Diagnosis, misdiagnosis, lucky guess, hearsay, and more: an ontological analysis

Disease and diagnosis have been the subject of much ontological inquiry. However, the insights gained therein have not yet been well enough applied to the study, management, and improvement of data quality in electronic health records (EHR) and administrative systems. Data in these systems suffer from workarounds clinicians are forced to apply due to limitations in the current state-of-the art in system design which ignore the various types of entities that diagnoses as information content entities can be and are about. This leads to difficulties in distinguishing amongst diagnostic assertions misdiagnosis from correct diagnosis, and the former from coincidentally correct statements about disease.

We applied recent advances in the ontological understanding of the aboutness relation to the problem of diagnosis and disease as defined by the Ontology for General Medical Science. We created six scenarios that we analyzed using the method of Referent Tracking to identify all the entities and their relationships which must be present for each scenario to hold true. We discovered deficiencies in existing ontological definitions and proposed revisions of them to account for the improved understanding that resulted from our analysis.

Our key result is that a diagnosis is an information content entity (ICE) whose concretization(s) are typically about a configuration in which there exists a disease that inheres in an organism and instantiates a certain type (e.g., hypertension). Misdiagnoses are ICEs whose concretizations succeed in aboutness on the level of reference for individual entities and types (the organism and the disease), but fail in aboutness on the level of compound expression (i.e., there is no configuration that corresponds in total with what is asserted). Provenance of diagnoses as concretizations is critical to distinguishing them from lucky guesses, hearsay, and justified layperson belief.

Recent improvements in our understanding of aboutness significantly improved our understanding of the ontology of diagnosis and related information content entities, which in turn opens new perspectives for the implementation of data capture methods in EHR and other systems to allow diagnostic assertions to be captured with less ambiguity.