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Archetype-based data transformation with LinkEHR

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How can we convert data to standard data (EN ISO 13606, openEHR, HL7 CDA...) using archetypes? LinkEHR is a tool that helps in achieving this objective. …

How can we convert data to standard data (EN ISO 13606, openEHR, HL7 CDA...) using archetypes? LinkEHR is a tool that helps in achieving this objective.

This presentation was made at the "Arctic Conference on Dual-Model based Clinical Decision Support and Knowledge Management", that took place the 27th and 28th of May, 2014 in Tromsø, Norway.

Published in: Software, Technology, Education

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  • 1. LinkEHR Studio: a tool for archetype-based data transformations David Moner damoca@upv.es Biomedical Informatics Group (IBIME) ITACA Institute, Technical University of Valencia Arctic Conference on Dual-Model based Clinical Decision Support and Knowledge Management Tromsø, May 27th and 28th, 2014
  • 2. Model and data transformations • Transformations are a key element for the communication and reuse of clinical information. – Mainly for clinical research, but other uses are also possible. 2
  • 3. Model and data transformations 3
  • 4. Model and data transformations • Two types of transformations are needed to achieve a full semantic interoperability: 4 • Consists in transforming clinical information models or clinical patterns into archetypes, DCM, templates… • The objective is to ease the reuse of clinical information models Model transformations • Consists in transforming data instances from one format to another • The objective is to ease the reuse of data Data transformations
  • 5. Model transformations • Option 1: Direct transformation through ontologies and model-driven engineering – http://miuras.inf.um.es:9080/PoseacleConverter/ – Martínez-Costa C, et al., “An approach for the semantic interoperability of ISO EN 13606 and OpenEHR archetypes”, J Biomed Inform, 43(5)(2010) pp.736-746 • Option 2: Automatic generation from common, shared and generic clinical information models – This is the CIMI approach – http://informatics.mayo.edu/CIMI/index.php/Main_Page 5
  • 6. Data transformations • We can have models defined for several standards, more or less aligned or equivalent. • We can have data following those models, but also not normalized or legacy data. • Can we make data interoperable? 6 Yes, defining one-to-one mappings between different clinical information models for enabling data transformations
  • 7. 7 Source schema Target schema Transform script Standard data Instance of Instance ofGenerates Single level mapping Mapping Legacy data
  • 8. Single level mapping • There is a direct relationship between the instances and their schemas – It is “only” a matter of assigning a source path to a target path (maybe with some data operations). – There are lots of tools for doing this… 8 $SOURCE/temperature $TARGET/temperature
  • 9. Two level mapping • When we use a dual-model it becomes more complicated – The archetype defines a sub-schema that must be used during the mapping process. – We can generate an ad hoc schema, specific for each archetype, but this solution can potentially create maintenance and interoperability problems. 9
  • 10. Two level mapping 10 www.linkehr.com • LinkEHR Studio is a Reference Model- independent archetype tool. – It can define archetypes based on EN ISO 13606, openEHR, HL7 CDA, HL7 FHIR, CDISC ODM… – It is also a mapping and transformation-generator tool to convert existing data into archetype/RM compliant data.
  • 11. Two level mapping • LinkEHR Studio mapping functionality allows using directly archetypes as source or target schema. – It is a tool for EHR systems developers. • It generates an XQuery transformation program that can be used by any system that needs a conversion to/from archetyped data. – It works with XML data. 11
  • 12. 12 Source schema (Legacy model) Target schema (Reference model) Transform script Standard data Instance of Instance ofGenerates Two level mapping Case 1 Mapping Target archetype Compliant with Legacy data
  • 13. Two level mapping Case 1 • Transformation of legacy to RM instance according to an archetype definition. • Main problems solved – We have to map the archetype structure + the RM properties: we map a comprehensive archetype. – We need a function library for transformations: we use the XQuery function library and functions to gain access to the archetype metadata and terminologies. – We have to generate compliant data: the script checks all constraints of the archetype and the RM. – Data integration: aggregate data pertaining to the same patient. 13
  • 14. Two level mapping Case 1 • DEMO: The good ol’ blood pressure example 14
  • 15. Two level mapping Case 1 15 This is also applicable to HL7 CDA or even to the new FHIR model DEMO: from legacy data to HL7 CDA
  • 16. Two level mapping Case 2 16 Source schema (Reference model) Target schema (Reference model) Transform script Standard data Instance of Instance ofGenerates Mapping Target archetype Compliant with Standard data Source archetype Compliant with
  • 17. Two level mapping Case 2 • Transformation of archetyped data according to an RM to an RM instance according to a different archetype definition (of the same or different RM). • Main problems solved – Conversion of source archetype paths into RM- instance paths. – Mapping of data scattered among multiple archetypes. 17
  • 18. Two level mapping Case 2 • DEMO: from openEHR blood pressure to 13606. • DEMO: from openEHR problems to an HL7 CDA document. • DEMO: from HL7 CDA consultation note to openEHR. 18
  • 19. Integrating the transformation scripts in your systems • The script generated by LinkEHR is standard XQuery. – It can be executed by any XQuery engine at any point of the information system where a normalization process is needed. 19 Communication interface Health Information System External data format XQuery + Archetypes
  • 20. Use cases • Medication reconciliation between primary and secondary care (Hospital de Fuenlabrada, Madrid) – Active medication information has been normalized to a EN ISO 13606 data structure. Primary and secondary care clinicians reach a consensus on the data structure. – The final result was integrated into the hospital HIS (Siemens SELENE). – This project was received the 2009 National Health System Quality Award, by the Spanish Ministry of Health. 20
  • 21. Use cases 21
  • 22. Use cases • Nephrology information communication using HL7 CDA documents (Hospital Virgen del Rocío, Sevilla) – We modeled and generated HL7 CDA documents to support the continuity of care of over 500 patients with chronic kidney disease. – Seven HL7 CDA archetypes were designed. – Normalization layer is built over the integration engine already available on the organization. 22
  • 23. Use cases 23
  • 24. Use cases • Feeding of a contract research organization (CRO) information system using CDISC ODM – Data from a commercial EHR system was extracted and transformed to CDISC ODM. – Data was anonymized during this process. – Normalized data was consolidated in the CRO system for further processing. 24
  • 25. Use cases 25
  • 26. Archetypes as the kernel for data reuse and query 26 Reference model Archetype Archetype- based repository Original data Research subset Defines Guides transformations Guides queries
  • 27. Thank you for your attention! Questions? This presentation has been supported by a grant from Iceland, Liechtenstein and Norway through the EEA Financial Mechanism. Operated by Universidad Complutense de Madrid