

- high sensitivity data (e.g., PHI)

- significant IRB/consent restrictions

- major sponsor restrictions

“EZ Pass” - General Website Terms of

Use

Standardized Click-Through

Terms and ConditionsBi-Lateral or Multi-Lateral MTA

Data/Specimens

Data Sensitivity(Regulatory Status)

IP Value(Need for Protection)

IRB/ Institutional Restrictions(Policy/ Consent Limitations) Sponsor Restrictions

(Contract Terms & Conditions)

High

Medium

None/Low

Identifiable Data

Coded/Limited Data

Set

De-Identified/

Anonymized Data Set

Explicit Consent

Limitations or

Restrictions

Policy Limitations

Generic Registry or

caGRID Permission

Classified Research/

Major Restrictions

Delays or Other

Moderate Restrictions

No Restrictions

Examples: is the data subject to a restrictive

license? Is it related to an invention report

you have or intend to file with your institution?

Do federal or state law or your institution's

policies prohibit or restrict disclosure?

Do your Institution's or IRB's policies or the applicable

informed consent documents explicitly or implicitly

restrict or permit disclosure (e.g., “no commercial use”)?

Do terms and conditions in any sponsored

agreements prohibit or restrict disclosure

outside institution or to caGRID?

Decision Tree for Privacy/Intellectual Capital Terms and Conditions

caGrid 1.1 Conceptual View

caBIG® Snapshot

Connected with caBIG®

• caBIG® adoption is unfolding in:

• 56 NCI-designated Cancer Centers

• 16 NCI Community Cancer Centers

• caBIG® being integrated into federal health architecture to connect National Health Information Network

• Global Expansion• United Kingdom

• China

• India

• Latin America

NCI-Designated Cancer Centers,

Community Cancer Centers, and

Community Oncology Programs

Local Authenticator

NCI GTS

Local Authenticator

Local Authenticator

Local Authenticator

NCI Dorian

Local Credential

Local Credential

Local Credential

Local Credential

Grid CredentialsGrid Grouper

NodeNode Node Node

NCI caDSR Service NCI Index Service NCI GME

caGrid

Local Authenticator

NCRI GTS

Local Authenticator

Local Authenticator

Local Authenticator

NCRI Dorian

Local Credential

Local Credential

Local Credential

Local Credential

Grid CredentialsGrid Grouper

NodeNode Node Node

NCRI caDSR Service NCRI Index Service NCRI GME

NCRI ONIX

Local Authenticator

NIH GTS

Local Authenticator

Local Authenticator

Local Authenticator

NIH Dorian

Local Credential

Local Credential

Local Credential

Local Credential

Grid CredentialsNIH Grid Grouper

NodeNode Node Node

NIH caDSR Service NIH Index Service NIH GME

NHLBI Grid (CVRG)

Bilateral

Negotiations

Grid of Grids…

Outline: BIG-Health™

I. Overview

II. caBIG™

V. Opportunities

III. BIG-Health™• Goals

• Participants

• Activities

• Challenges

BIG Health Consortium™

Mission:

The BIG Health Consortium™ is a collaboration among stakeholders in biomedicine,

including government, academe, industry, non-profit, and consumers, who come

together in a novel organizational framework to demonstrate the feasibility and

benefits of the personalized medicine paradigm.

Strategy:

Through a series of personalized medicine demonstration projects, with an

expanding number of collaborators, BIG Health will bootstrap a new approach in

which clinical care, clinical research, and scientific discovery are linked.

Vision:

A biomedical system that synergizes the capabilities of the entire community

to realize the promise of personalized medicine

Government

Personalized Medicine Requires Participation

of Multiple Members of a Complex Ecosystem

Researchers

Clinical Communities

Discovery ScienceInformation Technology

Payers

CareDeliverers

Consumers

Foundations

Payers / Insurance

Companies

GenomicistsProteomicists

Systems Biologists

Research Infrastructure

Electronic Health Records

Research

Participants

Patients join research networks, grant consent, agree to be “sought” and to enroll – “on-demand” participants

Biospecimen Collections

Researchers can access and query large collections of well-characterized, clinically annotated specimens

Discovery of Correlations

Biomarkers are identified and validated; disease sub-groups emerge

Individualization of Treatment

Patients are identified by sub-groups and treated appropriately

Clinical

Practice

Electronic Health RecordsEHRs can connect to clinical trials and

hospital settings

Outcomes InformationLarge-scale databases of outcomes can be queried

Patient ParticipationPatients can access clinical trials, educational

materials, etc.

Consumer

My Genomic ProfileConsumers get their genetic and predisposition

risk information

My Prevention StrategiesConsumers work with genetic counselors;

coordinate with health care provider

My Clinical RecordConsumers link to their clinical histories with genetic

profiles; access clinical research; participate in

volunteer networks

Standards

Interoperability

Data Sharing

Connectivity

Outline: Process for Implementation

I. Overview

II. caBIG™

III. BIG-Health™

• Goals

• Participants

• Activities

• Challenges

V. Opportunities

BIG-Health™ Consortium Next Steps

• Convened Roundtable Workshop on September 10

• Determining organizational and communications structure

• Developing Pilot Projects working plans, timelines, etc.

BIG Approaches to Big Challenges

in Personalized Medicine

Possibilities for Demonstration Projects

• Virtual clinical research

• On-demand clinical trial

• 21st century cohort study

• Molecularly-based comparative effectiveness

• Learning health care system

• Monitor outcomes

• Monitor incidence

• Post marketing surveillance

• Rapidly disseminate

http://caBIG.cancer.gov

http://caBIG.nci.nih.gov

http://BIGHealthConsortium.orgOctober 2008