Abstract
BackgroundInteroperability 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.MethodsWe 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.ResultsComparing 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.ConclusionsThe 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.
Highlights
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
Examples of those efforts are the standardisation specifications developed by Health Level Seven International (HL7) [1], the International Organization for Standardization (ISO) [2], the openEHR Foundation [3], the Clinical Information Modeling Initiative
In order to further validate our methodology, the aim of this project was to test it on thousands of real stroke patient case files from the Safe Implementation of Treatments in Stroke (SITS) registry and compare the results to those obtained from conventional data analysis using standard statistical software, which we recently reported for a clinical audience [11]
Summary
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. Reaching interoperability would mean that different health information systems could exchange information between each other and the receiving system would understand what has been sent to it, and be able to read it. Examples of those efforts are the standardisation specifications developed by Health Level Seven International (HL7) [1], the International Organization for Standardization (ISO) [2], the openEHR Foundation [3], the Clinical Information Modeling Initiative (CIMI) [4], the International Health Terminology Standards Development Organisation (IHTSDO) [5] and the World Health Organization (WHO) [6]. The different approaches are, interconnected as data elements from the former kind can often be bound to standardised terms from the latter type of initiatives
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