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This study introduces a new method to harmonize diverse healthcare data by focusing on underlying concepts. It enables consistent data mapping across standards like HL7 FHIR and OMOP, improving data integration.

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Area of Science:

  • Health Informatics
  • Data Science
  • Ontology Engineering

Background:

  • Healthcare data exhibits significant heterogeneity across different standards.
  • Existing data harmonization methods often struggle with context-dependent mappings.
  • There is a need for a concept-centric approach to reconcile data elements across health data standards.

Purpose of the Study:

  • To propose and validate a novel approach for harmonizing data elements across health data standards based on their underlying concepts.
  • To demonstrate the creation of reusable mappings between data elements from disparate health data standards.
  • To showcase the utility of concept-based mappings in data harmonization and querying.

Main Methods:

  • Developed a concept-centric approach involving concept identification, clustering, and mapping.
  • Utilized Simple Standard for Sharing Ontological Mappings (SSSOM) and Resource Description Framework (RDF) for mapping construction.
  • Applied the approach to five major health data standards (HL7 FHIR, OMOP, CDISC, Phenopackets, openEHR) across multiple domains and topics.
  • Analyzed 64 data elements, identified underlying concepts, and developed mappings.
  • Implemented three use cases to demonstrate the approach's effectiveness.

Main Results:

  • Successfully identified underlying concepts for 64 data elements across five health data standards.
  • Constructed reusable mappings between data elements based on identified concepts.
  • Demonstrated improved data harmonization and querying capabilities through three practical use cases.
  • Validated the approach's ability to overcome limitations of context-dependent mappings.

Conclusions:

  • The proposed concept-centric approach effectively harmonizes heterogeneous healthcare data elements.
  • This method facilitates the creation of robust and reusable mappings across diverse health data standards.
  • The approach offers valuable insights and practical solutions for health data mapping and integration challenges.