JMIR Publications

ABSTRACT

Background:

Clinical decision-making is inherently shaped by contextual factors that extend beyond guideline-based knowledge and objective clinical parameters. While clinical decision support systems (CDSS) aim to improve decision quality and safety, many existing systems insufficiently account for such contextual influences, limiting their effectiveness and acceptance. A prerequisite for context-sensitive CDSS is the systematic and standardized representation of context factors. However, the extent to which existing clinical terminologies are capable of representing these factors remains unclear.

Objective:

This study investigates the terminological representability of empirically identified context factors influencing clinical decision-making using SNOMED CT as semantic interoperability standard.

Methods:

The analysis builds on a previously established set of context concepts identified through a scoping review of empirical literature and organized within a hierarchical conceptual structure. An exploratory manual semantic mapping of these context concepts to SNOMED CT was conducted. Mapping quality was assessed using standardized equivalence classifications (Equal, Equivalent, Wider, Narrower, Inexact, Unmatched). An independent expert review and consensus process ensured harmonized mapping results. Context concepts not represented in SNOMED CT were additionally examined using alternative vocabularies via the OHDSI Athena platform.

Results:

In total, 189 context concepts were analyzed. Of these, 81% (n = 154) could be mapped to existing SNOMED CT concepts, while 19% (n = 35) showed no suitable terminological correspondence. Patient-related context concepts demonstrated the highest coverage (93%), whereas institution-related concepts showed substantially lower coverage (50%). Mapped concepts were distributed across eleven SNOMED CT domains, most frequently Observable entity and Qualifier value. Entity-specific analyses revealed differences in both terminological coverage and equivalence status. A majority of non-represented concepts could be identified in alternative vocabularies, primarily MeSH and LOINC, albeit often with inexact or partial correspondence.

Conclusions:

The findings indicate that a substantial proportion of empirically identified context factors influencing clinical decision-making can be terminologically represented in SNOMED CT, while relevant gaps persist, particularly for organizational and social context factors. These results highlight both the potential and the limitations of existing clinical terminologies for supporting context-sensitive CDSS and provide a foundation for future work on semantic standardization, ontology development, and the integration of standardized contextual information into CDSS.