Efforts towards standardization of biomedical terminology and electronic healthcare records (EHCR) were focused so far primarily on syntax. Semantic standardization is restricted to terminological issues around the concept-based semantic triangle paradigm and to knowledge representation that rests on set-theoretic model theory, description logics being the most prominent example. However, without applying a consistent ontological theory in addition to syntax and model checking, information exchange amongst patient record systems leads in the worst case to messages that are grammatically correct, but contain pure nonsense, or in the best case to messages that contain mere believes from the part of the sender, i.e. are consistent with his own model of the world, but cannot be checked whether and in what way that model corresponds to the real world.

How, for instance, must a receiving EHCR interpret an incoming (ICD-10 coded) diagnosis such as B83.9: Helminthiasis, unspecified? On what grounds should it assume that this does not refer to a disease caused by a worm belonging to the species Unspecified which would be some sub-species of Acanthocephalia or Metastrongylia, but refers to a statement made by a physician who for whatever reason did not specify the actual type of Helminth the patient was suffering from? Neither OWL nor reasoners using models expressed in OWL would complain about making the class B83.9: Helminthiasis, unspecified a subclass of B83: Other helminthiasis; from the point of view of a coherent ontology, however, such a view is nonsense: it rests precisely on a confusion between ontology and epistemology.

A similar confusion can be found in EHCR architectures, model specifications, message specifications or data types for EHCR systems in which one believes that merely agreeing on a number of XML-tags is sufficient for deep understanding. References to a patient’s gender/sex are a typical example. Some specifications, such as the Belgian KMEHR (Kind Messages for Electronic Healthcare Records), refer to it as administrative sex (leaving it to the reader of the specification to determine what this might actually mean). The possible values for administrative sex are then female, male, unknown, or changed. Unknown, here, does not refer to a new and special type of gender (reflecting some novel scientific discovery); rather it refers to the fact that the actual gender is not documented in the record.

However, an interpretation along these lines does not work in every case. Consider those specifications which refer explicitly to clinical observations, as is the case for Corbamed-COAS (Clinical Observations Access Server), which consists of:

any information that has been captured about a single patient’s medical/physical state and relevant context information. This [information] may be derived by instruments such as in the case of images, vital signs, and lab results or it may be derived by a health professional via direct examination of the patient and transcribed [sic]. This term applies to information that has been captured whether or not it has been reviewed by an appropriate authority to confirm its applicability to the patient record.

When in a EHCR system that claims to follow the COAS specifications the value unknown would be registered for gender, then that value has to be interpreted such that an observation has been made with respect to the patient’s gender, and that as a result of that, an unknown kind of gender has been observed. Of course, that is not supposed to be the idea.

We argue that there would be more value in reasoning using ontologies for biomedical information systems, when these ontologies are build in such a way that they give a veridical view on that portion of the world they intend to describe.