Improving the 'Fitness for Purpose' of Common Data Models through Realism Based Ontology

Common data models are designed and built based on requirements that are aimed towards fitness for purpose. But when common data models are used as lenses through which reality is observed from the perspective according to which they are built, then they exhibit restrictions that distort such view. Realism-based ontology design, when done properly, does not have these limitations as its fitness for purpose is only determined by the degree to which reality is represented the way it is. Therefore, we can use the principles that realism-based ontologies adhere to, not only to design application ontologies serving some specific purpose, but also to assess whether and where common data models fall short in their representational adequacy and how they can be corrected. If a realism based ontological perspective on the portion of reality the some common data model is trying to represent is compared with the perspective of the common data model itself, it is possible to determine how the latter deviates from the former and to suggest solutions to correct the misrepresentations found. Applying this method to the common data model of the Observational Medical Outcomes Partnership, revealed two major categories of errors: one where relationships are restricted based on the constraints of the data model, and one where the representation of reality is oversimplified.