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Modelling clinical knowledge
- 1. Modelling clinical knowledge Dr Heather Leslie @omowizard
- 2. © Atomica Informatics The challenge We humans are biological animals. We are analog devices trapped in a digital world… We are compliant, flexible, tolerant. Photo credits: Unknown; Shvets Production; Pavel Danilyuk; Jonathan Borba: https://www.pexels.com The digital health challenge Yet we people have constructed a world of machines that requires us to be rigid, fixed, intolerant. Donald Norman. The Invisible Computer. 1998
- 3. “We’re still in the traveller’s cheque phase of interoperability…” © Atomica Informatics https://wildhealth.net.au/were-still-in-the-travellers-cheque-phase/
- 5. Data design • Chaotic • Inconsistent • Fragmented • Reactive • Proprietary Photo credit: Brian Hewitt © Atomica Informatics
- 6. The Information structure problem… • All clinical software programs, registries, messages… have a different underlying information structure OPTIONS: • *Transformation* between unique or unequal information structures OR • Common understanding of information - requiring shared or equal information models © Atomica Informatics
- 8. Reference Model Archetypes Templates Data dictionary International/ National resource ‘Little data’ patterns • Standardised • ‘Fit for use’ • Centrally governed Datasets • Clinical Documents • Messages • Forms • Minimum data sets openEHR infostructure © Atomica Informatics Technical rules
- 9. CLINICIANS model their clinical knowledge Beale T, Heard S. An ontology-based model of clinical information. Stud Health Technol Inform. 2007;129(Pt 1):760-4. © Atomica Informatics
- 10. What do we need to know about a patient? • Current situation • Current context • Plans for care • Activity status Support point of care • Health record • Dataflow • Exchange • Right person, info, place, time • Aggregation, abstraction • Research, Registries • Reporting, Population health • Decision support, guidelines • AI, Personalised medicine et al • Quality indicators/KPIs Support any context of use © Atomica Informatics © Atomica Informatics image: Flaticon.com
- 13. OBSERVATIONs © Atomica Informatics = Current/past situation • Historical record of observable phenomenon ▪ “the evidence” ▪ Point in time/Interval/Aggregate-based data ▪ State required for interpretation Examples • History-taking • Physical examination • Test results • Measurements • Questionnaire results • Assessments & evaluations
- 14. “NEW” addition to ontology ~2009 = Designed for reuse • Never standalone • Always nested within an ENTRY archetype CLUSTERs © Atomica Informatics Examples • Anatomical location • Relative anatomical location • Medical device • Media file • Clinical evidence • Dosage • Lab analyte result • Timing – daily • Timing – non-daily • Specimen • Service direction • Inspired oxygen Variations • Examination findings (family) • Imaging examination findings (family)
- 15. = Current context/status • Persistent data • Record once in an EHR, update over time… ▪ Includes ‘date started’, ‘date stopped’ etc EVALUATIONs © Atomica Informatics Examples • Problem/Diagnosis (summary) • Adverse reaction (summary) • Tobacco smoking summary • Family history (summary) • Living arrangement (summary) • Birth summary • Precaution • Contraindication • Obstetric summary (all pregnancies) Variations • Travel summary (one per trip) • Pregnancy summary (one per pregnancy) • Clinical synopsis (one per event/episode) • Goal
- 17. www.openEHR.org/CKM Clinical Knowledge Manager (CKM) © Atomica Informatics
- 18. International CKM snapshot Community • 2880 registered users • 105 countries • >1100 volunteer reviewers Archetypes – ~500 • 6000+ data points • 25% published • 31 languages Core published archetypes: • Service request • Adverse reaction risk • Tobacco, Alcohol use • Physical exam findings • Lab & Imaging result • Problem/Diagnosis • Procedure • Measurements • Vital signs ~16,000+ person hours (2019) © Atomica Informatics
- 19. Archetype reuse Leslie H, openEHR Archetype Use and Reuse Within Multilingual Clinical Data Sets: Case Study J Med Internet Res 2020;22(11). Open access from: https://www.jmir.org/2020/11/e23361 Most new data sets/templates ~70-85% reuse © Atomica Informatics
- 20. Case study 1: Maternal mortality surveillance • National epidemiology/public health focus • Transitioning from paper to standalone GIS • openEHR integration anticipated in future • 6 templates • 186 instances of 45 unique archetypes • 24 published • 2 in review • 7 draft • 12 new – all designed for international re-use • 465 exposed data elements • Significant re-use • Despite significantly novel clinical content • 33/45 archetypes = 73% • 26/45 published/near published = 58% • Cause of death/Death summary © Atomica Informatics
- 21. If FHIR resources were archetypes… © Atomica Informatics COMPOSITION OBSERVATION EVALUATION INSTRUCTION ACTION CLUSTER ADMIN CLINICAL DEMOGRAPHICS RM OTHER TOTAL 3 4 7 4 5 17 3 6 6 66 121 26 248 60 8 10 226 6 5 - - 589 FHIR
- 22. Case study 2: AU PCDQ project… https://confluence.csiro.au/display/PCDQFPhase2/Primary+Care+Data+Quality+Foundations+-+Phase+2 © Atomica Informatics Release 1 Release 2 Use case 1: Practice-to-practice transfer inside FHIR Profiles/ Implementation Guides
- 23. Primary care scope – R2 © Atomica Informatics
- 24. Progress… © Atomica Informatics Release 1 Release 2 Release 3 Increasing models Increasing detail Use case 2: Indigenous health check inside FHIR Profiles/IGs HL7AUbase Aged care
- 25. Primary Care Quality Indicators - $$$ PIP QI measures: 1. Proportion of patients with diabetes with a current HbA1c result 2. Proportion of patients with a smoking status 3. Proportion of patients with a weight classification 4. Proportion of patients aged 65 and over who were immunised against influenza 5. Proportion of patients with diabetes who were immunised against influenza 6. Proportion of patients with COPD who were immunised against influenza 7. Proportion of patients with an alcohol consumption status 8. Proportion of patients with the necessary risk factors assessed to enable CVD assessment 9. Proportion of female patients with an up-to-date cervical screening 10.Proportion of patients with diabetes with a blood pressure result. © Atomica Informatics
- 26. PIP documentation requirements • Age • Gender • Problem list • Diabetes • COPD • Chronic kidney disease • Family history • Familial hypercholesterolaemia • Measurements • Weight • Blood pressure measurement • Health habits • Smoking status • Alcohol consumption status • Prevention • Cervical screening status • Influenza immunisation • Lab test results • HBA1c test result • Total cholesterol • HDL cholesterol • ECG LVH © Atomica Informatics
- 27. Primary Care data ecosystem Point of care Medical/Nursing GP or Practice benchmarking PIP QI Primary Care data asset (NMDS) © Atomica Informatics
- 29. Aged care data ecosystem Point of care Medical/Nursing Facility benchmarking National Quality indicators Snapshot assessments/questionnaires Aged care data asset (NMDS) © Atomica Informatics
- 30. Example 1 - Medications Point of care data Medical/Nursing Facility benchmarking National Quality indicators • Up-to-date medication lists • Medication reviews Number of medications Use of antipsychotics/sedatives Aged care data asset (NMDS) © Atomica Informatics
- 31. Example 2 - Pressure sores Point of care data Medical/Nursing Facility benchmarking National Quality indicators • Physical examination • Wound examination Number of pressure sores Number of pressure sores per severity classification Aged care data asset (NMDS) ??Number of pressure sores ??Number of pressure sores per severity classification © Atomica Informatics
- 32. Exam of a pressure ulcer © Atomica Informatics
- 33. Example 3 - Falls risk Point of care data Medical/Nursing Facility benchmarking National Quality indicators ??Number of falls ??Number of falls resulting in a major injury • History of falls • Cognitive impairment, delirium • Posture, gait, muscular weakness • Urinary frequency, incontinence • Postural hypotension • Dizziness, fainting • Sedatives, antipsychotics • Visual impairment • Diagnoses – stroke, vertigo • Locomotion ??Incident reports Number of falls Number of falls resulting in a major injury Aged care data asset (NMDS) © Atomica Informatics
- 34. Falls risk QI concepts • History of falls • Diagnoses – stroke, vertigo • Sedatives, antipsychotics • Postural hypotension • Dizziness, fainting • Posture, gait, muscular weakness • Cognitive impairment, delirium • Urinary frequency, incontinence • Visual impairment • Locomotion Recording • Facility • Incident reports • Health record • Problem list • Medication list • Medication review • History taking • Physical examination findings • Cognition assessments • Vision/hearing assessments • BP measurement – postural drop • Nursing care plan • Continence • Mobility etc © Atomica Informatics
- 35. Contact Dr Heather Leslie Atomica Informatics heather.leslie@atomicainformatics.com @omowizard openEHR International @openEHR / @clinicalmodels openEHR CKM - https://www.openehr.org/ckm/ openEHR Discourse site - https://discourse.openehr.org/ © Atomica Informatics