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LinkEHR Studio: a tool for archetype-based data transformations David Moner Biomedical Informatics Group (IBIME) ITACA Institute, Technical.

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Presentation on theme: "LinkEHR Studio: a tool for archetype-based data transformations David Moner Biomedical Informatics Group (IBIME) ITACA Institute, Technical."— Presentation transcript:

1 LinkEHR Studio: a tool for archetype-based data transformations David Moner Biomedical Informatics Group (IBIME) ITACA Institute, Technical University of Valencia Arctic Conference on Dual-Model based Clinical Decision Support and Knowledge Management Tromsø, May 27 th and 28 th, 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 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 – – Martínez-Costa C, et al., “An approach for the semantic interoperability of ISO EN and OpenEHR archetypes”, J Biomed Inform, 43(5)(2010) pp Option 2: Automatic generation from common, shared and generic clinical information models – This is the CIMI approach – 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 schemaTarget schema Transform script Transform script Standard data Standard data Instance of Generates Single level mapping Mapping Legacy data 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 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 Transform script Standard data Standard data Instance of Generates Two level mapping Case 1 Mapping Target archetype Compliant with Legacy data 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 Transform script Standard data Standard data Instance of Generates Mapping Target archetype Compliant with Standard data 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 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 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 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


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