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


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In this talk I will touch on some hard problems in health informatics around working with structured data and why we can’t link and reuse them with ease. The essence of the problem is that, while clinicians can perfectly understand each other, IT systems can’t. Traditional IT requires formally defined common terminology, meta-data, data and process definitions. While Medicine is mostly accepted as positive science, yet the great variation in the body of knowledge and practice is often seen as ‘Art’. Ignoring this bit, IT people tend to develop all-inclusive common information models (almost always too complex to implement) and expect everybody adhere to that. Clinicians love to do things a bit differently and of course don’t buy into that! Maybe they are right! Maybe we don’t have to agree on a uniform model at all. This is the basic assumption of the openEHR methodology which I will describe by giving clinical examples. The main premise of this approach is to effectively separate tasks of healthcare and technical professionals. Clinicians can easily define their information needs as they like using visual tools – called Archetypes which are essentially maximal data sets. These computable artefacts, built using a well defined set of technical building blocks, are then fed into the technical environment to integrate data or develop software. Lastly the free web based openEHR Clinical Knowledge Manager portal provides collaborative Archetype development and ensures semantic consistency among different models.

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

  1. 1. What If We Never Agree On A Common Health Information Model? 2 Nov 2011, CTRU Research Seminar Koray Atalag, MD, PhD, FACHI The National InstituteInformatics for Health Innovation
  2. 2. A look at clinicalcommunication• 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 
  3. 3. What’s the problem with IThere?• 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!
  4. 4. 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.)
  5. 5. If the banks can do it, why can’thealth?• 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…
  6. 6. Wickedness: Medication timing Dose frequency Examples every 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 …every 4-6 hours, range …2-3 times per day Maximum interval …not less than every 8 hours Maximum per time …to a maximum of 4 times period per day Acknowledgement: Sam Heard
  7. 7. Wickedness: Medication timing Time specific Examples Morning and/or lunch …take after breakfast and/or evening and lunch Specific times of day 06:00, 12:00, 20:00 Dose duration Time period …via a syringe driver over 4 hours Acknowledgement: Sam Heard
  8. 8. Wickedness: Medication timing Event related Examples After/Before event …after meals …before lying down …after each loose stool …after each nappy change n time period …3 days before travel before/after event Duration n time period …on days 5-10 after before/after event menstruation begins Acknowledgement: Sam Heard
  9. 9. Wickedness: Medication timing Treatment Examples duration Date/time to date/time 1-7 January 2005 Now and then repeat …start, repeat in 14 days after n time period/s n time period/s …for 5 days n doses …Take every 2 hours for 5 doses Acknowledgement: Sam Heard
  10. 10. Wickedness: Medication timing Triggers/Outco Examples mes 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 Acknowledgement: Sam Heard
  11. 11. How do we model now?complex techy stuff
  12. 12. 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
  13. 13. 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 layer Archetypes provide ‘semantics’ for mapping and GUI forms Terminology provides linkage to knowledge sources (e.g. Publications, knowledge bases, ontologies)
  14. 14. Multi-Level Modelling in openEHR
  15. 15. Date and Time Handling in openEHR
  16. 16. 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
  17. 17. Clinicians in the Driver’s Seat!
  18. 18. Content Example:Blood Pressure Measurement
  19. 19. Blood Pressure MeasurementMeta-Data
  20. 20. Blood Pressure MeasurementData
  21. 21. Blood Pressure MeasurementPatient State
  22. 22. Blood Pressure MeasurementProtocol
  23. 23. Open Source Archetype Editor
  24. 24. Content Modelling in Action Back in 2009 – GP view of BP WHAT HAVE WE MISSED? Acknowledgement: Heather Leslie & Ian McNicoll
  25. 25. Blood pressure: CKM review Acknowledgement: Heather Leslie & Ian McNicoll
  26. 26. Blood pressure: CKM review Acknowledgement: Heather Leslie & Ian McNicoll
  27. 27. Blood Pressure v2 …additional input from other clinical settings Acknowledgement: Heather Leslie & Ian McNicoll
  28. 28. Blood Pressure v3 …and researchers Acknowledgement: Heather Leslie & Ian McNicoll
  29. 29. CKM: Versioning Acknowledgement: Heather Leslie & Ian McNicoll
  30. 30. CKM: Discussions
  31. 31. Blood pressure: Translation Acknowledgement: Heather Leslie & Ian McNicoll
  32. 32. 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
  33. 33. Patterns in Health Information Observations Clinician measurable or Published observableevidence base Subject Personal Actions knowledge Recording data for each activity Evaluation clinically interpreted findings Administrative Investigator’s agents Entry Instructions order or initiation of a (e.g. Nurses, technicians, other physicians or workflow process automated devices)
  34. 34. A Simple Health InformationOrganisation EHR Folders Compositions Sections Entries Clusters Elements Data values
  35. 35. 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]
  36. 36. 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 Acknowledgement: Sam Heard
  37. 37. Can clinicians agree on singledefinitions 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  Clinical description  Exacerbations name  Date clinically  Related problems  Status recognised  Date of Resolution  Date of initial onset  Anatomical location  Age at resolution  Age at initial onset  Aetiology  Diagnostic criteria  Severity  Occurrences Acknowledgement: Sam Heard
  38. 38. Problem Archetype
  39. 39. 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
  40. 40. NZ Interoperability Architectureis underpinned by openEHR
  41. 41. Thanks... Questions? k.atalag@auckland.ac.nz If you are really interested in Health Informatics,consider attending HINZ. This years annual conference is in Auckland 23-25 November http://www.hinz.org.nz/page/conference