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Related Experiment Video

Updated: Feb 2, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

OpenEHR modeling for genomics in clinical practice.

Cecilia Mascia¹, Paolo Uva¹, Simone Leo¹

¹Center for Advanced Studies, Research and Development in Sardinia (CRS4), Loc. Piscina Manna, Ed.1, 09010 Pula, CA, Italy.

International Journal of Medical Informatics

|November 10, 2018

Summary

Integrating genomic data into electronic health records is crucial for personalized medicine. This study introduces a novel openEHR archetype model for structured representation of genetic test results, enhancing healthcare informatics.

Keywords:

Electronic health record Genomics OpenEHR Structured data Variant calling

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Area of Science:

Genomic Data Integration
Healthcare Informatics
Personalized Medicine

Background:

High throughput sequencing in personalized medicine presents significant healthcare informatics challenges.
Electronic health records require robust methods to manage large, complex genomic data and its provenance.
Standardized data representation is essential for effective clinical use.

Purpose of the Study:

To present a solution for integrating genomic data into electronic health records using openEHR archetypes.
To establish a structured format for representing genetic test results within healthcare systems.
To address the challenges of data size, complexity, and provenance tracking in genomic data management.

Main Methods:

Utilized the Variant Call Format as the foundational format for genetic test results within openEHR.
Evaluated existing openEHR archetypes for extensibility and identified needs for new development.
Developed eleven new openEHR archetypes and specialized an existing one for genomic data representation.

Main Results:

Successfully developed and applied eleven new openEHR archetypes for genomic data.
Specialized an existing archetype to effectively represent genomic data, including its provenance.
Demonstrated applicability to rare genetic diseases and compared the approach with HL7 FHIR.

Conclusions:

The proposed model enables structured representation of genetic test results in health records.
Supports automated processing and clinical decision support through different abstraction levels.
Extensible via external references, ensuring data provenance tracking and adaptability to future changes.