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Content Modelling for VIEW Datasets Using Archetypes
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Content Modelling for VIEW Datasets Using Archetypes



Koray Atalag

Koray Atalag
National Institute for Health Innovation
University of Auckland
(Friday, 10.35am, Sigma Room)



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  • Published by HISO (2012); Part of the Reference Architecture for Interoperability“To create a uniform model of health information to be reused by different eHealth Projects involving HIE”Consistent, Extensible, Interoperable and Future-Proof Data

Content Modelling for VIEW Datasets Using Archetypes Content Modelling for VIEW Datasets Using Archetypes Presentation Transcript

  • Content Modelling for VIEW Datasets Using Archetypes Koray Atalag1, Jim Warren1,2, Rod Jackson21.NIHI – University of Auckland2.Department of Computer Science – University of Auckland3.School of Population Health – University of Auckland
  • What’s VIEW• Smart (!) name  Vascular Informatics using Epidemiology & the Web (VIEW)• Building on PREDICT CVD-DM (primary care) – Extending to secondary (acute Predict) – Improving risk prediction models – Creating a variation map/atlas of NZ• Data linkages to: – National Mortality Register, – National Minimum Dataset (public and private hospital discharges) – National Pharmaceutical Collection (drugs dispensed from community pharmacies with government subsidy) – National PHO Enrolment Collection – Auckland regional CVD-relevant laboratory data from DML – TestSafe (in progress)
  • Objectives of this study• Extend existing data management capabilities; – Define a canonical information model (openEHR) – Normalise and link external datasets – Ability to extend without compromising backward data compatibility – future-proofing• Create a state-of-the-art research data repository – Transform existing datasets into full-EHR records – Data integration using model as map – Powerful semantic querying & stata on the fly
  • Archetypes• Smallest indivisible units of clinical information – Preserving clinical context – maximal datasets for given concept• Brings together building blocks from Reference Model• Puts constraints on them: – Structural constraints (List, table, tree, clusters) – 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
  • Logical building blocks of EHREHRFoldersCompositionsSectionsEntriesClustersElementsData values
  • Example Model:Blood Pressure Measurement
  • BP Measurement Archetype
  • PREDICT Dataset Definitions
  • Current Model
  • NZ Extension
  • Results• Archetype based content model can faithfully represent PREDICT dataset• Modelling: – two new archetypes‘Lifestyle Management’ and ‘Diabetic Glycaemic Control’ checklists – NZ extensions for demographics (DHB catchment, meshblock/domicile, geocode NZDep)• Difficulty: overlap between openEHR and NEHTA repositories – different archetypes for tobacco use, laboratory results and diagnosis which we reused – Considering both repositories are evolving separately it is challenging to make definitive modelling decisions.
  • Potential Benefits• High level of interoperability and increased data linkage ability• Important for research data sharing• Can sync more frequently (even real-time!)• Can leverage biomedical ontologies (through Archetype terminology bindings and service)• Can perform complex and fast queries on clinical data (real-time decision support)
  • Bigger Picture• Interoperability for clinical information systems – great – But what about population health & research?• Research data also sits in silos – mostly C Drives or even worse in memory sticks!• Difficult to reuse beyond specific research purpose – clinical context usually lost• No rigour in handling and sharing of data
  • Shared Health Information Platform (SHIP)
  • VIEW extensions  ECM
  • Working Principle Exchange Content Model Conforms to Message Payload (CDA) Source System Recipient System Map Map Source to Web Service ECM to ECM Recipient Exchange Data ObjectSource data Recipient data
  • Questions / Further Info Koray Atalag, MD, PhD, FACHI k.atalag@nihi.auckland.ac.nz