Medical informatics and Electronic Health Records

The modelling standards described above include metadata that provides the biological and biophysical meaning to the mathematical terms in the models. To use these models as descriptions of physiological phenotypes in order to interpret clinical measurements, we need to map the CellML and FieldML model metadata to Electronic Health Records (EHRs) and in particular the openEHR standard (www.openehr.org). An exploratory project is now underway using openEHR Archetypes (www.openehr.org/programs/clinicalmodels) to achieve this mapping. Implementation of modelling workflows with clinical data in a hospital setting is being implemented via the VPH-Share project (www.vph-share.eu).

The objective is to represent measurement data and associated clinical information using Archetypes and then map to CellML and FieldML parameters for model validation and to create next generation of personalised and predictive decision support tools at the bedside.

Archetypes allow for capturing of clinical data and context as an indivisible whole so that the exact meaning can be preserved and safely acted upon across different systems. The following example illustrates the blood pressure measurement archetype comprising:

  1. Data: holding the actual measurement data (e.g. systolic and diastolic blood pressure in mmHg with max and min allowable values)
  2. Protocol: holding protocol of the measurement such as cuff size (e.g. adult, child)
  3. State: holding state information such as patient position (e.g. lying, sitting)
  4. Events: depicting whether it is a one off measurement or 24 hour average
A diagram showing the archetypes and their sub-classifications in openEHR.
By using shared biomedical ontologies (e.g. FMA, Gene Ontology) and clinical terminology (e.g. SNOMED CT, LOINC) to annotate both computational and clinical models, automated reasoning and new knowledge discovery will be possible which will ultimately help reach the goals of the Physiome Project. The project will extend PMR to extract and store archetype meta-data which will allow searching of both models and associated clinical data.

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