OpenEHR modeling for genomics in clinical practice. (December 2018)

• Record Type: Journal Article
• Authors: Mascia, Cecilia; Uva, Paolo; Leo, Simone; Zanetti, Gianluigi
• Is Part Of: International journal of medical informatics. Volume 120(2018)
• Page Start: 147
• Page End: 156
• Publication Date: 2018-12
• Subjects: Genomics -- OpenEHR -- Structured data -- Electronic health record -- Variant calling; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2018.10.007
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: archetype models used to structure the data ensure machine readability and computability. particular attention has been given to the preservation of the specific version of each versionable entity (e.g., tools, reference genome, etc.), which have been included in the model by linking external objects. we show an application of the models to a real use case and its compatibility with HL7 FHIR standard. Abstract: Purpose: The increasing usage of high throughput sequencing in personalized medicine brings new challenges to the realm of healthcare informatics. Patient records need to accommodate data of unprecedented size and complexity as well as keep track of their production process. In this work we present a solution for integrating genomic data into electronic health records via openEHR archetypes. Methods: We use the popular Variant Call Format as the base format to represent genetic test results within openEHR. We evaluate existing openEHR archetypes to determine what can be extended or specialized and what needs to be developed ex novo. Results: Eleven new archetypes have been developed, while an existing one has been specialized to represent genomic data. We show their applicability to rare genetic diseases and compare our approach to HL7 FHIR. Conclusion: The proposed model allows to represent genetic test results in health records in a structured format. It supports different levels of abstraction, allowing both automated processing and clinical decision support. It is extensible via external references, allowing to keep track of data provenance and adapt to future domain changes.