What if we never agree on a common health information model?

The National Institutefor Health InnovationInformatics

What If We Never Agree On A Common Health Information Model?

2 Nov 2011, CTRU Research Seminar

Koray Atalag, MD, PhD, FACHI

A look at clinical communication

• Clinicians usually understand each other when conveying information about patients, studies etc.

• Perhaps because:– Communication is a natural phenomenon– Common language (common training, experience, culture,

goals, universality of Medicine)– Moral responsibility, drive for success, money etc.

• It is a seamless human thing - involves greatest computer of all times – the Brain!

• We still don’t have a clue how this happens though

What’s the problem with IT here?

• We capture heaps of healthcare data - sit in silos• Partly structured and coded – depends on purpose

– eg ICD10, ICD-O, LOINC• Coding is not easy

– depends on context, purpose, or just coder’s mood!• Still wealth of valuable information in free text• Difficult to code from free text after capturing

– Usually context is lost• Ultimately we cannot link, share and reuse!

What are the implications?

• Apart from:– safety, quality, effectiveness and equity in healthcare– New knowledge discovery and advances in Science

• Cost of not sharing health information:– in the US could sum up to a net value of $77.8 billion/yr

(Walker J. The Value Of Health Care Information Exchange And Interoperability. Health Affairs 2005 Jan)

– In Australia well over AUD 2 billion(Sprivulis, P., Walker, J., Johnston, D. et al., "The Economic Benefits of Health Information Exchange Interoperability for Australia," Australian Health Review, Nov. 2007 31(4):531–39.)

If the banks can do it, why can’t health?

• Clinical data is wicked:– Breadth, depth and complexity

• >600,000 concepts, 1.2m relationships in SNOMED

– Variability of practice– Diversity in concepts and language– Conflicting evidence– Long term coverage– Links to others (e.g. family)– Peculiarities in privacy and security– Medico-legal issues– It IS critical…

Wickedness: Medication timing

Dose frequency Examplesevery time period …every 4 hours

n times per time period …three times per day

n per time period …2 per day…6 per week

every time period range

…every 4-6 hours, …2-3 times per day

Maximum interval …not less than every 8 hours

Maximum per time period

…to a maximum of 4 times per day

Acknowledgement: Sam Heard


Time specific ExamplesMorning and/or lunch and/or evening

…take after breakfast and lunch

Specific times of day 06:00, 12:00, 20:00

Dose durationTime period …via a syringe driver

over 4 hours



Event related ExamplesAfter/Before event …after meals

…before lying down…after each loose stool…after each nappy change

n time period before/after event

…3 days before travel

Duration n time period before/after event

…on days 5-10 after menstruation begins



Treatment duration

Examples

Date/time to date/time 1-7 January 2005

Now and then repeat after n time period/s

…start, repeat in 14 days

n time period/s …for 5 days

n doses …Take every 2 hours for 5 doses



Triggers/Outcomes

Examples

If condition is true …if pulse is greater than 80 …until bleeding stops

Start event …Start 3 days before travel

Finish event …Apply daily until day 21 of menstrual cycle


How do we model now?complex techy stuff

A new approach:

Open source specifications for representing health information and person-centric records– Based on 18+ years of international implementation experience

including Good European Health Record Project– 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

Key Innovations

• “Multi-level Modelling”– separation of health information representation into layers

1) Reference Model: Technical building blocks (generic)

2) Content Model: Archetypes & Templates (domain-specific)

3) Terminology: ICD, CDISC/CDASH, SNOMED etc.

Data exchange and software based on only the first layerArchetypes provide ‘semantics’ for mapping and GUI formsTerminology provides linkage to knowledge sources (e.g.

Publications, knowledge bases, ontologies)

Multi-Level Modelling in openEHR

Date and Time Handling in openEHR

Archetypes: Blueprints of Health Information

• Puts together RM building blocks to define clinically meaningful information (e.g. Blood pressure)

• Configures RM blocks• Structural constraints (List, table, tree)• What labels can be used• What data types can be used• What values are allowed for these data types• How many times a data item can exist?• Whether a particular data item is mandatory• Whether a selection is involved from a number of items/values

• They are maximal datasets–contain every possible item• Modelled by domain experts using visual tools

Clinicians in the Driver’s Seat!

Content Example:Blood Pressure Measurement

Blood Pressure MeasurementMeta-Data

Blood Pressure MeasurementData

Blood Pressure MeasurementPatient State

Blood Pressure MeasurementProtocol

Open Source Archetype Editor

Content Modelling in Action

Back in 2009 – GP view of BPWHAT HAVE WE MISSED?

Acknowledgement: Heather Leslie & Ian McNicoll

Blood pressure: CKM review


Blood pressure: CKM review


…additional input from other clinical settings

Blood Pressure v2


…and researchers

Blood Pressure v3


CKM: Versioning


CKM: Discussions

Blood pressure: Translation


How do they all fit together?(to share and reuse data)

• Common RM blocks ensure data compatibility– No need for type conversions, enumerations, coding etc.

• Common Archetypes ensure semantic consistency– when a data exchange contains blood pressure

measurement data or lab result etc. it is guaranteed to mean the same thing.

– Additional consistency through terminology linkage• Common health information patterns and

organisation provide ‘canonical’ representation– All similar bits of information go into right buckets

• Addresses provenance and medico-legal issues

Patterns in Health Information

Actions

Published evidence base

Personal knowledge

Evaluation

Observations

Subject

Instructions Investigator’s agents

(e.g. Nurses, technicians, other physicians or automated devices)

Clinicianmeasurable or

observable

clinically interpreted findings

order or initiation of a workflow process

Recording data for each activity

Administrative Entry

A Simple Health InformationOrganisation

Compositions

EHR

Folders

Sections

Clusters

Elements

Data values

Entries

Achievable?

• 4̴ 10-20 archetypes core clinical information to ‘save a life’

• 4̴ 100 archetypes primary care

• 4̴ 2000 archetypes secondary care– [compared to >600,000 concepts

in SNOMED]

Achievable? 2

• Initial core clinical content is common to all disciplines and will be re-used by other specialist colleges and groups– Online archetype consensus in CKM– Achieved in weeks/archetype– Minimises need for F2F meetings– Multiple archetype reviews run in parallel

• Leverage existing and ongoing international work


Can clinicians agree on single definitions of concepts?

• “What is a heart attack?”– “5 clinicians, potentially >1 answer” – probably more!

• “What is an issue vs. problem vs. diagnosis?”– No consensus for conceptual definition for years!

BUT• There is generally agreement on the structure and

attributes of information to be captured Problem/Diagnosis

name Status Date of initial onset Age at initial onset Severity Clinical description

Date clinically recognised

Anatomical location Aetiology Occurrences Exacerbations Related problems

Date of Resolution Age at resolution Diagnostic criteria


Problem Archetype

Who’s using it for research?

• The Victorian Cancer Council– Transformed all their research data over the last 20 years

to an openEHR repository• SINTERO Project

– Wellcome Trust funded – at Cardiff Univ.– Gather data for diabetes from patients, devices and

hospital records– openEHR based repository to aggregate and query data

NZ Interoperability Architectureis underpinned by openEHR

Thanks... Questions?

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What if we never agree on a common health information model?