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Electronic Health Record and Semantic Issues Using Fast Healthcare Interoperability Resources: Systematic Mapping

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Summary
This summary is machine-generated.

This study reviews 70 papers on Fast Healthcare Interoperability Resources (FHIR) semantic interoperability, classifying approaches like mapping, ML/NLP, and ontologies. Findings highlight the need for better tools and services to improve healthcare data exchange and interpretation.

Keywords:
EHRHL7 FHIRHealth Level Seven International Fast Healthcare Interoperability ResourcesMLNLPOWLRDFelectronic health recordinteroperability, web ontology languagemachine learningnatural language processingontologyresource description frameworksemanticterminology

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Area of Science:

  • Health Informatics
  • Computer Science

Background:

  • Interoperability issues in electronic health records (EHRs) and IoT hinder data exchange and interpretation.
  • Fast Healthcare Interoperability Resources (FHIR) standard is crucial for achieving semantic interoperability in healthcare.
  • Structuring and mapping clinical data to FHIR improves shared data quality.

Approach:

  • A systematic mapping review (SMR) analyzed 70 studies published between 2012 and 2022.
  • Studies focused on FHIR semantic interoperability resources and terminologies.
  • Approaches were inventoried and classified into 6 categories.

Key Points:

  • Mapping, terminology services, RDF/OWL, annotation, ML/NLP, and ontology-based proposals were identified.
  • Contributions included frameworks, architectures, models, techniques, services, and tools.
  • Framework and architecture proposals were the most frequent contributions.

Conclusions:

  • FHIR facilitates semantic interoperability through data collection, annotation, modeling, and mapping.
  • ML and NLP show promise for unstructured data, but require terminology services and automation tools.
  • Automated annotation and ontology comparison tools can reduce human effort and accelerate adoption.