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![Biomedical Informatics
© 2009 Mayo Clinic 5
Medical Concepts
Events, Observations, Interventions
•How should we represent it? Language:
• Nuance, detail, unfettered combination
• Timely, current, never obsolete
• Natural, friendly, established
• [Ambiguous, imprecise, unpredictable]
•Codes:
• Concise, precise
• Structured, consistent, well formed
• Analyzable, manipulable
• [Rigid, tedious, high maintenance]](https://image.slidesharecdn.com/chute-240420155143-3e2e9272/75/Standards-for-interoperable-EHR-Christopher-G-Chute-MD-DrPH-Professor-Biomedical-Informatics-Mayo-Clinic-College-of-Medicine-July-13-2009-5-2048.jpg)
![Biomedical Informatics
2009– ARRA Requirements and Tiger Teams
HITSP Process EHR Centric IS
• Capitalize on existing
specifications
• Organize according to EHR
Information Exchanges
• Establish Capability Concept
Security, Privacy, and
Infrastructure
• Define Infrastructure Service
Collaborations
• Integrate Security and Privacy
functions
Quality IS
• Ability to interoperably specify
Measure
• Ability to extract patient-specific
data from EHR and other sources
for a measure
[adopted from HITSP Panel]](https://image.slidesharecdn.com/chute-240420155143-3e2e9272/75/Standards-for-interoperable-EHR-Christopher-G-Chute-MD-DrPH-Professor-Biomedical-Informatics-Mayo-Clinic-College-of-Medicine-July-13-2009-12-2048.jpg)
![Biomedical Informatics
ARRA / HITECH Eight Priority Areas
Eight Priority Areas for HIT in ARRA
HITSP Tasks
for ARRA
Security
+
Privacy
HIT
Infrastr
ucture
Certifi
ed
EHR
Discl
osure
Audit
Quality IIHI*
Unus
able
Demog
raphic
Data
Vulner
able
Pop
EHR-Centric IS n n n n
Security and
Privacy
Service Collab
n n n n n n
Quality
Measures
n n n
Supporting Deliverables
Harmonization
framework
n n n n
Data
Architecture
n n n n n
* Individually Identifiable Health Information (IIHI) Unusable
[adopted from HITSP Panel]](https://image.slidesharecdn.com/chute-240420155143-3e2e9272/75/Standards-for-interoperable-EHR-Christopher-G-Chute-MD-DrPH-Professor-Biomedical-Informatics-Mayo-Clinic-College-of-Medicine-July-13-2009-13-2048.jpg)
![Biomedical Informatics
ARRA Title IV (Division B) – Section 401 – Medicare
Incentives
HITSP Tasks
for ARRA
e-
Prescribing
Info Exchange to
Improve Quality
Report Quality
Measures
Certified
EHR
EHR-Centric IS n n n n
Security and
Privacy Service
Collab
n n n
Quality
Measures
n n
Supporting Deliverables
Harmonization
framework
n n n
Data
Architecture
n n n n
ARRA / HITECH Meaningful Use
[adopted from HITSP Panel]](https://image.slidesharecdn.com/chute-240420155143-3e2e9272/75/Standards-for-interoperable-EHR-Christopher-G-Chute-MD-DrPH-Professor-Biomedical-Informatics-Mayo-Clinic-College-of-Medicine-July-13-2009-14-2048.jpg)
![Biomedical Informatics
© 2009 Mayo Clinic 25
Clinical Data Interchange Standards
Consortium – CDISC (2)
•CDISC has established global standards for
collection, exchange, regulatory submission and
archive of medical research data.
•Charter Agreement with HL7 since 2002;
commitment to harmonize standards
•Liaison A status to ISO TC 215 (Healthcare)
•Newest member of JIC (Joint Initiative
Council) [HL7/CEN/ISO/CDISC]](https://image.slidesharecdn.com/chute-240420155143-3e2e9272/75/Standards-for-interoperable-EHR-Christopher-G-Chute-MD-DrPH-Professor-Biomedical-Informatics-Mayo-Clinic-College-of-Medicine-July-13-2009-25-2048.jpg)
![Biomedical Informatics
© 2009 Mayo Clinic 31
Urgent Need for
US Health Terminology Authority
•Model after HL7 “US Realm” notion
•Forum for adjudicating “value set” contents
• Prototyped within HITSP Foundations Committee
•Must also identify National Terminology Service
• Distribution point for USHTA Content
• Download whole code systems, values sets
• Synchronizing master for “local” terminology services
• Built along standard for common terminology services
• May provide direct terminology services to small
installations
[IOM testimony]](https://image.slidesharecdn.com/chute-240420155143-3e2e9272/75/Standards-for-interoperable-EHR-Christopher-G-Chute-MD-DrPH-Professor-Biomedical-Informatics-Mayo-Clinic-College-of-Medicine-July-13-2009-31-2048.jpg)
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Similar to Standards for interoperable EHR Christopher G Chute MD DrPH Professor, Biomedical Informatics Mayo Clinic College of Medicine July 13, 2009
![Medical Informatics World 2014 [Full Agenda]](https://cdn.slidesharecdn.com/ss_thumbnails/hitfinal-140204065632-phpapp02-thumbnail.jpg?width=640&height=640&fit=bounds)
Standards for interoperable EHR Christopher G Chute MD DrPH Professor, Biomedical Informatics Mayo Clinic College of Medicine July 13, 2009
- 1. Biomedical Informatics Standards forinteroperable EHR Narrowing the Research-Practice Divide in Evidence-Based Medicine with the Adoption of EHRs NIDA Christopher G Chute MD DrPH Professor, Biomedical Informatics Mayo Clinic College of Medicine July 13, 2009
- 2. Biomedical Informatics © 2009Mayo Clinic 2 Health Care Is An Information Intensive Industry •Control of Health Care Costs ... •Improved Quality of Care ... •Improved Health Outcomes ... •Appropriate Use of Health Technology... •Compassionate Resource Management... ... depend upon information … Ultimately Patient Data
- 3. Biomedical Informatics © 2009Mayo Clinic 3 Information Beyond Practice Secondary Re-use as Primary A Interest •Data Collected for Clinical Care Forms the Basis for Patient Experience Repositories •The Importance of a Well Characterized, High Quality Patient Experience Repository May Exceed the Value of the Primary Information Many Fold
- 4. Biomedical Informatics © 2009Mayo Clinic 4 Repositories of Patient Information •Disease Natural History •Treatment Response (non-RCT) •Basis for Guidelines, Clinical Paths, Best Practice •“Just in Time” Source for Decision Support • Have we seen a patient just like this… •Efficient and Effective Care Delivery
- 5. Biomedical Informatics © 2009Mayo Clinic 5 Medical Concepts Events, Observations, Interventions •How should we represent it? Language: • Nuance, detail, unfettered combination • Timely, current, never obsolete • Natural, friendly, established • [Ambiguous, imprecise, unpredictable] •Codes: • Concise, precise • Structured, consistent, well formed • Analyzable, manipulable • [Rigid, tedious, high maintenance]
- 6. Biomedical Informatics © 2009Mayo Clinic 6 Mayo: A Century-Long Tradition of Studying Patient Outcomes Demographics Diagnoses Procedures Narratives Laboratories Pathology… High-Volume Data Storage
- 7. Biomedical Informatics © 2009Mayo Clinic 7 Semantic Organization
- 8. Biomedical Informatics © 2009Mayo Clinic 8 From Practice-based Evidence to Evidence-based Practice Patient Encounters Clinical Databases Registries et al. Clinical Guidelines Medical Knowledge Expert Systems Data Inference Knowledge Management Decision support Standards Shared Semantics Vocabularies & Terminologies
- 9. Biomedical Informatics © 2009Mayo Clinic 9 Value Proposition “Those with more detailed, reliable and comparable data for cost and outcome studies, identification of best practices, guidelines development, and management will be more successful in the marketplace.” SP Cohn; Kaiser Permanente
- 10. Biomedical Informatics © 2009Mayo Clinic 10 Standards as the Basis for Scientific Data Representation and Interchange Without Standards... •Health Data is non-comparable •Health Systems cannot Interchange Data •Secondary Uses (Research, Efficiency) are not possible •Linkage to Decision Support Resources not Possible •Translational research is hobbled
- 11. Biomedical Informatics © 2009Mayo Clinic 11 US Health Standards Initiatives •1986 Laboratory transport message – ASTM •1987 HL7 founded •1991 Coalition for HISPP within ANSI • Health Information Standards Planning Panel •1992 HISPP formed •1995 HISB formed (Board) •1996 HIPAA passed; NCVHS rechartered •1998 ISO TC 215 formed •2005 Office of the National Coordinator formed •2005 HITSP formed (supersedes HISB) •2009 HIT Policy and Standards Committees
- 12. Biomedical Informatics 2009– ARRARequirements and Tiger Teams HITSP Process EHR Centric IS • Capitalize on existing specifications • Organize according to EHR Information Exchanges • Establish Capability Concept Security, Privacy, and Infrastructure • Define Infrastructure Service Collaborations • Integrate Security and Privacy functions Quality IS • Ability to interoperably specify Measure • Ability to extract patient-specific data from EHR and other sources for a measure [adopted from HITSP Panel]
- 13. Biomedical Informatics ARRA /HITECH Eight Priority Areas Eight Priority Areas for HIT in ARRA HITSP Tasks for ARRA Security + Privacy HIT Infrastr ucture Certifi ed EHR Discl osure Audit Quality IIHI* Unus able Demog raphic Data Vulner able Pop EHR-Centric IS n n n n Security and Privacy Service Collab n n n n n n Quality Measures n n n Supporting Deliverables Harmonization framework n n n n Data Architecture n n n n n * Individually Identifiable Health Information (IIHI) Unusable [adopted from HITSP Panel]
- 14. Biomedical Informatics ARRA TitleIV (Division B) – Section 401 – Medicare Incentives HITSP Tasks for ARRA e- Prescribing Info Exchange to Improve Quality Report Quality Measures Certified EHR EHR-Centric IS n n n n Security and Privacy Service Collab n n n Quality Measures n n Supporting Deliverables Harmonization framework n n n Data Architecture n n n n ARRA / HITECH Meaningful Use [adopted from HITSP Panel]
- 15. Biomedical Informatics © 2009Mayo Clinic 15 Clinical Research in EHRs •Proposed presentation to AHIC – early 2007 •Discussed at CTSA/caBIG meeting w/ ONC •AHIC approves as “alternative path” June 2008 • Funding to coordinate from research community •ANSI convenes EHR Clinical Research Value Case Workgroup – fall 2008 •CCHIT adds Clinical Research to roadmap for EHR certification, January, 2009 •HITSP Tiger Team for Research, May 2009
- 16. Biomedical Informatics © 2009Mayo Clinic 16 HL7 Reality •ANSI accredited standards organization •Peer international organization with ISO and CEN •Roughly 5000 person members •Working Group meetings three times per year • Roughly 500 attendees for one week •De facto think tank and forum for state of the art issues in Health care record, messages, and content •Recognized by CHI, HITSP, and ONC in US
- 17. Biomedical Informatics © 2009Mayo Clinic 17 So we are all using HL7, what is the problem? Œ OBX|1|CE|ABO^ABO GROUP| |O^Type O| • OBX|1|CE|BLDTYP^ABO GROUP| |TYPEO^Type O| ŽOBX|1|CE|ABOTYPE^ABO GROUP| |OPOS^Type O| Equivalence not obvious to computer OBX|1|CE|883-9^ABO GROUP| |F-D1250^Group O|
- 18. Biomedical Informatics © 2009Mayo Clinic 18 The HL7 Reference Information Model (RIM)
- 19. Biomedical Informatics © 2009Mayo Clinic 19 Core Abstractions of the RIM Act Participant Role Entity
- 20. Biomedical Informatics © 2009Mayo Clinic 20 Is that all? •The RIM adheres to a high-level abstract model •Most of the “detail” exists within the vocabulary extensions of the RIM •The Model goes much deeper than the boxes •The surface boxes are the veneer
- 21. Biomedical Informatics © 2009Mayo Clinic 21 PersonPractitioner class_cd : CS determiner_cd : CS id : SET<II> nm : BAG<EN> telecom : BAG<TEL> CertifiedEntity class_cd : CS id : SET<II> telecom : BAG<TEL> 0... 1... subjectPerson 0... certificate 1... Author type_cd : CS signature_cd : CS signature_txt : ED 1... 0... origination 1... certifiedEntity 0... Organization class_cd : CS determiner_cd : CS id : SET<II> nm : BAG<EN> HealthCareProvider class_cd : CS id : SET<II> telecom : BAG<TEL> 0... 1... healthCareOrganization 0... healthCareProviderLicense 1... Performer type_cd : CS 1... 0... performance 1... healthCareProvider 0... ObservationOrder class_cd : CS mood_cd : CS id : SET<II> cd : CD activity_time : GTS 0... 0... observationOrder 0... author 0... 0... 1... performer 0... observationOrder 1... Person class_cd : CS determiner_cd : CS id : SET<II> nm : BAG<EN> telecom : BAG<TEL> administrative_gender_cd : CE birth_time : TS Patient class_cd : CS id : SET<II> addr : BAG<AD> 1... 0... healthCareProvider 1... patient 0... Subject type_cd : CS 1... 0... observationOrder 1... subject 0... 1... 0... subjectOf 1... patient 0... Message structure from RMIM 1 2 3 4 8 9 10 5 6 7
- 22. Biomedical Informatics © 2009Mayo Clinic 22 Specialization by restriction (constraint) Act class_cd <= ACT mood <= ActMood code <= ActCode Observation class_cd <= OBS mood <= ActMood code <= ObservationType ObservationOrder class_cd <=OBS mood <= ORD code <= ObservationType LabOrder class_cd <=OBS mood <= ORD code <= LabObservation LabOrder (US) class_cd <=OBS mood <= ORD code <= LOINC LabOrder (UK) class_cd <=OBS mood <= ORD code <= SNOMED Lab
- 23. Biomedical Informatics © 2009Mayo Clinic 23 HL7 V2 vs V3 •V2 – bar delimited ASCII • No semantic interoperability • Vocabulary binding – underspecified • Everybody is using it. •V3 – model driven architecture • XML syntax (option) • Well-defined semantic interoperability • Elegant vocabulary binding • Nobody is using it. •ISO OSI vs. TCP/IP parable…
- 24. Biomedical Informatics © 2009Mayo Clinic 24 •Clinical Data Interchange Standards Consortium – a global, open non-profit standards development organization (SDO) •Standards openly available (www.cdisc.org) •Initiated as volunteer group 1997; incorporated 2000 • now > 230 organizational members • biopharmaceutical companies, technology providers, contract research organizations and academia • active committees in U.S., Europe, Japan, China 24
- 25. Biomedical Informatics © 2009Mayo Clinic 25 Clinical Data Interchange Standards Consortium – CDISC (2) •CDISC has established global standards for collection, exchange, regulatory submission and archive of medical research data. •Charter Agreement with HL7 since 2002; commitment to harmonize standards •Liaison A status to ISO TC 215 (Healthcare) •Newest member of JIC (Joint Initiative Council) [HL7/CEN/ISO/CDISC]
- 26. Biomedical Informatics © 2009Mayo Clinic 26 CDISC Standard Description Implementation Version Release Date SDTM, SEND Ready for regulatory submission of CRT Over 12,000 downloads as of Apr 08 2004* ODM CDISC Transport Standard for acquisition, exchange, submission (define.xml) archive 2001* Define.xml Case Report Tabulation Data Definition Specification (CRTDDS) 2005* LAB Content standard – available for transfer of clinical lab data to sponsors 2002 ADaM General Considerations document and examples of datasets for submission 2004 Protocol Representation Collaborative effort to develop machine- readable standard protocol with data layer In progress- due in 2008 Terminology Codelists Developing standard terminology to support all CDISC standards 2006 (Pkg1 & 2A) Pkg 2B in progress CDASH Data acquisition (CRF) standards In progress- due in 2008 * Specification referenced via FDA Final Guidance
- 27. Biomedical Informatics © 2009Mayo Clinic 27 27 A clinical research domain analysis model (UML) initiated by CDISC, BRIDGing •Organizations (CDISC, HL7, FDA, NCI) •Standards - all CDISC standards harmonized into BRIDG •Research and Healthcare Towards semantic interoperability; a Portal to Healthcare Represents clinical research in the context of the HL7 RIM Open source ; Collaborative Project • See BRIDG Model on CDISC website or www.bridgmodel.org *Biomedical Research Integrated Domain Group (BRIDG) Model The BRIDG Model*
- 28. Biomedical Informatics © 2009Mayo Clinic 28 The Revised, 2-layered (2-views) BRIDG Model Consistent levels of abstraction and explicitness in multiple sub-domain ‘Requirements Models’ Consistent levels of RIM-compliance and explicitness in a single ‘Analysis Model’ Sub-Domain 1 Sub-Domain 2 Sub-Domain 3 Sub-Domain 4 Sub-Domain 5 Understandable to Domain Experts (DaM) Unambiguously mappable to HL7 RIM (DAM) NOTE: Sub-domains may or may not intersect semantically
- 29. Biomedical Informatics © 2009Mayo Clinic 29 ISO TC215 Health Informatics •Created in 1998 •Heritage of national standards organizations • Many countries REQUIRE use of ISO standards if they exist (not USA) •Organized by Core Infrastructure and Use Cases •Coordinates with other standards development organizations through Joint Initiative Council
- 30. Biomedical Informatics © 2009Mayo Clinic 30 ISO TC 215
- 31. Biomedical Informatics © 2009Mayo Clinic 31 Urgent Need for US Health Terminology Authority •Model after HL7 “US Realm” notion •Forum for adjudicating “value set” contents • Prototyped within HITSP Foundations Committee •Must also identify National Terminology Service • Distribution point for USHTA Content • Download whole code systems, values sets • Synchronizing master for “local” terminology services • Built along standard for common terminology services • May provide direct terminology services to small installations [IOM testimony]
- 32. Biomedical Informatics © 2009Mayo Clinic 32 HL7 Codes and Values – this week… •364 Concept Domains • Kinds of things, Dx, Px, Appt, drug,… •251 code systems • 155 internally maintained • 96 managed by reference (ICD, CPT, SNOMED…) •1579 Value sets (hierarchical) • Lists of things used in messages or applications • Drawn from coding systems • Used to represent Concept Domains •Tooling and maintenance done at Mayo
- 33. Biomedical Informatics © 2009Mayo Clinic 33 Proliferation of Content “Have it your way” Vocabulary Models •Major ontologies • SNOMED CT; Gene Ontology; LOINC; NDF-RT • UMLS Metathesaurus; NCI Thesaurus • HL7 RIM and Vocabulary; DICOM RadLex • CDC bioterrorism PHIN standards • caBIG DSR / CDEs (Common Data Elements) •All created with differing formats and models •Mechanisms for content sharing • Research Area
- 34. Biomedical Informatics © 2009Mayo Clinic 34 Mayo LexGrid Project Ontology Services •HL7 ANSI Standard •ISO Standard •Open specification •Provide consistency and standardization required to support large-scale vocabulary adoption and use • Common model, tools, formats, and interfaces •Standard terminology model (Excel to OWL) •Grid-nodal architecture •http://informatics.mayo.edu
- 35. Biomedical Informatics © 2009Mayo Clinic 35 LexGrid Node Data S e r v i c e s Java .NET ... Import Editors Browsers Query Tools XML Browse and Edit Export Embed L e x B I G Index LexGrid Conceptual Architecture Components RRF OBO OBO Text Protégé CTS Text OWL XML Lex* Web Clients LexGrid Service Index Registry
- 36. Biomedical Informatics © 2009Mayo Clinic 36 LexGrid Model Coding Scheme Relations Concepts Properties cd codingSchemes describable codingScheme concepts::concepts describable relations::relations describable relations::association relations:: associationInstance associatableElement relations:: associationTarget versionableAndDescribable concepts::codedEntry concepts::property concepts::comment concepts::definition concepts:: presentation 0..1 +concepts 0..* +relations 1..* +association 0..* +sourceConcept 0..* +targetConcept 1..* +concept 0..* +property
- 37. Biomedical Informatics © 2009Mayo Clinic 37 Examples and Proof of Concept •HL7 Vocabulary Model • Common Terminology Services •NIH RoadMap: Nat. Center Biomedical Ontologies • Mayo LexGrid project • Clinical and basic science (Gene Ontology) communities •NCI caBIG – Bioinformatics Grid • LexEVS (Enterprise Vocabulary Services) • NIH CTSA – Translational science
- 38. Biomedical Informatics © 2009Mayo Clinic 38 Mayo Enterprise Vocabulary Organization Reference Vocabularies External SNOMED LOINC GO ICDs CPTs … ~200 Mayo Internal Table 22 Table 61 SNOMED mods Mayo CPTs WARS … ~50 Mayo Thesauri Value Sets Cross Mapping Tables External UMLS WHO FIC … ~3 Mayo Internal Drugs Disease Symptoms Pt Functioning EDT Aggregation … ~8 LexGrid: Data Model, Data Store and Machinery External JACHO NACCR Ca NIH/NCI … ~1000 Mayo Internal Flow sheets MICS apps/screens Dept systems Registry screens Form questions Inf. for your Phys. … ~10,000 External SNOMED↔ICD CPT↔ICD LOINC↔SNOMED FDB↔NDC … ~500 Mayo Internal All thesauri All value sets Tab 22↔ICD FDB↔Fomulary … … >10,000
- 39. Biomedical Informatics © 2009Mayo Clinic 39 NCBO – A Bridge Across the Chasm
- 40. Biomedical Informatics © 2009Mayo Clinic 40 Expanded Categories
- 41. Biomedical Informatics © 2009Mayo Clinic 41 Proposed Process draft versioning not shown ICD in LexGrid Change Sets Review and select OWL DL editor Protégé Export and Load HL7/ISO format OWL RDF dump
- 42. Biomedical Informatics © 2009Mayo Clinic 42 Alternate Future ICD-11 SNOMED Joint ICD-IHTSDO Effort
- 43. Biomedical Informatics © 2009Mayo Clinic 43 Where is This Going? •Standards and interopreabilty are emerging as a first-rank US health priority •The boundary between “clinical” and “research” standards in biology and medicine is eroding •Information models and vocabulary exist along a continuum, that must integrate. •Information standards, especially vocabularies, are the foundation for scientific synergies. •Unprecedented consolidation has emerged across the health standards community