archetype-based data transformation with linkehr
DESCRIPTION
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.TRANSCRIPT
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LinkEHR Studio: a tool for archetype-based data transformations
David [email protected]
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
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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
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Model and data transformations
• Two types of transformations are needed to achieve a full semantic interoperability:
• 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
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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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Source schema Target schema
Transformscript
Standarddata
Instance of Instance ofGenerates
Single level mapping
Mapping
Legacydata
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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
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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
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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.
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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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Source schema(Legacy model)
Target schema(Reference model)
Transformscript
Standarddata
Instance of Instance ofGenerates
Two level mappingCase 1
Mapping Targetarchetype
Compliantwith
Legacydata
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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.
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Two level mappingCase 1
• DEMO: The good ol’ blood pressure example
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Two level mappingCase 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 mappingCase 2
Source schema(Reference model)
Target schema(Reference model)
Transformscript
Standarddata
Instance of Instance ofGenerates
Mapping Targetarchetype
Compliantwith
Standarddata
Sourcearchetype
Compliantwith
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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.
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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.
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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.
Communicationinterface
Health Information System
External data
format
XQuery
+ Archetypes
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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
Archetype-based
repository
Original data
Research subset
Defines
Guidestransformations
Guidesqueries
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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