title: Retrospective checking of compliance with practice guidelines for acute stroke care: a novel experiment using openEHR's Guideline Definition Language.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Health Informatics Centre, LIME, Karolinska Institutet, Tomtebodavägen 18, SE 17177 Stockholm, Sweden. nadim.anani@ki.se.
authors.forename: Nadim
authors.initials: N
authors.lastname: Anani
authors.forename: Rong
authors.initials: R
authors.lastname: Chen
authors.forename: Tiago
authors.initials: T
authors.lastname: Prazeres Moreira
authors.forename: Sabine
authors.initials: S
authors.lastname: Koch
mesh_terms: D001185:Artificial Intelligence; D003658:Decision Making, Computer-Assisted; D057286:Electronic Health Records; D019983:Guideline Adherence; D006801:Humans; D017410:Practice Guidelines as Topic; D012189:Retrospective Studies; D012660:Semantics; D020521:Stroke / Q000628:therapy; D013997:Time Factors