Semantic Interoperability in Digital Health: From Terminologies to Ontologies

Semantic interoperability refers to the ability of computer systems to exchange data with unambiguous, shared meaning, not merely at the level of data formatting but at the level of meaning itself. It goes beyond syntactic interoperability—the correct structuring or packaging of data—by ensuring that the receiving system interprets the data exactly as intended by the sender, regardless of platform or locale. This involves linking each data element to a controlled shared vocabulary or ontology so that meaning travels with the data. In healthcare, that might mean encoding a symptom, diagnosis or medication using an agreed concept rather than free text or local codes.

Only when semantics are preserved can systems perform logical reasoning, inferencing, or data federation across organisations. Without this, data remains siloed and subject to misinterpretation—leading to wasted effort, clinical errors, or flawed analytics. Semantic interoperability represents the highest level of interoperability and is essential for next‑generation digital health capabilities like AI‑driven insights, decision support and federated research across institutions.

The Role of Controlled Terminologies

A controlled terminology is the foundation of semantic interoperability. These standardised vocabularies—such as SNOMED CT, LOINC, ICD‑10, and proprietary systems like MEDCIN—provide coded terms with precise definitions and relationships.

SNOMED CT, for instance, is one of the most comprehensive multilingual clinical healthcare terminologies, containing hundreds of thousands of concepts with relationships, synonyms and hierarchies. It enables consistent indexing, retrieval and aggregation of clinical data, supporting encoding of symptoms, diagnoses, procedures, substances and more.

MEDCIN is another example: a point‑of‑care terminology with hundreds of thousands of clinical elements, engineered to map into SNOMED CT, ICD, LOINC, CPT and other coding systems—thereby enabling interoperability when integrated into EHR systems.

Controlled terminologies ensure that data is encoded with measurable meaning, reducing ambiguity caused by synonyms, regional variations or free‑text entry. When terminology is aligned across systems via agreed concept codes, semantic interoperability becomes possible.

Ontologies: Elevating Terminologies into Knowledge Representations

Terminologies are excellent for encoding concepts, but ontologies take interoperability further by formalising relationships and definitions using logic. An ontology is a structured representation of domain knowledge: classes, relations, constraints and axioms.

Key examples include:

• Foundation ontologies or upper ontologies that define primitive, domain‑agnostic concepts.
• Domain ontologies that build on top of foundational ones to model healthcare‑specific knowledge.
• Large‑scale biomedical ontology initiatives that promote shared principles such as common formats and documented relations.

Through ontologies, systems can perform inference—for example, deducing that a “viral upper respiratory tract infection” is a subclass of pneumonia, or that a particular concept implies causative agent and anatomical site relationships. Description logics underpin this inference capability, using a formal syntax to enable reasoning and automated classification.

Ontologies also allow mapping disparate terminologies together. A canonical ontology can reference codes from local EHR models and classical terminologies, enriched with path‑mapping axioms to align structural differences across proprietary systems.

Bridging Terminologies and Ontologies: Technical Challenges and Architecture

Bridging controlled terminologies and formal ontologies presents technical challenges that require careful architecture. These entail:

• Mapping and alignment between local or legacy terminologies and standard codes via ontology mappings.
• Creating or employing canonical models whose concepts sit above varied local structures.
• Using modular ontology design to reflect clinical modules like diagnoses, procedures or observations.

A common approach involves a module reading a diagnosis statement in native format, mapping it to a canonical ontology term, then applying logic to translate it into SNOMED CT or equivalent. Path mapping axioms handle structural misalignments: for instance, mapping different ways of capturing laterality or severity into a single defined ontology expression.

Modern approaches also explore automated mapping tools, which align observational models with biomedical ontologies. At scale, such mappings can cover tens of thousands of conditions and support phenotype discovery across multiple hospitals.

This layered architecture—legacy input → mapping module → canonical ontology → target terminology—supports scalable semantic interoperability across diverse EHRs.

Implementing Semantic Interoperability in Practice

Implementation demands both technical precision and governance. Successful semantic interoperability systems typically include:

• An agreed controlled vocabulary layer (SNOMED CT, LOINC, etc.).
• An ontology layer overlaying vocabulary with formal relationships, axiomatically defined.
• Mapping engines or transformation pipelines to convert local codes or data structures into standardised models.
• Governance processes to maintain mappings, version control, and documentation.

Key implementation steps:

1. Select or develop a canonical ontology aligned with clinical and data model needs.
2. Build mapping tables or transformation logic linking local proprietary code systems to standard terms.
3. Incorporate logic inference engines using description logic to validate and enrich data interpretation.
4. Ensure high data quality, since terminologies only succeed when underlying data has integrity.
5. Operate versioning, authority control and audit trails to manage updates and evolution of terminologies and ontology definitions.

The Road Ahead: Emerging Trends and Future Directions

Semantic interoperability remains an active and evolving research domain. Some future‑focused developments include:

• Linking ontologies with AI and natural language processing, to annotate unstructured clinical text automatically and map findings into structured concepts.
• Embedding semantic models in knowledge graphs that integrate health and social care domains, strengthening continuity of care and personalisation.
• Greater adoption of commons‑based governance models, allowing open sharing and collective curation of semantic resources aligned with FAIR data principles.
• Enhanced synchronisation across standards such as FHIR, terminologies and ontology frameworks, emphasising alignment to improve patient outcomes.

As healthcare evolves—to include precision medicine, federated research, cross‑border data exchange and AI‑driven care—the ability of systems not just to exchange, but to truly interpret health data semantically, will become mission‑critical.

Concluding Thoughts

Semantic interoperability in digital health moves us from merely transferring data to enabling meaningful communication between systems. Controlled terminologies provide the essential vocabulary; ontologies elevate that vocabulary into formal, machine‑interpretable knowledge. Together they bridge structural differences, resolve ambiguity, and enable inference across diverse sources.

Technical implementations require mapping pipelines, canonical models, ontology logic engines and rigorous governance. The payoff: cleaner data, safer care, interoperable systems and a foundation for innovation.

By approaching interoperability as a deep semantic integration problem—not just messaging or formats—health systems can achieve genuine understanding of data, powering better care, analytics and innovation in the years ahead.