Content Modelling for VIEW Datasets Using Archetypes

Abstract

Use of health information for multiple purposes maximises its value. A good example is PREDICT, a clinical decision support system which has been used in New Zealand for a decade. Collected data are linked and enriched with a number of databases, including national collections, laboratory tests and pharmacy dispensing. We are proposing a new model-driven approach for data management based on openEHR Archetypes for the purpose of improving data linkage and future-proofing of data. The study looks at feasibility of building a content model for PREDICT - a methodology underpinning the Interoperability Reference Architecture. The main premise of the content model will be to provide a canonical model of health information which will be used to align incoming data from other data sources. With this approach it is possible to extend datasets without breaking semantics over long periods of time – a valuable capability for research. The content model was developed using existing archetypes from openEHR and NEHTA repositories. Except for two checklist type items, reused archetypes can faithfully represent the whole PREDICT dataset. The study also revealed we will need New Zealand specific extensions for demographic data. Use of archetype based content modelling can improve secondary use of clinical data.