The University of Auckland
openEHR-DataManagement.pptx (1.31 MB)

Exploiting Electronic Health Record Standard openEHR to Manage Experimental Data in Computational Physiology

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posted on 2016-09-22, 10:36 authored by David NickersonDavid Nickerson, Koray AtalagKoray Atalag, Jonathan CooperJonathan Cooper, Gary MiramsGary Mirams, Geoff Williams
A presentation that I delivered at COMBINE 2016, mostly on behalf of Koray and Geoff.

In computational physiology, standards to express mathematical and anatomically based geometric models rely heavily on the XML suite of standards and the Semantic Web. The Physiome Model Repository (PMR) provides an infrastructure to manage models and personal workspaces using distributed version control as well as providing ontology-based semantic annotation and advanced semantic searching mechanisms (including a SPARQL endpoint). We have previously described how to link clinical data to this semantic pipeline using the open access electronic health record (EHR) standard, openEHR, such that both models and related clinical data could be discovered and retrieved. This is an important step forward for enabling translational research and creating personalised decision support tools for the computational physiology community. However there is no agreed formalism to manage the structure and semantics of experimental data (e.g. from a wet lab), nor one which supports the semantic linkage of such data to model resources. Yet a set of experimental data is the basis for model development and validation. Linking models and data is therefore vital, but is currently done manually or in an ad hoc process. We have explored the utilisation of the openEHR standard to manage experimental data. openEHR provides a generic model-based approach to data modelling, and a very flexible means to express, persist and query structured data. The main premise of openEHR is to be able to manage heterogeneous data without the need to build custom data models – reusable and modular models of information can instead be represented using high level tools (known as an Archetype) and can be persisted in this form and queried using an openEHR compliant backend system easily. This can simplify data management tasks for the computational physiology community and also enable semantic interoperability.