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      Applying openEHR’s Guideline Definition Language to the SITS international stroke treatment registry: a European retrospective observational study

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          Abstract

          Background

          Interoperability standards intend to standardise health information, clinical practice guidelines intend to standardise care procedures, and patient data registries are vital for monitoring quality of care and for clinical research. This study combines all three: it uses interoperability specifications to model guideline knowledge and applies the result to registry data.

          Methods

          We applied the openEHR Guideline Definition Language (GDL) to data from 18,400 European patients in the Safe Implementation of Treatments in Stroke (SITS) registry to retrospectively check their compliance with European recommendations for acute stroke treatment.

          Results

          Comparing compliance rates obtained with GDL to those obtained by conventional statistical data analysis yielded a complete match, suggesting that GDL technology is reliable for guideline compliance checking.

          Conclusions

          The successful application of a standard guideline formalism to a large patient registry dataset is an important step toward widespread implementation of computer-interpretable guidelines in clinical practice and registry-based research. Application of the methodology gave important results on the evolution of stroke care in Europe, important both for quality of care monitoring and clinical research.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12911-016-0401-5) contains supplementary material, which is available to authorized users.

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          Most cited references12

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          Guidelines for Management of Ischaemic Stroke and Transient Ischaemic Attack 2008

          This article represents the update of the European Stroke Initiative Recommendations for Stroke Management. These guidelines cover both ischaemic stroke and transient ischaemic attacks, which are now considered to be a single entity. The article covers referral and emergency management, Stroke Unit service, diagnostics, primary and secondary prevention, general stroke treatment, specific treatment including acute management, management of complications, and rehabilitation.
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            The syntax and semantics of the PROforma guideline modeling language.

            PROforma is an executable process modeling language that has been used successfully to build and deploy a range of decision support systems, guidelines, and other clinical applications. It is one of a number of recent proposals for representing clinical protocols and guidelines in a machine-executable format (see ). In this report, the authors outline the task model for the language and provide an operational semantics for process enactment together with a semantics for expressions, which may be used to query the state of a task during enactment. The operational semantics includes a number of public operations that may be performed on an application by an external agent, including operations that change the values of data items, recommend or make decisions, manage tasks that have been performed, and perform any task state changes that are implied by the current state of the application. Disclosure: PROforma has been used as the basis of a commercial decision support and guideline technology Arezzo (Infermed, London, UK; details in text).
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              The SAGE Guideline Model: achievements and overview.

              The SAGE (Standards-Based Active Guideline Environment) project was formed to create a methodology and infrastructure required to demonstrate integration of decision-support technology for guideline-based care in commercial clinical information systems. This paper describes the development and innovative features of the SAGE Guideline Model and reports our experience encoding four guidelines. Innovations include methods for integrating guideline-based decision support with clinical workflow and employment of enterprise order sets. Using SAGE, a clinician informatician can encode computable guideline content as recommendation sets using only standard terminologies and standards-based patient information models. The SAGE Model supports encoding large portions of guideline knowledge as re-usable declarative evidence statements and supports querying external knowledge sources.
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                Author and article information

                Contributors
                +46729176781 , nanani@gmx.de
                michael.mazya@karolinska.se
                rong.chen@cambio.se
                tiago.more@gmail.com
                olivier.bill@chuv.ch
                niaz.ahmed@karolinska.se
                nils.wahlgren@karolinska.se
                sabine.koch@ki.se
                Journal
                BMC Med Inform Decis Mak
                BMC Med Inform Decis Mak
                BMC Medical Informatics and Decision Making
                BioMed Central (London )
                1472-6947
                10 January 2017
                10 January 2017
                2017
                : 17
                : 7
                Affiliations
                [1 ]Health Informatics Centre, LIME, Karolinska Institutet, Tomtebodavägen 18, Stockholm, SE 17177 Sweden
                [2 ]Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
                [3 ]Department of Neurology, Karolinska University Hospital Solna, Stockholm, Sweden
                [4 ]Cambio Healthcare Systems, Stockholm, Sweden
                [5 ]Department of Neurology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
                Author information
                http://orcid.org/0000-0003-2975-4589
                Article
                401
                10.1186/s12911-016-0401-5
                5223429
                0b46b38d-f69f-4799-aff6-452255dc1c54
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 25 May 2016
                : 15 December 2016
                Funding
                Funded by: Health Informatics Centre
                Funded by: Strategic Research Programme in Care Sciences at Karolinska Institutet
                Funded by: Stockholm County Research Initiative
                Categories
                Technical Advance
                Custom metadata
                © The Author(s) 2017

                Bioinformatics & Computational biology
                knowledge management,standards,registries,practice guidelines,guideline adherence

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