Modelling Taskforce Report. Dr Linda Bird 19 th January 2013. Agenda. Background Overview of modelling approach Laboratory Results Report Submitted models Comparative analysis Modelling patterns Clinical Models Terminology binding Demographics Model Future work and discussion.
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Taskforce Members • Technical Resources: • Peter Hendler • Galen Mulrooney • Daniel Karlsson • Cecil Lynch • Joey Coyle • Grahame Grieve • Dipak Kalra • David Moner • Clinical Modelling Resource: • William Goossen • Jay Lyle • Ian McNicoll • AnnekeGoossen • Heather Leslie • Sarah Ryan • Harold Solbrig • Marcelo Rodrigues dos Santos Core Members: • Linda Bird (chair) • Tom Beale • Dave Carlson • Stephen Chu • Stan Huff • Mike Lincoln • Rahil Qamar Siddiqui • Gerard Freriks • Josh Mandel • Mark Shafarman • Michael van der Zel Secretary: • Eithne Keelaghan
Mission To develop a CIMI modelling methodology, style guide and a set of models, which together demonstrate and test the approach to CIMI clinical modelling.
Terms of Reference This taskforce has been established to: • Further develop CIMI's modelling methodology; • Create an initial set of CIMI clinical models; • Further test and develop CIMI technical models, including: • CIMI reference model • Archetype Object Model 1.5, and • CIMI terminology.
Modelling Taskforce History (2012) May 10 - 12 Meeting in Pleasanton Modelling Taskforce was established May to Sep Taskforce infrastructure and planning; Modelling methodology; Observation modelling pattern; Heart rate model Sept 14 - 16 Meeting in Rockville Modelling priorities: Lab Results Report (followed by Immunizations and Body Temperature) Oct to Dec Replanning and review of submitted Lab Result models Design Lab Results model structure and modelling patterns Lab Results - Comparative analysis spreadsheet CIMI Terminology subgroup established October 2012 • Terminology binding use cases, requirements, options, proposal • Lab Results terminology semantic bindings • Lab Results terminology reference sets started • Terminology server requirements Review of CIMI Demographics Reference Model & model analysis Dec 2 – 4 Taskforce meeting in Groningen Lab Results comparative analysis, mindmaps, implementation, terminology binding, reference model, instances and planning
CIMI Modelling Layers COMPOSITION CLUSTER ENTRY SECTION Dispensed Medications GUI Laboratory Report Message Implementation Purpose Context Neonatal Blood Pressure in EHR Current Medication List in EHR G.P. Dispensed Medication Item Inpatient Discharge Summary Care Setting Context Home Blood Pressure Outpatient Clinic Current Medication List Dispensed Medication Item Full Blood Count Results Report Use Case Context Standing Blood Pressure Current Medication List Paediatric Medication Item Biochemistry Laboratory Results Report Specialty Context Neonatal Blood Pressure Cardiology Medication List Medication Item Laboratory Results Report Clinical Models Blood Pressure Medication List Material Entity, Observable, Action, Schedule, Address, Clinical Report, Event Summary Patterns Observation, Action Clinical List Reference Model
CIMI’s Modeling Approach • Modular for reusability • Composablefor meeting use-cases • Pattern-based for consistency • Maximal for completeness • Logical for implementation in multiple formats • Constraint-based to allow specialisation • Extensible for local requirements • Bound to terminologyfor isosemanticity & interoperability
Modelling Methodology • Foundations • CIMI Reference Model • Archetype Object Model • CIMI Modelling Patterns • CIMI Style Guide • Modelling Approach • Analyse clinical models submitted (with value sets) • Identify maximal set of data elements • Remove ‘out of scope’ data elements (Style Guide) • Select appropriate CIMI Modelling Patterns(Style Guide) • Define CIMI model (Mindmap, ADL, UML) • Add Terminology bindings • Meaning (nodes, node relationships) • Value sets (maximal set from submitted models) • Add Example Model Data Instances • Technical Validation • ADL, UML • Clinical Validation / Review • Confirm mappings from submitted models
CIMI Laboratory Results Report Design Lab Results Report (Composition) 1 Lab Report Header (Entry) 0..1 Patient Encounter Summary (Entry) 0..* Lab Test Request Summary (Entry) 0..* Lab Test Result (Entry) 0..* Specimen (Cluster) 0..* Test Result Group/Item (Cluster) 0..* Specimen (Cluster) 0..* Reference Range (Cluster)
Identify maximal set of data elements • Comparative analysis of submitted models is used to identify the maximal set of relevant data elements for each model (see spreadsheet)
Archetype Map for Laboratory Results Report Laboratory Results Report Composition: Laboratory Report Header Laboratory Test Observation Laboratory Test Request Summary Patient Encounter Summary Entry:
Archetype Map for Laboratory Results Report Laboratory Results Report Composition: Laboratory Report Header Laboratory Test Observation Laboratory Test Request Summary Patient Encounter Summary Entry: Laboratory Test Observable Cluster: Laboratory Test Result Group Action Laboratory Test Request Specimen Laboratory Test Observable Reference Range Action Action Action
Archetype Map for Laboratory Results Report Laboratory Results Report Composition: Laboratory Report Header Laboratory Test Observation Laboratory Test Request Summary Patient Encounter Summary Entry: Laboratory Test Observable Cluster: Laboratory Test Result Group Action Laboratory Test Request Specimen Laboratory Test Observable Reference Range Action Action Action
CLUSTER modelling patterns CLUSTER constrains constrains constrains Observable Material Entity constrains Finding Action constrains constrains constrains Specimen Laboratory Test Observable Specimen Collection Site Laboratory Test Result Group Laboratory Test Request Reference Range