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LinkEHR Studio: a tool for archetype-based data transformations

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

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LinkEHR Studio: a tool for archetype-based data transformations

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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.

  3. Model and data transformations

  4. Model and data transformations • Two types of transformations are needed to achieve a full semantic interoperability:

  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 OpenEHRarchetypes”, 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

  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? Yes, definingone-to-onemappings betweendifferentclinicalinformationmodels forenabling data transformations

  7. Single level mapping Mapping Source schema Target schema Instance of Instance of Generates Transform script Legacy data Standard 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… $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.

  10. Two level mapping • 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. www.linkehr.com

  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.

  12. Two level mappingCase 1 Target schema (Reference model) Source schema (Legacy model) Target archetype Mapping Compliant with Instance of Generates Instance of Transform script Legacy data Standard data

  13. Two level mappingCase 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.

  14. Two level mappingCase 1 • DEMO: The good ol’ blood pressure example

  15. Two level mappingCase 1 Thisisalsoapplicableto HL7 CDA oreventothe new FHIR model DEMO: fromlegacy data to HL7 CDA

  16. Two level mappingCase 2 Target schema (Reference model) Source schema (Reference model) Source archetype Target archetype Mapping Compliant with Compliant with Instance of Generates Instance of Transform script Standard data Standard data

  17. Two level mappingCase 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.

  18. Two level mappingCase 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.

  19. Integratingthetransformation scripts in yoursystems • 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. + Archetypes HealthInformationSystem XQuery External data format Communication interface

  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.

  21. Use cases

  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.

  23. Use cases

  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 anonymizedduring this process. • Normalized data was consolidated in the CRO system for further processing.

  25. Use cases

  26. Archetypes as thekernelfor data reuse and query Reference model Archetype Guides transformations Guides queries Defines Archetype-basedrepository Original data Researchsubset

  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. Operatedby Universidad Complutense de Madrid

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