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Related Experiment Video

Updated: Jun 8, 2026

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases
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Published on: March 19, 2018

Ensuring HL7-based information model requirements within an ontology framework.

David Ouagne1, Nadia Nadah, Daniel Schober

  • 1Université René Descartes, Paris, F-75006 France. david.ouagne@crc.jussieu.fr

Studies in Health Technology and Informatics
|September 16, 2010
PubMed
Summary
This summary is machine-generated.

This study built an HL7-based Information Model Ontology (IMO) to enable distributed querying across clinical data repositories. The IMO mediates between complex queries and less-defined Hospital Information Systems (HIS).

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Last Updated: Jun 8, 2026

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

Area of Science:

  • Medical Informatics
  • Ontology Engineering
  • Clinical Data Management

Background:

  • Hospital Information Systems (HIS) often lack semantic clarity, hindering effective data querying.
  • Distributing queries across diverse clinical data repositories presents significant technical challenges.
  • Integrating domain-specific knowledge with clinical data is crucial for advanced medical research and disease control.

Purpose of the Study:

  • To develop an HL7-based Information Model Ontology (IMO) for mediating ontological queries.
  • To enable distributed querying over heterogeneous clinical data repositories.
  • To support projects like DebugIT in controlling infectious diseases and antimicrobial resistance.

Main Methods:

  • Employed the Open Medical Development Framework (OMDF) utilizing a model-driven development methodology.
  • Utilized OMDF's model transformation features to construct an HL7-based information model.
  • Integrated the Information Model Ontology with a domain ontology for query expression.

Main Results:

  • Successfully built an HL7-based Information Model Ontology (IMO).
  • The IMO effectively mediates between ontological queries and information retrieval from semantically less defined HIS.
  • Demonstrated the utility of the IMO within the DebugIT project for infectious disease and antimicrobial resistance control.

Conclusions:

  • The developed HL7-based IMO facilitates distributed querying across clinical data repositories.
  • This approach enhances data interoperability between ontologically-driven queries and existing HIS.
  • The IMO serves as a valuable tool for projects requiring integrated clinical data analysis, such as infectious disease surveillance.