Linkages to EHRs and Related Standards

What can we learn from the Parallel Universe of Medical Informatics?

Koray Atalag, MD, PhD, FACHISenior Research Fellow (ABI & NIHI)k.atalag@auckland.ac.nz

Outline

• Models defined

• Clinical information modelling (CIM)

• EHR interop standards stack

• Clinical Terminology resources

• openEHR & HL7 modelling

• Shared semantics – how to?

• Q/A

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 made5: an example for imitation or emulation6: a person or thing that serves as a pattern for an artist; especially : one who poses for an artist7: archetype8: an organism whose appearance a mimic imitates9: one who is employed to display clothes or other merchandise10: a type or design of clothing/product11: a description or analogy used to help visualize something (as an atom) that cannot be directly observed12: 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)

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

Clinical Information ModelsArchetypes, 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

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!)

Example: Blood Pressure Measurement

mindmap representation of openEHR Archetype

Example: Clinical Problem/Diagnosis

8

mindmap representation of openEHR Archetype

Example: NZ Cardiac Registry

9Screenshot of openEHR Template from a tool

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

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

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

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 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

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!

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

SNOMED Example

Coronary arteriosclerosi

s

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

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)

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

UMLS: Integrating BiomedicineUnified Medical Language System

Biomedicalliterature

MeSH

Genomeannotations

GOModel

organisms

NCBITaxonom

y

Geneticknowledge bases

OMIM

Clinicalrepositories

SNOMED CT

Othersubdomains

…

Anatomy

FMA

UMLS

By NLM - UMLS integrates and distributes key terminology and ontology (knowledge sources)

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

Archetype Editor

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)

Example: Blood Pressure Measurement

mindmap representation of openEHR Archetype

32

Onl

ine

Mod

el R

epos

itory

HL7 FHIRFast 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

Example FHIRResource(Medical Device)

Some concluding thoughtsLinking 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?