Open Access Article

Title: Patterns: a simple but expressive data modelling formalism

Authors: Tony Austin; Shanghua Sun; Nathan Lea; Yin Su Lim; Archana Tapuria; David Nguyen; Dipak Kalra

Addresses: Helicon Health, 97 Tottenham Court Road, London, W1T 4TP, UK ' Helicon Health, 97 Tottenham Court Road, London, W1T 4TP, UK ' CHIME, University College London, The Farr Institute of Health Informatics Research, 222 Euston Road, London NW1 2DA, UK ' CHIME, University College London, The Farr Institute of Health Informatics Research, 222 Euston Road, London NW1 2DA, UK ' Department of Primary Care and Public Health Sciences, King's College London, 3rd floor Addison House, Guy's Hospital, London, UK ' Helicon Health, 97 Tottenham Court Road, London W1T 4TP, UK ' CHIME, University College London, The Farr Institute of Health Informatics Research, 222 Euston Road, London NW1 2DA, UK

Abstract: The creation of a clinical application requires models that describe the structure of data in a way that can be displayed, exchanged and stored. A number of approaches for this have been proposed and are in widespread use. However, these are often complex and/or have shortcomings in the breadth of data that they are able to represent. The annotations facility provided by many computer languages could be used to include information shaping the development and run-time behaviour of a clinical application. If this were comprehensive, then annotations alone would be sufficient for conceptual modelling. A model for representing such annotations is presented and some examples shown and discussed. The paper concludes that such a formalism is simple to use while developing semantic concepts but is capable of representing information from many models simultaneously. It is well suited to the needs of clinical teams seeking consensus on the structure of records.

Keywords: database design; data models; conceptual modelling; electronic healthcare records; EHRs; EHR structure; semantic interoperability; archetypes; annotations; model-driven development; MDD; data modelling; patterns formalism; semantics; clinical application design; clinical applications; healthcare technology.

DOI: 10.1504/IJKEDM.2016.082078

International Journal of Knowledge Engineering and Data Mining, 2016 Vol.4 No.1, pp.74 - 92

Available online: 06 Feb 2017 *