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Linkages to EHRs and
Related Standards
What can we learn from the Parallel Universe of
Medical Informatics?
Koray Atalag, ...
Outline
• Models defined
• Clinical information modelling (CIM)
• EHR interop standards stack
• Clinical Terminology resou...
What is a “Model”?
3: structural design <a home on the model of an old farmhouse>
4: a usually miniature representation of...
Setting the context: Models?
• Biophysical realm: mathematical & anatomical models
– CellML, FieldML, SBML, SEDML, Biosign...
Clinical Information Models
Archetypes, Detailed Clinical Models, Clinical Models etc.
• Depict how clinical information i...
Clinical Information Models - Why?
• Hardcoding domain knowledge into software is bad:
clinical software is difficult (=ex...
Example:Blood Pressure Measurement
mindmap representation of openEHR Archetype
Example: Clinical Problem/Diagnosis
8
mindmap representation of openEHR Archetype
Example: NZ
Cardiac
Registry
9
Screenshot of openEHR Template from a tool
Where to they fit?
• Biophysical models: best-effort approximation of
biophysical phenomena (entity & process)
– Quantitat...
Some early explorations
• Physiome/VPH context
Digital Patient (ref: Discipulus Digital Patient Roadmap)
“digital represen...
Patient Avatar:
digital representation
of all health-related data that is
available for the individual,
as the general bas...
Can the Physiome community learn from this?
EHR Interoperability Standards
Clinical Terminology
(The study of terms and their use in healthcare)
• Umbrella term for:
– Pure coding: LOINC
(assigning...
Popular Terminologies
• ICD - International Classification of Diseases (by WHO)
– First edition published in 1900! Revised...
SNOMED-CT
(Systematized Nomenclature of Medicine)
• >300,000 biomedical concepts
• ~800,000 English language descriptions ...
SNOMED Example
Coronary
arteriosclerosis
Structural
disorder of heart
Heart disease
Cardiac finding
Cardiovascular
finding
Finding by sit...
Expressing Composite Clinical
Statements in SNOMED
• Pre-coordinated terms present for most commonly seen
concepts; i.e. g...
Refining precise semantics
(aka clinical expressions)
 Can be useful for the Biophysical
community for tackling composite...
UMLS: Integrating Biomedicine
Unified Medical Language System
Biomedical
literature
MeSH
Genome
annotations
GO
Model
organ...
 Open source specs & software for representing health
information and person-centric records
– Based on 20 years of inter...
Archetype
Editor
Semantics in openEHR
• Whole-of-model meta-data:
– Description, concept references (terminology/ontology),
purpose, use, m...
Example:Blood Pressure Measurement
mindmap representation of openEHR Archetype
32
OnlineModelRepository
HL7 FHIR
Fast Healthcare Interoperability Resources
• Very recent! A draft standard but crazy adoption!
– ONC supports, Ep...
Example FHIR
Resource
(Medical Device)
Some concluding thoughts
Linking the two universes – shared semantics!
• Semantic annotation mechanisms & tooling already
...
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Linkages to EHRs and Related Standards. What can we learn from the Parallel Universe of Medical Informatics?

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This is the prezo I used during the CellML workshop in Waiheke Island, Auckland, New Zealand on 13 April 2015. The aim was to introduce information modelling methods and tools for the purpose of inspiring computational modelling work in the area of semantics and interoperability.

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Linkages to EHRs and Related Standards. What can we learn from the Parallel Universe of Medical Informatics?

  1. 1. Linkages to EHRs and Related Standards What can we learn from the Parallel Universe of Medical Informatics? Koray Atalag, MD, PhD, FACHI Senior Research Fellow (ABI & NIHI) k.atalag@auckland.ac.nz
  2. 2. Outline • Models defined • Clinical information modelling (CIM) • EHR interop standards stack • Clinical Terminology resources • openEHR & HL7 modelling • Shared semantics – how to? • Q/A
  3. 3. What is a “Model”? 3: structural design <a home on the model of an old farmhouse> 4: a usually miniature representation of something; also: a pattern of something to be made 5: an example for imitation or emulation 6: a person or thing that serves as a pattern for an artist; especially : one who poses for an artist 7: archetype 8: an organism whose appearance a mimic imitates 9: one who is employed to display clothes or other merchandise 10: a type or design of clothing/product 11: a description or analogy used to help visualize something (as an atom) that cannot be directly observed 12: a system of postulates, data, and inferences presented as a mathematical description of an entity or state of affairs Source: Merriam-Webster online (accessed yesterday)
  4. 4. Setting the context: Models? • Biophysical realm: mathematical & anatomical models – CellML, FieldML, SBML, SEDML, BiosignalML etc. – But also image or measurement based models • Medical informatics/EHR realm: information models – openEHR, HL7, ISO 13606, CIMI, OMG etc. • Software engineering: many models – UML, data models (ER, EER) • Business/Management: – Business Process, workflow etc. • Probably many more; – e.g. generic conceptual models
  5. 5. Clinical Information Models Archetypes, Detailed Clinical Models, Clinical Models etc. • Depict how clinical information is organized and described inside an EHR system or repository, or for EHR communication • Define both the information structure and formal semantics of documented clinical concepts • CIM Facilitate: – Clinical  technical communication – Organizing, storing, querying, & displaying data – Data exchange & distributed computing – Data linkage, analytics & decision support * Main purpose is to support healthcare delivery
  6. 6. Clinical Information Models - Why? • Hardcoding domain knowledge into software is bad: clinical software is difficult (=expensive) to build and even more difficult to maintain! – Size and complexity of Biomedicine – Changeability of requirements (mostly clinical information) – Variability of practice • Propriety clinical applications form silos of data – Non-compatible information hinders data reuse • One big goal is to employ Model Driven Architecture/Engineering principles by defining reusable models with non-ambiguous/shared semantics  Provides scientific rigour for clinical information required for Research (e.g. EHR data are dirty!)
  7. 7. Example:Blood Pressure Measurement mindmap representation of openEHR Archetype
  8. 8. Example: Clinical Problem/Diagnosis 8 mindmap representation of openEHR Archetype
  9. 9. Example: NZ Cardiac Registry 9 Screenshot of openEHR Template from a tool
  10. 10. Where to they fit? • Biophysical models: best-effort approximation of biophysical phenomena (entity & process) – Quantitative; using math equations for laws of physics & chemistry acting on biological material properties – Formal semantics using annotations w/ ontology • Clinical Information Models: patterns/blueprints – Capture structure & semantics of clinical information – Formal semantics using terminology bindings & annotations – Designed for instantiation  data instances carry real world data CIM key to obtaining reliable computable data from EHR – Can be used to validate biophysical models – provide parameter values for patient-specific models – Key to understand effects of environment & random (unexplained) phenomena
  11. 11. Some early explorations • Physiome/VPH context Digital Patient (ref: Discipulus Digital Patient Roadmap) “digital representation of the integration of the different patients-specific models for better prediction and treatment of diseases in order to provide patients with an affordable, personalised and predictive care”  Use multi-scale integrated biophysical models + CIM
  12. 12. Patient Avatar: digital representation of all health-related data that is available for the individual, as the general basis for the construction of Virtual Physiological Human workflows
  13. 13. Can the Physiome community learn from this?
  14. 14. EHR Interoperability Standards
  15. 15. Clinical Terminology (The study of terms and their use in healthcare) • Umbrella term for: – Pure coding: LOINC (assigning a code to an object or concept) – Coding/classification: ICD, ICPC, ATC, ICNP (coding and ordering/grouping within a domain for a specific purpose; i.e. mortality statistics, costing) – Nomenclature: GMDN, UMDNS (assigning a word or phrase to an object or concept) – Controlled vocabulary: SNOMED, MEDCIN (all of above plus formal relationships - a way to organize knowledge). Also known as Semantic Nets – Also at times ontology
  16. 16. Popular Terminologies • ICD - International Classification of Diseases (by WHO) – First edition published in 1900! Revised every 10 years • SNOMED CT - Systematized Nomenclature of Medicine - Clinical Terms • LOINC – Lab test orders and results • READ Codes – v3 used in UK/NZ General Practice – > 7,000 anatomic concepts, 16,000 operative procedures and 40,000 disorders – Hierarchical; code + term or a short phrase about a healthcare concept  A big problem is that there are so many alternative terminologies!
  17. 17. SNOMED-CT (Systematized Nomenclature of Medicine) • >300,000 biomedical concepts • ~800,000 English language descriptions (terms) • ~1.4 million semantic relationships (i.e. IS_A) • Hierarchically organised in multiple axes • Addressing the whole EHR space • Governed by IHTSDO (intl. and powerful) • Mapped to ICD and through UMLS to 100s others • Aligned/harmonised with LOINC and HL7 • Considered as formal ontology (OWL representation)  The single most important terminology now
  18. 18. SNOMED Example
  19. 19. Coronary arteriosclerosis Structural disorder of heart Heart disease Cardiac finding Cardiovascular finding Finding by site Clinical finding SNOMED CT Concept Mediastinal finding Finding of region of thorax Finding of trunk structure Finding of body region Viscus structure finding Disorder of mediastinum Disorder of thorax Disorder of trunk Disorder by body site Disease Disorder of body system Disorder of body cavity Disorder of cardiovascular system Disorder of coronary artery Coronary artery finding Arterial finding Blood vessel finding General finding of soft tissue Disorder of soft tissue of thoracic cavity Disorder of soft tissue of body cavity Disorder of soft tissue Disorder of artery Vascular disorder Arteriosclerotic vascular disease Soft tissue lesion Degenerative disorder
  20. 20. Expressing Composite Clinical Statements in SNOMED • Pre-coordinated terms present for most commonly seen concepts; i.e. gastric ulcer • Post coordination; more “meaning” can be added by appending other terms and relationships – i.e. ulcer | has site: stomach | has severity: low • Formal mathematical basis (Description Logics)
  21. 21. Refining precise semantics (aka clinical expressions)  Can be useful for the Biophysical community for tackling composites?? ^ 1111000000132 |allergy event|: 246075003 |causative agent| = < 373873005 |pharmaceutical / biologic product| OR < 105590001 |substance| HL7 & OMG: CTSII - Common Terminology Services -common functional characteristics -basic functionality to query and access
  22. 22. UMLS: Integrating Biomedicine Unified Medical Language System Biomedical literature MeSH Genome annotations GO Model organisms NCBI Taxonomy Genetic knowledge bases OMIM Clinical repositories SNOMED CTOther subdomains … Anatomy FMA UMLS By NLM - UMLS integrates and distributes key terminology and ontology (knowledge sources)
  23. 23.  Open source specs & software for representing health information and person-centric records – Based on 20 years of international research, including Good European Health Record Project (GEHR) – Superset of ISO/CEN 13606 EHR standard  Not-for-profit organisation - established in 2001 www.openEHR.org  Extensively used in research  Separation of clinical and technical worlds • Big international community • Recently been elected to Board
  24. 24. Archetype Editor
  25. 25. Semantics in openEHR • Whole-of-model meta-data: – Description, concept references (terminology/ontology), purpose, use, misuse, provenance, translations • Item level semantics (implicit information related) – Trees/Clusters (Structure) – Leaf nodes (Data Elements) • Explicitly: different types of terminology bindings: – linking an item concept (structure or element) to external terminology for the purpose of defining its meaning – Linking of data element values to external terminology (e.g. a RefSet or terminology query) – Linking of runtime data element names to external terminology (e.g. a RefSet or terminology query) • Instance level semantic annotations – applies to actual data collected (to be discussed on Tuesday)
  26. 26. Example:Blood Pressure Measurement mindmap representation of openEHR Archetype
  27. 27. 32 OnlineModelRepository
  28. 28. HL7 FHIR Fast Healthcare Interoperability Resources • Very recent! A draft standard but crazy adoption! – ONC supports, Epic, Cerner, Orion…all big vendors support • Developer oriented / pragmatic • RESTful API • Inspired by modern Web technologies – leveraging W3C standards and Services oriented App world • Purpose: Health Information Exchange – But can underpin an EHR, clinical data repository • Clinical information defined by Resources; – 80/20 rule – only model majority of use cases (as opposed to Archetypes being maximal datasets) – 20% go into extensions – Terminology bindings supported
  29. 29. Example FHIR Resource (Medical Device)
  30. 30. Some concluding thoughts Linking the two universes – shared semantics! • Semantic annotation mechanisms & tooling already exist in both universes – CellML annotations, SemGen, Chaste etc. – openEHR Archetypes, SNOMED, CTSII etc.  Key considerations should be: • Shared ontologies / identifiers – SNOMED>UMLS> FMA/GO etc. – But SNOMED and FMA anatomy not same but similar! Bodenreider O, Zhang S. Comparing the Representation of Anatomy in the FMA and SNOMED CT. AMIA Annu Symp Proc. 2006;2006:46–50. • Shared annotation approach (inc. repository) – RICORDO, PMR2, SemGen etc. – More research on joint semantic annotations. • Shared modelling patterns & governance?

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