Informatics: a scientific basis for Informatics: a scientific basis for use of IT in health care and use of IT in health care and

biomedicinebiomedicine

William W. Stead, M.D.

McKesson Foundation Professor of Biomedical Informatics & Medicine

Associate Vice Chancellor for Strategy/Transformation & CIO

Vanderbilt University Medical Center

April 21, 2008

Disclosures: McKesson’s Horizon Expert Orders is licensed from Vanderbilt. Vanderbilt is a founder of Informatics Corporation of America. I am a Director of Healthstream.

The Science of Informatics

Challenges to Application of IT to Health Care and Biomedicine

Examples of the Science at Work

Assembling a Critical Mass of Talent

Structural Considerations

Road Map to the LectureRoad Map to the Lecture

InformaticsInformaticsDefinition:

Science that deals with information, its structure, acquisition and use

Cornerstones:Techniques to structure, discover, visualize & reason with information contentApproaches to link people, process & technology together as a systemMethods to evaluate systems and their technology componentsProcesses to facilitate change

Techniques to Structure, Discover, Visualize & Reason with Information Content

InformaticsInformatics++

• Data knowledge structures

• Ontologies

& reference terminologies

• Algorithms for matching & machine learning

• Models

Data sourcesData sourcescreated by peoplecreated by people

• Text literature

• Bibliographic + evidence databases

• Patient records

• Environmentaldatabases

Information useable Information useable by people & by people & computerscomputers

==

• Drug x potentiates drug y

• Pt x is on the following drugs

…

…

…

Approaches to Link People, Process & Technology together as a System

• Compassion

• Pattern Recognition

• Judgment

PeoplePeople

• Simplification

• Standardization

ProcessProcess++

•↓

Memory Dependence

•↑Forcing Function

InformaticsInformatics++

• Reproducible Performance

SystemsSystems==

Methods to Evaluate Systems and their Technology Components

Informatics Informatics EvaluationEvaluation++

•• Interface usability and Interface usability and safetysafety

•• Information dose and Information dose and responseresponse

•• Information recall and Information recall and precisionprecision

EvidenceEvidence-- based Designbased Design==

•• What to doWhat to do

•• How to get it How to get it donedone

Health Service Health Service ResearchResearch

•• System effect on System effect on quality and costquality and cost

Processes to Facilitate ChangeProcesses to Facilitate Change

Working in Working in New WaysNew Ways==

People & People & Organizational Organizational

IssuesIssues

•• StrategyStrategy

•• UnderstandingUnderstanding

•• SupportSupport

InformaticsInformatics

•• Scenario planning & use Scenario planning & use casescases

•• Iterative design: concept Iterative design: concept ==> > build => use => refinebuild => use => refine

•• Embedded monitoring & Embedded monitoring & self correctionself correction

++

Informatics is InterInformatics is Inter--disciplinarydisciplinary

Biological SciencesCognitive SciencesComputer ScienceEngineeringHealth SciencesInformation ScienceStatistics

Informatics has SubInformatics has Sub--disciplinesdisciplinesInformation Structure & Information Structure & Semantic RelationshipsSemantic Relationships

Database architectureDatabase architectureTerminology architectureTerminology architectureKnowledge architectureKnowledge architectureSystem architectureSystem architecture

Workflow Fit & EvaluationWorkflow Fit & EvaluationOrganizational developmentOrganizational developmentDesign & requirementsDesign & requirementsHuman factors & safetyHuman factors & safetyUtility & effectiveness Utility & effectiveness assessmentassessment

Algorithms, Models & SystemsAlgorithms, Models & SystemsInformation retrievalInformation retrievalNatural language processingNatural language processingData miningData miningModelingModelingData privacy, deData privacy, de--identification identification and reand re--identificationidentificationMachine learning & discovery systemsMachine learning & discovery systemsScientific visualization/imaging Scientific visualization/imaging informaticsinformaticsDecision models & expert systemsDecision models & expert systems

Application DomainsApplication DomainsHealth Informatics (medical, nursing, Health Informatics (medical, nursing, public health, regional, consumer...public health, regional, consumer...Educational processesEducational processesBiomolecular analysis methods & toolsBiomolecular analysis methods & toolsClinical research data management & Clinical research data management & analysisanalysis

Characteristics of Biological Systems &

Health Care that Challenge Conventional IT

Representative Challenges

Health care is fragmented and chaotic

Clinical measurements rarely have precise meaning

Diagnoses lack clinical detail

Biological systems are highly variable

Fact

s pe

r Dec

isio

n

1000

10

100

5Human

Cognitive Capacity

2000 20101990 2020

Structural Genetics: e.g. SNPs, haplotypes

Functional Genetics: Gene expression

profiles

Proteomics and othereffector molecules

Decisions by Clinical Phenotype

#1 Data Aggregation & Organization

Examples of Informatics Examples of Informatics at Workat Work

Systems (ADT, Lab, Rad…)

Dictation/Transcription

Note Capture Tools

Sources

Image (PACS, EKG…)

Fax/Scan

Text Reports

Externalize Content as Documents

Report Header plus Pointerto Image

Index each Word or Number for Cross-

Patient Queries

Electronic Patient Chart

Assemble “Documents”

by Patient

“Tag”

Clinical Concepts for Decision Support

⎨ StarChartStarChart

Vanderbilt Knowledge Map Vanderbilt Knowledge Map Concept IdentifierConcept Identifier

Uses NLP techniquesAbbreviation and acronym extractionConjunctive expansion

Score-basedDerivational forms (stenosis →stenotic, lungs → pulmonary)Document-based disambiguation

Find all of my student’s notes discussing “breast cancer”

A prior diagnosis of breast cancer

The Vanderbilt DNA Databank Project DesignThe Vanderbilt DNA Databank Project Design

Create de-identified “Synthetic Derivative” of Electronic Medical Records (650 Gb data on 1.4 million individuals)Extract DNA from leftover blood samples that have been de-identified (25K-75K per year)Enable DNA sample retrieval based on clinical queriesProspective genotyping panels

Examples of Informatics Examples of Informatics at Workat Work

#2 Discovery Systems

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

ALL

LAR

SLA

RS-

ENG

A-K

NN

RFE

RFE

-Guy

onR

FE-P

OLY

RFE

-PO

LY-G

uyon

SIM

CA

SIM

CA

-SVM

WFC

CM

-CC

RU

AF-

KW

UA

F-B

WU

AF2

-BW

UA

F-S2

NX H

ITO

N-P

CH

ITO

N-P

C-W

HIT

ON

-MB

HIT

ON

-MB

-WH

ITO

Ngp

-PC

HIT

ON

gp-M

BH

ITO

Ngp

-PC

-WH

ITO

Ngp

-MB

-W

Averaged over datasets

Marker selection method

Per

form

ance

Random markersSelected markers

1. Many genes that belong to different pathways are determining phenotype

2. Our predictive algorithms select the locally causal and

essential (non-redundant) genes

3. This leads to great parsimony

of selected marker panels

4. It also leads to better performance than random marker selection (other methods are barely distinguishable than random)

5. Selected marker genes are localized in pathways responsible for phenotype improved biological significance.

Other methods are not localized and thus uninterpretable.

Advantages of Causal Algorithms for HighAdvantages of Causal Algorithms for High--Throughput DiscoveryThroughput Discovery

A

E

Ph

H

Q

L

M

N

P O

C

D I

J

K

R

S

T Y

1

2

3 4

5

B

B

A

E

T

H Q L

M N

P O

C D

I J

K

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

ALLLARS

LARS-EN

HITON_PC

HITON_PC_W

HITON_MB

HITON_MB_W

HITONgp_PC

HITONgp_MB

HITONgp_PC_W

HITONgp_MB_W

HITONgp_PC_S

HITONgp_MB_S

GA_KNNRFE

RFE_Guyo

nRFE_P

OLY

RFE_POLY_G

uyon

SIMCA

SIMCA_S

VM

WFCCM_CCR

UAF_KW

UAF_BW

UAF2_BW

UAF_S2N

FullyFully--Automated Discovery SoftwareAutomated Discovery Software

Acc

urac

y, %

0

20

40

60

80

100

Multiple specialized classification methods(original primary studies: results by human expert analysis)

GEMS(fully automated)

1.

Data

analysis protocol that is disease & assay specific

e.g. GEMS protocol benchmarked and tailored for microarray gene expression analysis for cancer

diagnosis and outcome prediction

2. Tested software system: e.g., GEMS was tested against published analyses and in cross-dataset experiments

3. Fully-automated use. The software:

Runs many different algorithms and configurations

Selects the best one

Fits the data and reports results

The system avoids overfitting

and gives unbiased estimation of model performance

GEMS

NormalCancerCancerNormalNormalCancer…CancerCancerNormal

Gene expression data andoutcome variable

ring finger protein 1tubulin, beta, 5glucose-6-phosphate dehydrogenaseglutathione S-transferase M5carnitine acetyltransferaseRho GTPase activating protein 4SMA3mannose phosphate isomerasemitogen-activated protein kinase 3leukotriene A4 hydrolasechromosome 21 open reading frame 1dihydropyrimidinase-like 2beta-2-microglobulindiscs, large (Drosophila) homolog 4

Optional: Gene names & IDs

Cross-validationperformance

estimate

Classificationmodel

Rho GTPase activating protein 4SMA3mannose phosphate isomerasemitogen-activated protein kinase 3

Reduced set of genes

Links to literature

Cross-validationperformance

estimate

Classificationmodel

Rho GTPase activating protein 4SMA3mannose phosphate isomerasemitogen-activated protein kinase 3

Reduced set of genes

Links to literature

(model generation & performance estimation mode)(model generation & performance estimation mode)

Random markers*Marker selection Bhattacharjee Bhattacharjee Bhattacharjee

Classifier training Beer Beer Bhattacharjee

Classifier testing Beer Beer BeerHITONgp_MB 98.71% 100.00% 93.79% 100.00%HITONgp_PC_W 98.47% 98.89% 64.79% 99.88%HITONgp_MB_W 98.47% 98.89% 58.36% 99.88%HITONgp_PC_S 99.53% 100.00% 79.43% 100.00%HITONgp_MB_S 99.71% 100.00% 90.32% 100.00%

Dataset ReferenceBhattacharjee Bhattacharjee, 2001

Beer Beer, 2002

Bhattacharjee

Diagnostic taskLung cancer vs normalsLung cancer vs normals

Dataset

Outer loop: Cross-validation for performance estimation

Inner Loop: Cross-validation for model selectionTraining

setValidation

setC Accuracy Average

AccuracyP1 P2 86%P2 P1 84%P1 P2 70%P2 P1 90%

1 85%

2 80%

Training set

Testing set

C Accuracy Average Accuracy

P1, P2 P3 1 89%P1,P3 P2 2 84%P2, P3 P1 1 76%

83%…

predictor variables outcome variable

P1

P2

P3

Choose C=1 since it maximizes accuracy

…

9_Tum

ors

14_Tu

mors

Brain_

Tumor2

Brai

n_Tu

mor1

11_Tu

morsLe

ukem

ia1

Leuk

emia2

Lu

ng_C

ancer

SRBCT

Prostat

e_Tum

or DLBCL

Examples of Informatics Examples of Informatics at Workat Work

#3 Assuring Quality

Assurance of Quality

Outcomes

Voluntary ReportingVoluntary Reporting

Algorithmic surveillance for potential adverse events

Need trend detection

or escalationmechanism

Algorithmicfalse positive or under-reporting

New rule needed No actionNo

Yes No

Yes

Assembling a Critical Assembling a Critical Mass of Biomedical Mass of Biomedical Informatics TalentInformatics Talent

0

10

20

30

40

50

60

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Faculty19

91

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Disciplines

NursingLibrary

Computer

Science Org Behavior

EducationBioinformatics

Biomedical Engineering

Medicine

Center for Better Health

BMI Training Grant

Division of BMI

Informatics Center

Structures Center for BMI Dept of BMI

McKesson Partnership

Masters & PhD Programs

Launch and Growth Launch and Growth of the Department of of the Department of

Biomedical Biomedical InformaticsInformatics

Mod

elin

g

Data

base

Desi

gnSy

stem

Arch

itect

ures

Voca

bula

ries

Data

Min

ing

Expe

rtSy

stem

sKn

owle

dge

Arch

itect

ures

Disc

over

y M

etho

ds&

Syst

ems

Scie

ntifi

c Vi

sual

izat

ion

Hum

an

Fact

ors

Educ

atio

nal

Proc

esse

sEv

alua

tion

Org

aniz

atio

nal

Deve

lopm

ent/

Man

agem

ent

AlphanumericClinicalSystems

Clinical Imaging

MolecularBiology &Genetics

Integrative (Systems) Biology

Molecular Cell and Tissue

Imaging

Relative Importance of Competencies in Selected Relative Importance of Competencies in Selected Application Areas (2004 DBMI Plan)Application Areas (2004 DBMI Plan)

Informatics Career RolesInformatics Career Roles

Basic informatics

research

Use of tools in research &

practice

Tool development & evaluation

Organization Structure

Informatics Education Informatics Education & Training& Training

Operation & Decision Operation & Decision Support SystemsSupport Systems

Informatics Research & Informatics Research & DevelopmentDevelopment

Operatio

nal Exp

erien

ce

Operatio

nal Exp

erien

ceReal World Problems,

Real World Problems,

Delivery Vehicle & Funding

Delivery Vehicle & Funding

Techniques & Technologies

Techniques & TechnologiesAdministrat

ors an

d Staff

Administrat

ors an

d Staff

InvestigatorsInvestigatorsLaboratory ExperienceLaboratory Experience

Relationship of Academics to OperationsRelationship of Academics to Operations

,, , ,

Steven Steven GabbeGabbeDean School of Dean School of

MedicineMedicine

John John DoulisDoulisAsst VC & Chief Asst VC & Chief

Operations Operations OfficerOfficer

NunziaNunzia

GiuseGiuseDirectorDirector

EskindEskind

LibraryLibrary

Bill SteadBill SteadChairman,Chairman,

Vanderbilt CenterVanderbilt Centerfor Better Healthfor Better Health

Carol AronsonCarol AronsonAdministrative Officer Administrative Officer

IC IC

Donna ForsytheDonna ForsytheIC Administration & IC Administration &

FinanceFinance

Bill SteadBill SteadAssociate VC Health AffairsAssociate VC Health AffairsDirector, Informatics CenterDirector, Informatics Center

CIOCIO

Harry JacobsonHarry JacobsonVice ChancellorVice ChancellorHealth AffairsHealth Affairs

Nick Nick ZepposZepposChancellorChancellor

Nancy Nancy LorenziLorenziAsst VC Health Affairs, Asst VC Health Affairs,

Organization Organization DevelopmentDevelopment

Bill SteadBill SteadChief Information Chief Information

Architect (CIA)Architect (CIA)

Lauren Lauren BriskyBriskyVice ChancellorVice Chancellor

Administration and CFOAdministration and CFO

Mark JohnsonMark JohnsonChief Information Chief Information

Security OfficerSecurity Officer

Tim Tim GetsayGetsayAsst VC, MISAsst VC, MIS& Assoc CIA, & Assoc CIA,

Enterprise Enterprise ApplicationsApplications

Matt HallMatt HallAsst VC, ITS & Assoc Asst VC, ITS & Assoc

CIA, Enterprise CIA, Enterprise InfrastructureInfrastructure

Dan Dan MasysMasysChair, DBMI & ChiefChair, DBMI & Chief

Academic OfficerAcademic Officer

VU Information Technology OrganizationVU Information Technology Organization

VUMC’s

Virtual Office of Research Informatics

Executive Sponsorship

Jeff BalserAssoc VC, Research

Bill SteadAssoc VC,

Strategy & CIO

Tactical Leadership

Gordon BernardAsst VC, Research

John ManningVP, Research

Operations

Dan MasysChair, DBMI

John DoulisAsst VC,

Informatics

Day to Day Operations

Jill PulleyDirector, DNA

Databank

Paul Harris Director, GCRU, Informatics Core