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 27 th and 28 th , 2014

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.
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Model and data transformations

Model and data transformations
• Two types of transformations are needed to achieve a full semantic interoperability :
Model transformations
• Consists in transforming clinical information models or clinical patterns into archetypes, DCM, templates…
• The objective is to ease the reuse of clinical information models
Data transformations
• Consists in transforming data instances from one format to another
• The objective is to ease the reuse of data
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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
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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, defining one-to-one mappings between different clinical information models for enabling data transformations
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Single level mapping
Source schema
Instance of
Legacy data
Generates
Mapping
Transform script
Target schema
Instance of
Standard data

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).
$SOURCE/temperature $TARGET/temperature
– There are lots of tools for doing this…
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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.
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Two level mapping
• LinkEHR Studio is a Reference Modelindependent 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
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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.
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Two level mapping
Case 1
Source schema
(Legacy model)
Instance of Generates
Mapping
Legacy data
Transform script
Target archetype
Target schema
(Reference model)
Compliant with
Instance of
Standard data

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.
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Two level mapping
Case 1
• DEMO: The good ol’ blood pressure example
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Two level mapping
Case 1
This is also applicable to
HL7 CDA or even to the new FHIR model
DEMO: from legacy data to HL7 CDA
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Two level mapping
Case 2
Source schema
(Reference model)
Instance of
Compliant with
Source archetype
Mapping
Generates
Target archetype
Target schema
(Reference model)
Compliant with
Instance of
Standard data
Transform script
Standard data
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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 RMinstance paths.
– Mapping of data scattered among multiple archetypes.
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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.
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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.
+ Archetypes
Health Information System
XQuery
Communication interface
External data format
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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.
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Use cases
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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.
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Use cases
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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.
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Use cases
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Archetypes as the kernel for data reuse and query
Reference model
Archetype
Original data
Guides transformations
Defines
Archetypebased repository
Guides queries
Research subset
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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