Evaluating Manual Mappings of Russian Proprietary Formats and Terminologies to FHIR.


Journal

Methods of information in medicine
ISSN: 2511-705X
Titre abrégé: Methods Inf Med
Pays: Germany
ID NLM: 0210453

Informations de publication

Date de publication:
Nov 2019
Historique:
pubmed: 15 3 2020
medline: 8 8 2020
entrez: 15 3 2020
Statut: ppublish

Résumé

 Evaluating potential data losses from mapping proprietary medical data formats to standards is essential for decision making. The article implements a method to evaluate the preliminary content overlap of proprietary medical formats, including national terminologies and Fast Healthcare Interoperability Resources (FHIR)-international medical standard.  Three types of mappings were evaluated in the article: proprietary format matched to FHIR, national terminologies matched to the FHIR mappings, and concepts from national terminologies matched to Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT). We matched attributes of the formats with FHIR definitions and calculated content overlap.  The article reports the results of a manual mapping between a proprietary medical format and the FHIR standard. The following results were obtained: 81% of content overlap for the proprietary format to FHIR mapping, 88% of content overlap for the national terminologies to FHIR mapping, and 98.6% of concepts matching can be reached from national terminologies to SNOMED CT mapping. Twenty tables from the proprietary format and 20 dictionaries were matched with FHIR resources; nine dictionaries were matched with SNOMED CT concepts.  Mapping medical formats is a challenge. The obtained overlaps are promising in comparison with the investigated results. The study showed that standardization of data exchange between proprietary formats and FHIR is possible in Russia, and national terminologies can be used in FHIR-based information systems.

Sections du résumé

BACKGROUND BACKGROUND
 Evaluating potential data losses from mapping proprietary medical data formats to standards is essential for decision making. The article implements a method to evaluate the preliminary content overlap of proprietary medical formats, including national terminologies and Fast Healthcare Interoperability Resources (FHIR)-international medical standard.
METHODS METHODS
 Three types of mappings were evaluated in the article: proprietary format matched to FHIR, national terminologies matched to the FHIR mappings, and concepts from national terminologies matched to Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT). We matched attributes of the formats with FHIR definitions and calculated content overlap.
RESULTS RESULTS
 The article reports the results of a manual mapping between a proprietary medical format and the FHIR standard. The following results were obtained: 81% of content overlap for the proprietary format to FHIR mapping, 88% of content overlap for the national terminologies to FHIR mapping, and 98.6% of concepts matching can be reached from national terminologies to SNOMED CT mapping. Twenty tables from the proprietary format and 20 dictionaries were matched with FHIR resources; nine dictionaries were matched with SNOMED CT concepts.
CONCLUSION CONCLUSIONS
 Mapping medical formats is a challenge. The obtained overlaps are promising in comparison with the investigated results. The study showed that standardization of data exchange between proprietary formats and FHIR is possible in Russia, and national terminologies can be used in FHIR-based information systems.

Identifiants

pubmed: 32170719
doi: 10.1055/s-0040-1702154
doi:

Types de publication

Evaluation Study Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

151-159

Subventions

Organisme : 18-37-20002
ID : Russian Fund for Basic research

Informations de copyright

Georg Thieme Verlag KG Stuttgart · New York.

Déclaration de conflit d'intérêts

None declared.

Auteurs

Iuliia D Lenivtceva (ID)

National Center for Cognitive Technologies, ITMO University, Saint Petersburg, Russia.

Georgy Kopanitsa (G)

National Center for Cognitive Technologies, ITMO University, Saint Petersburg, Russia.

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