Data aggregation across an academic medical center: The...

Biomedical Informatics

Data aggregation across an academic medical center:

The role of semantic harmonization

CG Chute MD DrPHProfessor and Chair, Biomedical Informatics

Mayo Clinic College of Medicine

© Mayo Clinic College of Medicine 2006 2


Data Aggregationa how-to guide

1.1. Build up the Blue space (Core registry content)Build up the Blue space (Core registry content)2.2. Support Support cloneablecloneable registry infrastructureregistry infrastructure3.3. Ensure data governance adopts standardsEnsure data governance adopts standards4.4. Annotate stuff with semantic metadataAnnotate stuff with semantic metadata5.5. Balance data investment with AnalyticsBalance data investment with Analytics

•• Semantic Integration Semantic Integration –– the LexGrid projectthe LexGrid project



From Practice-based Evidenceto Evidence-based Practice

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Clinical DataClinical Data(Niklas Koppernigk)

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““specializedspecialized”” registry dataregistry dataDisease, site, or Disease, site, or PxPx specificspecific



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““CoreCore”” registry dataregistry dataCommon to all registriesCommon to all registriesDemographics, labs, Demographics, labs, DxsDxs, , PxsPxs

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“Core” registry data has two primary sourcesDatabases and Humans

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An obvious goal is to increase the proportion ofthe Blue space

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Recognize that registry specific data will always exist

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Recommendation #1Build up the Blue space

Focus on infrastructure resources that can• Systematically collect data from clinical and

genomic source systems – build warehouse• Prioritize use of the warehouse – build marts

• Automate Core sections of registries ∅End-user data retrievals from warehouse• May create epidemiology, quality marts…

• Emphasize tools that have greatest impact on the questions and users



Building the Blue spaceExtract, Transform, Load (ETL) – the obvious• Problem lists, diagnoses, findings, Chief Comp.• Co-Path, pathology notes, CAP protocols• Laboratory data (atop SOA LIMS environment)• Imaging data – Radiology notes, Px, Dx• Treatments and Interventions

• Drug, chemotherapies, OTCs, Rx elsewhere…• Surgeries, bedside procedures, interventional Ψ…• Radiation, implants, devices, stents…

• Genomics, Proteomics, LIMS, …



Registries, intelligently implemented,are good

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Suite of Cancer RegistriesSuite of Cancer RegistriesSynergistically wroughtSynergistically wroughtComputationally populatedComputationally populated



Mayo’s Registry Design and Deploymentstrategic, consistent, supportable, useful

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Registries connected to scalable “Blue” spaceEfficiently get consistent core data to registriesBb

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Core from Core from WarehouseWarehouse



Registries can inform the Warehouse about their site specific or human annotated data

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Registries may evolve into non-transactional, federated resources →“marts” of their own

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Recommendation #2Support Cloneable Registry Infrastructure

• Metaphor of registries as “kits”• Just add your specialized data

• Interfaces to “Core” data as commodity • Federation to Network or Grid of Registries

• Warehouse as coordinating core – initially• Retrieval Tools scalable across registries

• Emergence of standard registry “Mart”• Common interfaces across federated data• Common security and auditing infrastructure• Establish cost-effective research resource base



Data StandardsPresume an accepted premise…

• Comparable and consistent data is crucial for pooling data and meta-analyses

• Data generated in standard form is more interoperable

• Data interchange outside the institution• Patient – Personal Health Records • Referring physicians• NHIN – RHIO (within One Mayo)• NIH collaboration, Big Science

• caBIG, PharmGKB, CTSA - NCRR



The Genomic Era• The genomic transformation of medicine far

exceeds the introduction of antibiotics and aseptic surgery

• The binding of genomic biology and clinical medicine will accelerate

• The implications for shared semantics across the basic science and clinical communities are unprecedented

• The implications for Public Health surveillance and inference are profound



The Continuum Of Biomedical InformaticsBioinformatics meets Medical Informatics

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Recommendation #3Ensure data governance adopts standards

• Process well on track in Data Trust efforts• Must synergize research and clinical agendas• Recognize this is all the same problem• Mayo cannot sustain costs of idiosyncratic data

• Maintenance of “one of a kind”• Opportunity costs in collaboration and interchange• Cannot leverage vision of patient “repositories”• Divergence: Warehouse, registries, data-marts...

• Efficient Phenotype-Genotype discovery



Semantic MetadataAnnotations for Applications

• Data Organization• Data Annotation• Ontology driven architectures• Semantic interoperability• Data retrieval Analytic subsets

• The right data for the problem• Knowledge enabled analyses

• The right analyses for the data



Content vs. StructureSemantic implications for applications

Family History of Breast CancerFamily History of Heart DiseaseFamily History of Stroke

Breast CancerBreast CancerHeart DiseaseHeart DiseaseStrokeStroke

Family HistoryFamily History

TerminologicModel

Information Model Equivalent

Content[adapted from Rossi-Mori]



Aggregation Logics by domainrule-based aggregations

Decision Support Decision Support and Error Detectionand Error Detection

Public Health andPublic Health andSurveillanceSurveillance

Reimbursement Reimbursement and Management and Management

Outcome Research Outcome Research and Epidemiologyand EpidemiologyFindingsFindings InterventionsInterventionsEventsEvents



Data organization

• Health IT Standards• Syntactic – XML, HL7, database schema,• Semantic – OWL, HL7, ontologies• Interoperable

• Self Describing Objects• Data storage technology (NCRR grant)

• Annotation Metadata• Provenance• Data management• Content, Context, Structure, Semantics



Recommendation #4Annotate stuff with semantic metadata

• Know what data you have• Extract, transform, load (ETL) the right stuff• Craft data marts (registries) from the right pieces• Use the right data for the question• Ensure data integrity, reliability, consistency



Mayo-IBM Semantic Annotation Project• Collaboration on application of semantic annotation to

clinical and genomic data• Apply UIMA tools• Leverage LexGrid standards• De facto evolution of distributed/SOA in Mayo research

• Demonstrate transformation of information model to interpretable semantics

• HL7 TermInfo; NCI caBIG; ONC NHIN (HITSP, HL7, NLM)• Evolve warehouse collaboration to support Semantics

• Data Navigation• Context Specification• Query Formation• Intelligent Analytic pipelining



Recommendation #5Balance data investment with Analytics

• Data all dressed up with no where to go• Analytics poses considerable challenge

• De facto bottleneck in many areas• Ideal methods not always obvious

• Collaborative opportunity with many partners• IBM, TGen, Workflow partners


The Lexical Grid(LexGrid)



The Lexical GridDefinition

• The LexGrid package represents a comprehensive set of software and services to load, publish, and access vocabulary or ontological resources.

• Provides a single information model • Published online, cross-linked, and indexed on

demand• Provides standardized building blocks and tools• Supports large-scale vocabulary adoption and use



LexGridNode

DataServices

Java

.NET

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Import

Editors

Browsers

Query Tools

XML

Browse andEdit

Export

EmbedLexBIG

Index

LexGrid Conceptual ArchitectureRRF

OBO

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Protégé CTS

Text

OWL

XML

Lex*Web

Clients



Common Terminology Services (CTS) • An HL7 ANSI standard

• Defines the minimum set of requirements for interoperability across disparate healthcare applications

• A specification for accessing terminology content• The CTS identifies the minimum set of functional

characteristics a terminology resource must possess for use in HL7.

• A functional model• Defining the functional characteristics of vocabulary

as a set of Application Programming Interfaces (APIs)



LexGrid for caBIG(LexBIG)

• Software development for NCI and Cancer Biomedical Informatics Grid (caBIG)

• Focused on common model and API• Use Cases and requirements demanded a rich

API• Coordinated infrastructure for Cancer Research• Clinical Trials, Integrative Cancer Research,

Tissue Banking and Pathology Tools• Vocabulary, Common Data Elements,

Architecture



NCBO LexBIONational Center for Biomedical Ontology

• Technology and methods allowing scientists to create, disseminate, and manage biomedical information

• Infrastructure for Biomedical Ontologies and Biomedical Data (LexBIO)

• Machine-able Knowledge Representation• LexGrid – Spanning the Chasm of Semantic

Despair



LexPHINCDC Public Health Informatics Network

• Adoption of the LexGrid Model• Replace PHIN Vocabulary Services (VS)• Addresses genomic characterization of disease

• Span semantic chasm with Gene Ontology• Organized Value Sets

• Outbreak Management System• Biosurveillance and Biosense aggregation



Health Level Seven (HL7)

• Vocabulary and value domain management• Tooling for vocabulary submissions• Includes change events for HL7 governance

process



LexGrid Applications toSemantic Annotation and Integration

• Basis for NLP (Natural Language Processing) entity annotation – clinical notes

• Harmonize data elements, values sets• Getting the data right

• Information retrieval and navigation• Getting the right data

• Grounding for data governance• Foundation for semantic interoperability



Conclusions – Data Integration

• Heterogeneous data breeds idiosyncratic structure

• Data element alignment is not sufficient• Semantics derive from information models and

content• Semantic annotation of data is crucial• Tools such as LexGrid must be applied



ResourcesLexGrid Projecthttp://informatics.mayo.edu/LexGrid

LexBIG Forge Sitehttp://gforge.nci.nih.gov/projects/lexbig

caBIG LexGrid CVShttp://cabigcvs.nci.nih.gov/viewcvs.cgi/lexgrid

NCBO Projecthttp://www.bioontology.org

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