Abstract:
Linking clinical data to computational physiology will enable real-world model validation as well as the possibility of personalised and population level predictive decision support tools. Electronic health records (EHR) embody quantifiable manifestations of genomic and environmental aspects that impact on biological systems when clinical data are structured. However data quality and semantic interoperability remains a major challenge in the world of EHRs. In the computational physiology domain recent attempts to enable semantic interoperability heavily rely on Semantic Web technologies and utilise ontology-based annotations (e.g. RICORDO) but a wealth of useful information and knowledge sits in EHRs where Semantic Web technologies have very limited use. openEHR provides a set of an open engineering specifications that provides a canonical health record architecture and open source tooling to support data collection and integration. Core openEHR specifications have also been adopted by ISO and CEN making it a full international standard which underpins many national programs and has multi-vendor implementations worldwide. Our work describes how to use openEHR to normalise, annotate and link clinical data with biophysical models by using openEHR Archetypes as semantic pointers to underlying clinical concepts in EHR.