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Open Access Highly Accessed Technical advance

Retrospective checking of compliance with practice guidelines for acute stroke care: a novel experiment using openEHR’s Guideline Definition Language

Nadim Anani1*, Rong Chen12, Tiago Prazeres Moreira34 and Sabine Koch1

Author Affiliations

1 Health Informatics Centre, LIME, Karolinska Institutet, Tomtebodavägen 18, SE 17177 Stockholm, Sweden

2 Cambio Healthcare Systems, Ringvägen 100, SE 11860 Stockholm, Sweden

3 Department of Neurology, Karolinska Stroke Research Unit, Karolinska University Hospital-Solna, SE 17176 Stockholm, Sweden

4 Department of Clinical Neuroscience, Stroke Research Group, Karolinska Institutet, SE 17177 Stockholm, Sweden

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BMC Medical Informatics and Decision Making 2014, 14:39  doi:10.1186/1472-6947-14-39

Published: 10 May 2014



Providing scalable clinical decision support (CDS) across institutions that use different electronic health record (EHR) systems has been a challenge for medical informatics researchers. The lack of commonly shared EHR models and terminology bindings has been recognised as a major barrier to sharing CDS content among different organisations. The openEHR Guideline Definition Language (GDL) expresses CDS content based on openEHR archetypes and can support any clinical terminologies or natural languages. Our aim was to explore in an experimental setting the practicability of GDL and its underlying archetype formalism. A further aim was to report on the artefacts produced by this new technological approach in this particular experiment. We modelled and automatically executed compliance checking rules from clinical practice guidelines for acute stroke care.


We extracted rules from the European clinical practice guidelines as well as from treatment contraindications for acute stroke care and represented them using GDL. Then we executed the rules retrospectively on 49 mock patient cases to check the cases’ compliance with the guidelines, and manually validated the execution results. We used openEHR archetypes, GDL rules, the openEHR reference information model, reference terminologies and the Data Archetype Definition Language. We utilised the open-sourced GDL Editor for authoring GDL rules, the international archetype repository for reusing archetypes, the open-sourced Ocean Archetype Editor for authoring or modifying archetypes and the CDS Workbench for executing GDL rules on patient data.


We successfully represented clinical rules about 14 out of 19 contraindications for thrombolysis and other aspects of acute stroke care with 80 GDL rules. These rules are based on 14 reused international archetypes (one of which was modified), 2 newly created archetypes and 51 terminology bindings (to three terminologies). Our manual compliance checks for 49 mock patients were a complete match versus the automated compliance results.


Shareable guideline knowledge for use in automated retrospective checking of guideline compliance may be achievable using GDL. Whether the same GDL rules can be used for at-the-point-of-care CDS remains unknown.

Computer-assisted decision making; Practice guideline; Guideline adherence; Electronic health records; Semantics; openEHR; Artificial intelligence; Stroke