The Importance of Terminologies and Ontologies in Healthcare: Standardization, Interoperability, and Artificial Intelligence Support

Discover how healthcare terminologies (SNOMED CT, LOINC) and ontologies (OBI, GO) enhance interoperability, patient safety, and AI in medicine. Learn key standards and implementation challenges.

Imagine a doctor needing to access a patient’s medical history from another city. If healthcare systems use different terminologies, data interpretation may be compromised, risking diagnosis and treatment. This lack of standardization directly impacts care quality, patient safety, and clinical decision-making.

In this article, we will explore how terminologies and ontologies structure healthcare information, ensuring interoperability, precise decision-making, and advancements in artificial intelligence (AI).

What Are Healthcare Terminologies and Why Are They Crucial?

Terminologies are standardized sets of terms used to describe clinical conditions, exams, procedures, and medications. They ensure different systems "speak the same language," enabling efficient data exchange among healthcare professionals and software.

Key Healthcare Terminologies: 

• SNOMED CT – Structures clinical concepts.

• LOINC – Standardizes laboratory and clinical tests.

• ICD-11 – International Classification of Diseases.

• ICHI – Classification of healthcare procedures.

• RxNorm – Standardizes medications.

  
  

Benefits of Using Terminologies: 

• Facilitates data exchange between electronic health systems.

• Improves data quality for research and epidemiological analysis. 

• Reduces interpretation errors and redundant records.

• Supports AI by providing structured, reliable data.

Ontologies in Healthcare: Organizing Knowledge Intelligently

While terminologies standardize terms, ontologies structure knowledge by defining relationships between concepts. This enables knowledge inference and supports clinical decision-making.

Examples of Healthcare Ontologies: 

• Ontology for Biomedical Investigations (OBI) – Represents biomedical research.

• Gene Ontology (GO) – Organizes gene and biological function data.

• Brazilian Drug Ontology (OBM) – Standardizes medication-related concepts in Brazil.

  
  

How Ontologies Enhance Interoperability:

• Improve semantic search, allowing systems to interpret concepts more deeply.

• Facilitate terminology integration, ensuring data standard compatibility.

• Boost AI by providing context for machine learning algorithms.

  
  

Challenges in Implementing Terminologies and Ontologies:

Despite their benefits, adoption faces challenges such as:

• Resistance from healthcare professionals unfamiliar with the concepts. 

• Technological limitations, especially in legacy systems.

• Mapping difficulties between different terminologies.

• The need for ongoing governance and data updates.

Best Practices to Overcome Barriers:

• Use terminology servers, such as FHIR Terminology Server.

• Apply semantic mapping techniques to ensure consistency between standards.

• Train healthcare professionals and developers on efficient terminology use.

• Adopt interoperability approaches based on FHIR + SNOMED CT + LOINC.

  
  

Conclusion

Standardization through terminologies and ontologies is essential for a more efficient and secure digital healthcare ecosystem. Beyond improving system communication, it enhances care quality, advances research, and enables cutting-edge AI solutions.

Successful implementation depends on collaboration among healthcare professionals, data scientists, and IT developers. Only through a coordinated, strategic approach can we advance healthcare digitalization and ensure the best outcomes for patients and institutions.

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