Content Modelling for VIEW Datasets Using Archetypes

Koray Atalag1, Jim Warren1,2, Rod Jackson2 1.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 EHR

Compositions

EHR

Folders

Sections

Clusters

Elements

Data values

Entries

Example Model:Blood Pressure Measurement

BP Measurement Archetype

PREDICT Dataset Definitions

Current Model

NZ Extens

ion

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

Source System Recipient System

Source data Recipient data

Exchange Content Model

Conforms to

Map Source to

ECM

Map ECM to

Recipient

Message Payload (CDA)

Exchange Data

Object

Web Service

Working Principle

Questions / Further Info

Koray Atalag, MD, PhD, FACHIk.atalag@nihi.auckland.ac.nz