Health Level 7 (HL7): A Brief Overview of a 23-Year Trajectory

W3C Semantic Web Health Care and Life Sciences SIG

Charlie Mead MD, MSc Chief Technology Officer, National Cancer Institute (NCI) Center for Biomedical Informatics and Information Technology (CBIIT) Chair, HL7 Architecture Board (ArB)

Overview

Time flies like an arrow.

Fruit flies like a banana.

•

HL7 Origins: Mission and Version 1.0

•

HL7 Adoption: Version 2.x

•

HL7 Maturation and Expansion: Version 3.0

•

HL7 Evolution: Reorganization & Collaboration, Domain Analysis Models, Service-Awareness, and Enterprise Architecture

•

HL7 Interoperability Contexts: the Translational Continuum

• Pharma’s “next” business model:

intersection with healthcare

• National Cancer Institute:

from caBIG ™ to BIG-Health™

HL7 Origins:

Version 1.0

1985-89: A (relatively) Simple Problem

•

Collection of seven enterprise-related hospitals with ~30 ADT systems needed to exchange data

•

RS-232-like byte-stream approach

• •

Defined delimiters in message headers , fields/sub-fields, etc.

Syntactic interoperability with agreed-upon semantics in a restricted/closed domain-of-application

•

An engineering solution that worked!

• Message paradigm (ACK/NACK) • “the only game in town” •

Version 1.0 (< 10 messages) released circa 1987, 1.1 circa 1989

•

Organization name chosen (Health Level 7) based on OSI stack

•

Solid adoption trajectory in US hospital community



expansion of message repertoire beyond ADT interest in

HL7 Adoption:

Version 2.x

1989-95: The Problem Gets Bigger

•

Increasing adoption drives interest in non-ADT domains

• Financial/Patient Accounting • • Orders management (e.g. labs, etc.) (gradually) Clinical Care (e.g. order sets, care plans, etc.) • • • • •

Organizational structure of HL7 established

• Technical Committees/SIGs (domain-specific) • Bottom up message development based on “business triggers driving information exchange” Optionality allowed in all messages Complex relationships were hand-tooled on a per-message basis Z-segments (the equivalent of free text in an RDBMS) allowed Cross TC sharing of semantics based on “good citizenship/awareness” •

Content expands

• Version 2.0, 2.1, and 2.2 released between 1989-92 • Version 2.3, 2.4, and 2.5 relesed between 1992-95

1989-95: The Problem Gets Bigger

•

Adoption increases

• 75% (1992)/95% (1995) of US hospitals utilized HL7 2.x in two or more systems • Initial interest from non-US entities • • • • Canada Australia Europe UK NHS •

HL7 becomes an ANSI SDO to facilitate interaction with ISO

•

Slowly but surely, HL7 was becoming a victim of its own success

•

“If you’ve seen one HL7 implementation, you’ve seen one HL7 implementation.”

•

“HL7 isn’t a standard, it’s a style guide.”

HL7 Maturation and Expansion:

Version 3.0

1995-2006: Success Drives New Approaches

•

HL7 BoD decided to embark on a new message-development strategy

• Adoption of emerging UML as a standard modeling language • • Adoption of a more formal abstract data type specification as underpinning for computable semantic interoperability Decision to “stay out of the terminology business” and concentrate on the structures that bind terminologies, i.e….

• Development of a common Reference Information Model that would provide the “universe of semantics” for all HL7 domains-of-interest and could be used to develop all HL7 message structures •

Emergence of a commitment to “more than just messaging in the HL7 2.x sense”

• Computable semantic interoperability • Other related standards • • • Arden Syntax CCOW Clinical Document Architecture (CDA)

Health Level Seven (HL7)

•

“HL7 develops specifications that enable the semantically interoperable exchange of healthcare data. ‘Data’ refers to any subject, patient, or population data required to facilitate the management or integration of any aspect healthcare including the management, delivery, evaluation of and reimbursement for healthcare services, as well as data necessary to conduct or support healthcare-related research. HL7 Specifications are created to enable the semantically interoperable interchange of data between healthcare information systems across the entire healthcare continuum.”

--

(Mead paraphrase of HL7 Mission Statement)

•

Conceptually congruent with W3C Semantic Web HCLS SIG Mission Statement

The Four Pillars of Computable Semantic Interoperability

Necessary but not Sufficient

•

#1 - Common model (or harmonized sibling models) across all domains-of-interest

• Information model vs Data model • • The semantics of common structures – Domain Analysis Model Discovered (in part) through analysis of business processes •

#2- Model bound to robust data type specification

• • HL7 V3 Abstract Data Type Specification (R2) ISO DT Specification

The Four Pillars of Computable Semantic Interoperability

Necessary but not Sufficient

•

#3 - Methodology for binding terms from concept-based terminologies

• Domain-specific semantics •

#4 - A formally defined process for defining specific structures to be exchanged between machines, i.e. a “data exchange standard”

• Static structures (as defined via Pillars 1-3) bound to explicit data/information exchange constructs • As of Version 3.0, these constructs were still defined as “messages” in the traditional HL7 sense • Documents (content RIM-derived) could also be defined by HL7 TCs and be exchanged within or without accompanying message constructs

A single CSI statement is made by binding common, cross-domain structures to domain-specific terminologies (semantics).

HL7 V3 Reference Information Model (RIM)

“An instance of an Entity may play zero or more Roles. Each instance of a Role may, in turn, play zero or more instances of a Participation in the context of an instance of an Act. Each instance of a Participation participates in a one and only one Act for the ‘duration’ of that Act. Acts may be related to each other through instances of Act Relationship.”

Entity • Organization • Place • Person • Living Subject • Material 1 • Has component • Is supported by Act Relationship 0..* 0..* 1 1 0..* 0..* Role 1 • Patient • Member • Healthcare facility • Practitioner • Practitioner assignment • Specimen • Location Participation • Author • Reviewer • Verifier • Subject • Target • Tracker 1..* 1 Act • Referral • Transportation • Supply • Procedure • Consent • Observation • Medication • Administrative act • Financial act

Collection, Context, and Attribution

Building Complex RIM-based structures

•

A diagnosis of pneumonia (observation Act) related to three other observations Acts. Each Act is fully attributed with its own context of Entity-Role-Participation values.

AR: “is supported by”

has target

OBS: Temp 101F Attribution PARTICIPAT: Subject PARTICIPAT: Author OBS: Dx Pneumonia

is source for

AR: “is supported by”

has target

OBS: Abnormal CXR Attribution ROLE: Clinician ROLE: Patient AR: “is supported by”

has target

OBS: Elevated WBC ENTITY: Person Attribution Attribution

Shakespeare in RIM-speak

(courtesy of David Markwell) All the

world’s

a

stage

And all

men

and

women

are merely

players

One

man,

in his time, has many

parts,

First the

infant,

subject subject

Mewling

and

puking

responsible party direct target In the

nurse’s

arms

.

Information vs Terminology Models:

Intersecting and interleaving semantic structures

Information Model Common Structures for Shared Semantics Binding/Interface Terminology Model Domain-Specific Terms specifying Domain-Specific Semantics Information Model Common Structures bound to Domain-Specific Structures specifying Domain-Specific Semantics Terminology Model Domain-Specific Terms specifying Domain-Specific Semantics Example: Appropriately constructed semantic web structures should be able to distinguish between “Grade IV allergic rxn to Penicillin” represented in several ways using various combinations of RIM and SNOMED-CT codes.

HL7’s Clinical Document Architecture (CDA)

•

Emerged coincident with development of XML

•

Driven by “document-centricity” of much of healthcare practice

• Emphasized the importance of transmitting both text and structured data • Identified “fundamental document characteristics” • • • • • • Persistence Authentication Stewardship Wholeness Global/Local Context Human Readability • Nicholas Negroponte’s “bits and atoms” paradigm is particularly relevent •

Release 1 (circa 2001) was only partially RIM-derived

• Rapid uptake/adoption in European community (less in US) •

Release 2 (circa 2006) entirely RIM-derived

• Adopted by HITSP (Coordination of Care Document (CCD)

Incremental Computable Semantic Interoperability

Highly “Informational” Systems * 1001 0100 0100 1011 1110 0101 * Less “Informational” Systems

*HL7 Clinical Document Architecture: Single standard for computer processable and computer manageable data (Wes Rishel, Gartner Group)

1001 0100 0100 1011 1110 0101

HL7 TCs and the Life Sciences

•

Focus up until circa 1997 was “clinical care”

•

However, international interest in HL7 led to new domains-of-interest and new TCs

• Regulated Clinical Research Information Management (RCRIM) • Pharma (CDISC), FDA • • Clinical Genomics Imaging • • • Medical Devices Security Etc.

•

Of particular interest to W3C SW HCLS SIG is the RCRIM TC

RCRIM TC

• • • • • •

The RCRIM TC … defines standards to improve or enhance information

management during research and regulatory evaluation of the safety and efficacy of therapeutic products or procedures worldwide. The committee defines messages, document structures, and terminologies to support the interoperability of systems and processes used in the collection, storage, distribution, integration and analysis of ‘clinical trial’ information. Specific areas of interest include:

Structured Protocols Product Stability and Labeling Clinical Trial Reporting AEs (with CDC) SDTM (with CDISC)

A caBIG™ Example

(from Covitz et al, Bioinformatics, V19, N18, P2404)

• • • • • •

Patient has headache, focal weakness, history of seizures Workup reveals glioblastoma multiforma, subtype astrocytoma Is this tumor histology associated with gene expression abnormalities?

•

Yes, in the p53 signaling pathway including BCL2, TIMP3, GADD45A, CCND1 Is there documented evidence of aberrant expression of (e.g. CCND1)?

•

Yes, SAGE tags for cyclin D1 appear with 3x greater frequency in cancerous vs normal brain tissue Are any gene products of the p53 signaling pathway known targets for therapeutic agents?

•

Yes, TP53, RB1, BCL2, CDK4, MDM2, CCNE1 Are any of the agents known to target these genes being specifically tested in glioblastoma patients?

•

Yes, trials xxx and yyy are currently underway

HL7 Evolution:

Reorganization & Collaboration, Domain Analysis Models, Service-Awareness, and Enterprise Architecture

Reorganization

•

As HL7 has grew to have ~3000 members, 40+ TCs and SIGs, ~30 International Affiliates, and as several countries implementing (or attempting to implement) its standards, it became increasingly obvious that -- like a growing company -- it needed to reassess its organizational structure and processes.

• • • • •

Multi-dimensional effort began in 2005 and continuuing to present

• Reconstituted BoD • • CEO CTO Technical Steering Committee Architecture Board Decreased number of TCs/SIGs Emphasis on project management and common development methodology • Use of ANSI DSTU to encourage testing before final ballot

Collaboration

•

HL7 actively seeks collaboration with other organizations developing standards for healthcare, life sciences, clinical research internationally

• • Goal is to avoid redundant efforts Examples include • • • • ISO CEN OMG CDISC •

CDISC (Clinical Data Interchange Standards Consortium)

• Established circa 2002 • • • Virtually entire pharma industry is represented Prototype collaboration relationship for HL7 via RCRIM TC Leading developer of a DAM for domain of “Protocol-driven research and associated regulatory artifacts”  the BRIDG Model

Domain Analysis Models:

The Communication Pyramid

Standardized Models (UML) --

DAM

Non-standard Graphics ad hoc Drawings Structured Documents Free-text Documents

`

Discussions

Communication

BRIDG circa 2004:

Separating Analysis from Design/Implementation

Problem-Space Model

(a la HL7 Development Framework)

ODM RIM / DMIM RMIM / HMD / XSD

Lessons Learned:

Using a DAM

•

DAMs need to be applied in the context of a larger development (message, service, application) management process

•

DAMs should be both domain-friendly and semantically robust (technology useful)

•

In order to be truly effective, standards development needs to become less like the Waterfall and more ‘Agile,’ i.e. embedded in an interactive, iterative, incremental process.

• Exemplar process has been successfully piloted at NCI and is now ready for application to all projects •

A DAM that is ultimately used in message, application, or service development needs to address

• Data Type bindings • Terminology bindings for coded data types

Lessons Learned:

Working with BRIDG (1)

•

BRIDG only makes sense ‘in context’

• e.g. message development, application development, service specification, etc.

• • Analysis Paralysis occurs otherwise Most effective use is in the context of an iterative/incremental development process (e.g. RUP, SCRUM, Agile, ect.) • NCI has integrated use of the BRIDG Model (and the use of analysis models in general) into its development practices • HL7 RCRIM appears to be ready to do the same •

The BRIDG domain-of-interest is stable after 4+ years of use

•

Protocol-driven research involving human, animal, or device subjects and associated regulatory artifacts

• • Recently, questions have been raised as to whether the BRIDG domain of-interest should include post-marketing safety/adverse events Initial indications are that the answer is ‘Yes’ and that the effect on the model’s structure will be minimal

Lessons Learned: Working with BRIDG (2)

•

Teams need to start with the existing BRIDG Model

• • Subset as needed based on project focus Add new semantics (e.g. classes, attributes, relationships, business rules, etc.) as needed • All new editions must be rigorously defined • Identify existing elements in the BRIDG model which are incorrect, unclear, too restrictive, etc.

BRIDG circa 2008:

Separating Analysis from Design/Implementation

Requirements Analysis Messages Messages Messages Services Applications

Implementation-dependencies

Design Services

Technology/platform bindings

Implementation Applications Services Applications

The Current BRIDG Model

Understandable to Domain Experts Unambiguously mappable to HL7 RIM Varying levels of abstraction, explicitness, and ‘RIM-compliance’

The Revised, 2-layered (2-views) BRIDG Model

Understandable to Domain Experts (DaM) Consistent levels of abstraction and explicitness in multiple sub Sub-Domain 1 Sub-Domain 2 domain ‘Requirements Models’ Sub-Domain 3 Sub-Domain 4 Sub-Domain 5 Unambiguously mappable to HL7 RIM (DAM) Consistent levels of RIM-compliance and explicitness in a single ‘Analysis Model’

NOTE: Sub-domains may or may not intersect semantically

DAMs and Ontologies (1)

Domain of Interest described by An OWL-DL definition is worth at least several UML classes Ontologic Representation (OWL-DL) A UML picture is worth a thousand Requirements Documents words Visual Conceptualization (UML DAM)

DAMs and Ontologies (2)

Domain of Interest Is described by / facilitates computational in An OWL-DL definition is worth at least several UML classes A UML picture is worth a thousand Requirements Documents words Visual Conceptualization (UML DAM) Ontologic Representation (OWL-DL)

Service Awareness within HL7

•

Initial work began in 2006 with the development of the Health Services Specification Project (HSSP), a joint effort with OMG

•

By CTO directive, has evolved to a directive to the newly established ArB to develop a “Services-Aware Enterprise Architecture Framework” (SAEAF) for HL7

•

Requirements include

• • • Maximum utilization of existing static artifacts Development of computationally robust behavioral/interaction model Development of a scalable Conformance/Compliance framework

Enterprise Integration Strategies:

Objects vs Messages vs Services

•

Objects

• • • Finely-granulated Difficult to trace to business functionality Encapsulation not a positive when crossing enterprise boundaries •

Messages

• Payloads based on standards support semantic interoperability • Embedding dynamic/behavioral semantics within message causes run time context ambiguity or non-enforceable contract semantics • • Application Roles Receiver Responsibilities •

Services

• • Traceable to business-level requirements Separation of static semantics (message payload) from dynamic semantics (“integration points,” contracts)

Service Awareness within HL7

•

Historically, HL7 as conceptualized the world as “communicating clouds” but has not formally specified the semantics of the interactions that occur

• HSSP began the specification process with its Service Functional Model (basically a services “requirements document”) • SAEAF extends the definitional space

•

HL7, MDA, CSI, SOA, and Distributed Systems Architecture

The intersection of HL7, MDA, Distributed Systems Architecture, SOA, and CSI provide a goal, the artifacts, portions of a methodology, and the framework for defining robust, durable business-oriented constructs that provide extensibility, reuse, and governance.

Health Level 7 Service Oriented Architecture Reference Model For Open Distributed Processing You are here

(

Vous êtes ici

)

Computable Semantic Interoperability Model Driven Architecture

Choreography: an Analysis Perspective

NCI is using CDL as an analysis tool (via pi4soa open-source tool)

SAEAF Behavioral Framework

The HL7 Specification Stack - Overview

RM-ODP Viewpoint

Reference Blueprint Platform Independent Platform-Bound + + / + + + + + + / / + / / / O Typical + Rare Never / Optional O

SAEAF Specification Pattern

Specification Enterprise / Business Viewpoint Information Viewpoint Computational Viewpoint

Analysis Conceptual Design EHR-FM, Clinical Statements RIM, Structured Vocab, ADTs Business Context, Reference Context Business Governance DIM CIM, LIM EHR-FM Dynamic Blueprint, Functional Profile(s) Dynamic Model, Interface Specification

Engineering Viewpoint Conformance Level

Reference N/A N/A Blueprint Platform Independent Implementable Design N/A Transforms, Schema Orchestration, Interface Realization Execution Context, Specification Bindings, Deployment Model Platform Bound

An Exemplar Service:

Clinical Research Filtered Query (CRFQ)

CRFQ client (clinician, caregiver, patient Qualified protocols

List Qualified Protocol

Interface Clinical data set

C R F Q I/E criteria P1 I/E criteria P2 I/E criteria P3 I/E criteria P4

Count Qualified Patients

CRFQ client (trial sponsor, CRO, Pharma) patients Protocol I/E criteria/ Safety criteria

C R F Q Pt data P1 Pt data P2 Pt data P3 Pt data P4

HL7 Interoperability Contexts:

The Translational Continuum

- Pharma’s “next” business model:

intersection with healthcare

-- National Cancer Institute:

from caBIG™ to BIG-Health™

Pharma’s essential challenge

:

Increased R&D expenditures, decreased NCEs to market

Genomics Proteomics Combichem UHTS Phase I

rejection

Phase II

rejection

Phase III

rejection

Approved NCE Today’s R&D Infrastructure

60 35 30 50 40 30 20 10 0

'90 '91 '92 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02 '03 NCEs R&D Expenditure

0 25 20 15 10 5 Source: PhRMA

Ian Ferrier, PhD

The Transformation:

Better early decisions, fewer late stage failures, decreased time-to-market

Increased Early Drug Development Capabilities Genomics Proteomics Combichem UHTS Fewer late stage failures Phase I

rejection

Phase II

rejection

Phase III

rejection

Increased Approved NCEs Decreased Cost Decreased Time in Pipeline

Ian Ferrier, PhD

The Vision is simple, and well understood …it is based on

individualized

data…and appropriate tools…

Clinical data Collection

Pharma-supplied Queries Sophisticated Knowledge Creation Tools Genomics, Proteomics, Chemistry, etc.

Clinical Data Repository

Ian Ferrier, PhD

… ‘Knowledge-creation’



CSI platform

caBIG ™ and BIG-Health™:

Addressing the Infrastructure of the Current World of Biomedicine

• • •

Isolated information “islands” Information dissemination uses models recognizable to Gutenberg Pioneered by British Royal Academy of Science in the 17 th century

• • • Write manuscripts “Publish” Exchange information at meetings

Need to convert islands into an integrated system

The caBIG™ Initiative

caBIG™ Goal A virtual network of interconnected data, individuals, and organizations that whose goal is to redefine 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 electronic infrastructure • •

Deploy and extend Build

standard rules and a common language to more easily share information or adapt tools for collecting, analyzing, integrating and disseminating information associated with cancer research and care

caBIG™ Strategy

•

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

caBIG ™ is utilizing information technology to join islands into a community

Alabama

Birmingham: UAB Comprehensive Cancer Center

Arizona

Phoenix: Translational Genomics Research Institute Tucson: University of Arizona

California

Berkeley: University of California Lawrence Berkeley National Laboratory University of California at Berkeley Los Angeles: AECOM California Institute of Technology University of Southern California Information Sciences Institute University of California at Irvine The Chao Family Comprehensive Cancer Center La Jolla: The Burnham Institute Sacramento: University of California Davis Cancer Center San Diego: SAIC San Francisco: University of California San Francisco Comprehensive Cancer Center

Colorado

Aurora: University of Colorado Cancer Center District of Columbia Department of Veterans Affairs Lombardi Cancer Research Center - Georgetown University Medical Center

Florida

Tampa: H. Lee Moffitt Cancer Center at the University of South Florida

Hawaii

Manoa: Cancer Research Center of Hawaii

Illinois

Argonne: Argonne National Laboratory Chicago: Robert H. Lurie Comprehensive Cancer Center of Northwestern University University of Chicago Cancer Research Center Urbana-Champaign: University of Illinois at Urbana-Champaign Indiana Indianapolis: Indiana University Cancer Center Regenstrief Institute, Inc.

Iowa

Iowa City: Holden Comprehensive Canter Center at the University of Iowa

Louisiana

New Orleans: Tulane University School of Medicine

Maine

Bar Harbor: The Jackson Laboratory

Maryland

Baltimore: The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University Bethesda: Consumer Advocates in Research and Related Activities (CARRA) NCI Cancer Therapy Evaluation Program NCI Center for Bioinformatics NCI Center for Cancer Research NCI Center for Strategic Dissemination NCI Division of Cancer Control and Population Sciences NCI Division of Cancer Epidemiology and Genetics NCI Division of Cancer Prevention NCI Division of Cancer Treatment and Diagnosis Terrapin Systems Rockville: Capital Technology Information Services Emmes Corporation Information Management Services, Inc.

Massachusetts

Cambridge: Akaza Research Massachusetts Institute of Technology Somerville: Panther Informatics

Michigan

Ann Arbor: Internet2 University of Michigan Comprehensive Cancer Center Detroit: Meyer L. Prentis/Karmanos Comprehensive Cancer Center

Minnesota

Minneapolis: University of Minnesota Cancer Center Rochester: Mayo Clinic Cancer Center

Nebraska

Omaha: University of Nebraska Medical Center/Eppley Cancer Center

New Hampshire

Lebanon: Dartmouth College Dartmouth-Hitchcock Medical Center

New York

Buffalo: Roswell Park Cancer Institute Bronx: Albert Einstein Cancer Center Cold Spring Harbor: Cold Spring Harbor Laboratory New York: Herbert Irving Comprehensive Cancer Center Columbia University Memorial Sloan-Kettering Cancer Center New York University Medical Center White Plains: IBM

North Carolina

Chapel Hill: University of North Carolina Lineberger Comprehensive Cancer Center Raleigh-Durham: Alpha-Gamma Technologies, Inc. Constella Health Sciences Duke Comprehensive Cancer Center

Ohio

Cleveland: Case Comprehensive Cancer Center Columbus: Ohio State University Comprehensive Cancer Center

Oregon

Portland: Oregon Health & Science University

Pennsylvania

Philadelphia: Drexel University Fox Chase Cancer Center Kimmel Cancer Center at Thomas Jefferson University Abramson Cancer Center of the University of Pennsylvania Pittsburgh: University of Pittsburgh Cancer Institute

Tennessee

Memphis: St. Jude’s Children’s Research Hospital

Texas

Austin: 9 Star Research Houston: M.D. Anderson Cancer Center

Virginia

Fairfax: SRA International Reston: Scenpro

Washington

Seattle: DataWorks Development, Inc. Fred Hutchinson Cancer Research Center

International

Paris, France: Sanofi Aventis

caBIG™ Tools and Infrastructure

• caBIG™ adoption is unfolding in: • 56 NCI-designated Cancer Centers • 16 NCI Community Cancer Center Sites •

caBIG™ being integrated into federal health architecture to connect

Nationwide Health Information Network • Global Expansion • • United Kingdom China • • India Latin America

NCI-Designated Cancer Centers, Community Cancer Centers, and Community Oncology Programs

Molecular Medicine:

Pre-emptive, Preventive, Participatory, Personalized

A Bridge Between Research and Care Delivery

Clinical Practice

• Medical centers • Community hospitals • Private practice • Government • • •

Shared HIT

Infrastructure Standards Development

Molecular Medicine

• Molecular Profiling • Family History • Molecular Diagnostics

Practice outcomes Extended participant access E Health Record Clinical Research

• Academic centers • Pharma/CROs • Biotech • Government

Molecular medicine Trials outcomes

caBIG TM is already linking clinical practice to clinical research

The BIG-Health ™ Model…

NCCCP Center - Patient and Physician Personal Genomics Firms Genomic Results ROLE:

Genetic data • Navigenics • 23andMe

Aggregated Data

(via standards)

Pharma Industry Scientific Literature / Research Community Diagnostic Results Diagnostic Labs Sample Sample and medical info Personalized Treatment Clinical Data PHRs EHRs ROLE:

Traditional and molecular testing

Aggregated Data

(via standards)

ROLE:

Data integration • Genomic Health • Genzyme • Monogram • Covance.

• Google • Healthvault

Association Results Research Centers ROLE:

Analysis • Baylor • Duke • Lombardi • UCSF

BIG-Health ™ Value Propositions

NCCCP Center

Unity of research and care

Personal Genomics Firms Personal Genomics Firms:

Broader market; research validation

Genomic Results Diagnostic Results Patient:

Research participation and improved treatments

Physician:

Real time knowledge; improved clinical outcomes

Personalized Treatment Aggregated Data

(via standards)

Clinical Data PHRs EHRs Aggregated Data

(via standards)

Diagnostic Labs Diagnostic Labs:

Broader market

PHR and EHR Providers:

Broader market

Pharma Industry Pharma Industry:

Discovery Engine + Patient Cohorts

Research Centers Association Results Research Centers:

Faster discovery; improved productivity

Scientific Literature / Research Community Scientific Literature / Research Community :

Enhanced Knowledge

Current Ecosystem Participation

Academic

•

Baylor

•

Duke

• •

Georgetown UCSF Diagnostic

•

Genzyme Genetics

•

Genomic Health Platform

•

Affymetrix Pharmaceutical

•

Genentech

•

Novartis IT

•

Microsoft Foundation

•

Gates Foundation

•

FasterCures

• • •

Personalized Medicine Coalition Prostate Cancer Foundation Canyon Ranch Institute Government

•

ONC

•

HHS Personalized Medicine Initiative Payer

•

Kaiser Permanente Venture Capital

•

Kleiner Perkins

•

MDV

• •

Health Evolution Partners 5am Ventures Personal Genomics

•

Navigenics

•

23 and Me

HL7’s Role in these two Contexts

•

Key components

• • • • • RIM Data type specification Terminology binding infrastructure Document architecture Services-Aware Enterprise Architecture Framework •

Adoption of various components by

• • • • Canada Infoway NCI UK NHS DoD/VA •

Collaboration with

• ISO, CEN, CDISC, IHE, HITSP, etc.

Q U E S T I O N S A N S W E R S