Semantic Difficulties in FHIR ‘Conditions’

One goal of the Fast Healthcare Interoperability Resources (FHIR) standard is to prevent semantic ambiguities when patient data are electronically exchanged. To assess whether the FHIR specifications live up to this expectation, we examined FHIR's Condition resource thereby focusing on the resource elements 'clinical status' and 'verification status' when used in combination. We found that the definitions for these elements, as well as of several of their allowed values, suffer from the following semantic difficulties: the use of disjunctive descriptions, the presence of pseudo-synonyms lacking clear explanation of what distinctions FHIR has in mind, and insufficient discrimination between evidence and what such evidence would be about.

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Publication Details

Journal
Studies in health technology and informatics
Published
2025-05-15
DOI
https://doi.org/10.3233/shti250263
Citations
2
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
9.48

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article

Semantic Difficulties in FHIR ‘Conditions’

Werner Ceusters, Michael Rabenberg
2 citations
Studies in health technology and informatics
Natural Language Processing Techniques
9.48
article

Semantic Difficulties in FHIR ‘Conditions’

Werner Ceusters, Michael Rabenberg
article en
2 citations

Abstract

One goal of the Fast Healthcare Interoperability Resources (FHIR) standard is to prevent semantic ambiguities when patient data are electronically exchanged. To assess whether the FHIR specifications live up to this expectation, we examined FHIR's Condition resource thereby focusing on the resource elements 'clinical status' and 'verification status' when used in combination. We found that the definitions for these elements, as well as of several of their allowed values, suffer from the following semantic difficulties: the use of disjunctive descriptions, the presence of pseudo-synonyms lacking clear explanation of what distinctions FHIR has in mind, and insufficient discrimination between evidence and what such evidence would be about.

Studies in health technology and informaticsVol. 327
University at Buffalo, State University of New York (US)
National Institutes of Health, National Center for Advancing Translational Sciences
Openalex Percentile: Top 3%
Natural Language Processing Techniques
9.48
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