1. ResearchEHR Use of semantic web technologies and archetypes for the description of EHRs Montserrat Robles, Jesualdo Tomás Fernández-Breis, Jose Alberto Maldonado, David Moner, Catalina Martínez-Costa, Diego Boscá, Marcos Menárguez-Tortosa EFMI STC 2010, Reykjavík June 3rd

2. ResearchEHR Providing a set of generic methods and tools for the capture, standardization, integration, description and dissemination of health related information Two main research streams – Data: archetypes to describe the semantics of legacy health data in a manner independent of the internal representation of data. – Knowledge: ontologies as formal representation of archetypes

3. Archetypes Dual model approach – Reference model (Information): stable and standard-specific – Archetype model (Knowledge): dynamic and generic Formal models of clinical concepts (i.e., heart rate pulse) – Structure + constraints Suitable for sharing and reuse Interoperability unit

4. ResearchEHR Overview

5. Data level Objective: use archetypes to describe the semantics of legacy health data in a manner independent of the particular data organization in the underlying data repositories. – Semantic enrichment: legacy data is described by using reference models and archetypes. – Normalization of legacy data: legacy data is transformed into structures compliant with the reference model. – It eases the use of ontological technologies (ontological level)

6. LinkEHR-Ed Software tool for the description and normalization of legacy data. Able to handle different reference models, expressed as XML schemas. – ISO/CEN EN13606, OpenEHR, HL7 CDA and CCR has been tested successfully

7. LinkEHR-Ed

8. Data normalization Existing data must be translated to be compliant with both reference models and archetypes: domains, labeling and structure. LinkEHR-Ed helps users to define mappings between data sources and archetypes. – Users define high level mappings – The tools generate low level transformation programs.

9. Mapping: define and generate High level mappings Instance of Low level mapping (Xquery script) Normalized data Target schema (archetype) Source data schema Data Compiler Reference model Constrains

10. Ontology Level The Semantic Web vision – Machines can understand and process all the data by making its meaning explicit through ontologies Formal semantic representation of clinical knowledge Semantics-powered systems – Poseacle Converter: Archetype Interoperability – ArchMS: Archetype Management System – ARCheck: Archetype Consistency checking – ArchForms: Automatic Generation of Applications

11. The Ontological Representation Semantic interpretation of the ISO 13606 and OpenEHR archetype and reference models. Set of EHR standards OWL ontologies http://miuras.inf.um.es/~researchehr/websvn/

12. ADL Parser Ontology Metamodel AOM Metamodel ADL Archetype OWL archetype AOM archetype modelOntology archetype model Archetypes for the Semantic Web ADL2OWL

13. Archetype Interoperability Architecture

14. Poseacle Converter http://miuras.inf.um.es/PoseacleConverter/

15. Archetype Management System ArchMS – EHR-standard independent – Annotations and searches based on our semantic models

16. Archetype Consistency Checking ARCheck http://miuras.inf.um.es/archeck Use of OWL reasoning to find invalid specializations

17. Automatic Applications from 13606 Archetypes ArchForms Compliant XML extracts Multiple archetypes Multiple devices

18. Conclusions Archetypes are used to construct a semantic layer over existing EHR based on Semantic Web technologies This is easen by upgrading already deployed systems in order to make them compatible with an EHR standard and a set of archetypes. Semantics-powered approaches can also facilitate the achievement of semantic interoperability by providing explicit meaning for data and knowledge

19. Thank you, questions? Montserrat Robles Universidad Politécnica de Valencia mrobles@fis.upv.es Jesualdo Tomás Fernández Breis Universidad de Murcia jfernand@um.es