What if We Really Had a Silver Bullet to Deal with Health Information ?
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Transcript of 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?
Visit:www.openehr.org
Not a silver bullet, but definitely a good shot!