Hybrid specification, storage, retrieval and runtime application of clinical guidelines

Abstract

Clinical guidelines are a major tool in improving the quality of medical care. However, most guidelines are in free text, are not machine-comprehensible and are not easily accessible to clinicians at the point of care. We have designed and implemented a web-based, modular, distributed architecture, the Digital Electronic Guideline Library (DeGeL), which facilitates gradual conversion of clinical guidelines from text to a formal representation in the chosen target guideline ontology. The architecture supports guideline classification, semantic markup, context-sensitive search, browsing, run-time application and retrospective quality assessment. The DeGeL hybrid meta-ontology includes elements common to all guideline ontologies, such as semantic classification and domain knowledge; it also includes four content-representation formats: free text, semi-structured text, semi-formal representation and a formal representation. These formats support increasingly sophisticated computational tasks. Guidelines can thus be in a hybrid representation in which guidelines, and even parts of the same guideline, might exist at different formalisation levels. We have also developed and rigorously evaluated a methodology and an associated web-based tool, Uruz, for gradually structuring and semi-formalising free-text clinical guidelines. Finally, we have designed, implemented and evaluated a new approach, the hybrid runtime application model, for supporting runtime application of clinical guidelines that are not necessarily in a machine-comprehensible format; in particular, when the guideline is in a semi-formal representation and the patient’s data are either in an electronic medical record or in a paper format. The tool implementing this new approach, the Spock module, is customised at this point to the Asbru guideline specification language and exploits the hybrid structure of guidelines in DeGeL. The Spock module also exploits our temporal-abstraction mediator to the patient record, IDAN, and our interactive intelligent-visualisation tool, KNAVE-II.