What if We Really Had a Silver Bullet to Deal with Health Information ?

What if We Really Had a Silver Bullet to Deal with Health Information?

1 Dec 2011, COMPASS Seminar

Koray Atalag, MD, PhD, FACHI

What’s the Problem with Health Information?

• We capture heaps of data - sit in silos • Partly structured and coded

– eg ICD10, ICD-O, READ, LOINC etc.

• Coding is not easy / expensive– Depends on context, purpose, or just coder’s mood!– Automated coding is not reliable

• Difficult to code from free text after capturing– Usually context is lost– Best at the time and place of data capture

• Still wealth of valuable information in free text• We cannot link, aggregate 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 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 dayn per time period …2 per day

…6 per weekevery 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

Wickedness: Medication timing

Time specific ExamplesMorning and/or lunch and/or evening

…take after breakfast and lunch

Specific times of day 06:00, 12:00, 20:00Dose durationTime period …via a syringe driver

over 4 hours

Acknowledgement: Sam Heard

Wickedness: Medication timing

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

Acknowledgement: Sam Heard

Wickedness: Medication timing

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 daysn doses …Take every 2 hours for 5

doses

Acknowledgement: Sam Heard

Wickedness: Medication timing

Triggers/Outcomes

Examples

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

Start event …Start 3 days before travelFinish event …Apply daily until day 21 of

menstrual cycle

Acknowledgement: Sam Heard

How Do We Model Now?Complex techy stuff

A New Approach:

Open source specifications for representing health information and person-centric records– Based on 20+ years of international experience including Good

European Health Record Project– Superset of ISO/CEN 13606 EHR standard

Not-for-profit organisation - established in 2001 www.openEHR.org

Separation of clinical and technical worlds*

• Big international community and research

Clinicians in the Driver’s Seat!

Key Innovation

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

1) Reference Model: Technical building blocks (generic)

2) Content Model: Archetypes (domain-specific)

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

Data exchange and software development based on first layerArchetypes provide ‘semantics’ + behaviour and GUITerminology provides linkage to knowledge sources

(e.g. Publications, knowledge bases, ontologies)

Multi-Level Modelling in openEHR

Date and Time Handling in openEHR

Archetypes: Models 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

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

Acknowledgement: Heather Leslie & Ian McNicoll

Blood pressure: CKM review

Acknowledgement: Heather Leslie & Ian McNicoll

…additional input from other clinical settings

Blood Pressure v2

Acknowledgement: Heather Leslie & Ian McNicoll

…and researchers

Blood Pressure v3

Acknowledgement: Heather Leslie & Ian McNicoll

CKM: Versioning

Acknowledgement: Heather Leslie & Ian McNicoll

CKM: Discussions

Blood Pressure: Translation

Acknowledgement: Heather Leslie & Ian McNicoll

How Do They All Fit Together?

• 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 a ‘canonical’ representation– All similar bits of information go into right buckets– Easy & accurate querying + aggregation for secondary use

• Addresses provenance and medico-legal issues

A Simple Health InformationOrganisation

Compositions

EHRFolders

Sections

Clusters

Elements

Data values

Entries

Patterns in Health Information

Actions

Published evidence base

Personal knowledge

Evaluation

Observations

Subject

InstructionsInvestigator’s agents(e.g. Nurses, technicians, other physicians or automated devices)

Clinician measurable or observable

clinically interpreted findings

order or initiation of a workflow process

Recording data for each activity

Administrative Entry

Specialisation of Archetypes

Data conforms %100 to parent archetype International -> national -> regional -> local Generalist -> specialist -> subspecialist

Problem

Diagnosis

Diabetesdiagnosis

Text or Term• Clinical description• Date of onset• Date of resolution• Side• No of occurrences

Term +• Grading• Diagnostic criteria• Stage

Term+• Diagnostic criteria

• Fasting > 6.1• GTT 2hr > 11.1• Random > 11.1

Providing a Canonical RepresentationD

emog

raph

ics

Clin

ical

Enc

ount

er

Vita

l Sig

ns

Med

icat

ions

Dia

gnos

es

Dia

gnos

tic T

ests

Inte

rven

tions

Fam

ily H

isto

ry

Past

His

tory

Phys

ical

Exa

m

Gen

etic

s

Life

Sty

le

etc.

etc

. etc

.

Subject A

Subject B

Person-Centric Record Organisation

NZ AddressEthicity1,2.Whanau

USAddressStateNext of kin

GP visitFlu-likePHO enrolm.

Hospital adm.DiabetesPriv insurance

BP 130/90HR 90T: 38.5 C

BP 120/70 (24 hour avg)HR 70T: 37 C

Rx ADispenseAdminister

Rx BDispenseAdminister

Dx 1Dx 2etc.

Diabetes Dx-Type-Severity-Course etc.

Routine BloodUrineX-Ray

Specific blood testUrine cultureGenomic assayRetinography

Rx

Fluid TxInsuline injInfection TxPsychologic

N/A

Pedigree

N/A

Chronic

Routine

DetailedFoot and eyes

N/A N/A

DNA Seq.Assays

Low sugarExercise

Shared Archetypes

Each finding usually depends on other – clinical context matters!

Can Clinicians Agree on Single Definitions of Concepts?

• “What is a heart attack?”– 5 clinicians: ~2-3 answers – 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

Acknowledgement: Sam Heard

Achievable?

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

• <̴ 100 archetypes primary care

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

SNOMED]

Achievable? – cont.

• 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

Acknowledgement: Sam Heard

NZ Interoperability Architectureis underpinned by openEHR

Thanks... Questions?

[email protected]

Visit:www.openehr.org

Not a silver bullet, but definitely a good shot!