JMIR Publications

ABSTRACT

Background:

Interoperability has been a challenge for half a century. Led by an informatic view of the world, the quest for interoperability has evolved from typing and categorizing data, to building increasingly complex models. In parallel with the development of these models, the field of terminologies and ontologies emerged to refine granularity and introduce notions of hierarchy. Clinical data models and terminology systems are inherently limited. They vary in purpose, and their fixed categories shape and constrain representation, which inevitably leads to information loss.

Objective:

Despite these efforts, true semantic interoperability remains out of reach and meaning remains lacking. Achieving it is essential for effective data reuse but requires more than rich terminologies and standardized models. This article proposes a paradigm shift: by mimicking natural languages, we formalize a shared grammar that defines how clinical reality can be expressed in coherent, meaningful sentences, rather than fixing the boundaries of representable reality a priori.

Methods:

This work builds on a decade of semantic representation efforts following established frameworks to develop a formal descriptive grammar through expert-guided and consensus-based refinement.

Results:

This formal descriptive grammar offers a dynamic decentralized framework, enabling large scale representation without the need of a central authority. This ensures machine-readability and enables meaningful representation of evolving medical knowledge, adapting through ongoing use to accurately represent reality. It addresses limitations in representation such as negation, scalar values, uncertainty, temporality, and integrates external vocabularies.

Conclusions:

After years of fragmented attempts at capturing meaning, this radically different approach proposes a unifying, computable framework to consistently represent data across sources and use cases.