LinkEHR Studio: a tool for archetype-based data transformations

David Monerdamoca@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

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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.

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.

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

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