Clinical guidelines are a major tool in improving the quality of medical care. However, most guidelines are in free text, not in a formal, executable format, and are not easily accessible to clinicians at the point of care. We introduce 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 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. The DeGeL tools for support of guideline-based care operate, at some level, on all guideline ontologies. We have demonstrated the feasibility of the architecture and the tools for several guideline ontologies, including Asbru and GEM.
We introduce a three-phase, nine-step methodology for specification of clinical guidelines (GLs) by expert physicians, clinical editors, and knowledge engineers and for quantitative evaluation of the specification's quality. We applied this methodology to a particular framework for incremental GL structuring (mark-up) and to GLs in three clinical domains. A gold-standard mark-up was created, including 196 plans and subplans, and 326 instances of ontological knowledge roles (KRs). A completeness measure of the acquired knowledge revealed that 97% of the plans and 91% of the KR instances of the GLs were recreated by the clinical editors. A correctness measure often revealed high variability within clinical editor pairs structuring each GL, but for all GLs and clinical editors the specification quality was significantly higher than random (p<0.01). Procedural KRs were more difficult to mark-up than declarative KRs. We conclude that given an ontology-specific consensus, clinical editors with mark-up training can structure GL knowledge with high completeness, whereas the main demand for correct structuring is training in the ontology's semantics.
Clinical guidelines are a major tool in improving the quality of medical care. However, to support the automation of guideline-based care, several requirements must be filled, such as specification of the guidelines in a machine-interpretable format and a connection to an Electronic Patient Record (EPR). For several different reasons, it is beneficial to convert free-text guidelines gradually, through several intermediate representations, to a machine-interpretable format. It is also realistic to consider the case when an EPR is unavailable. We propose an innovative approach to the runtime application of intermediate-represented Hybrid-Asbru guidelines, with or without an available EPR. The new approach capitalizes on our extensive work on developing the Digital electronic Guideline Library (DeGeL) framework. The new approach was implemented as the Spock system. For evaluation, three guidelines were specified in an intermediate format and were applied to a set of simulated patient records designed to cover prototypical cases. In all cases, the Spock system produced the expected output, and did not produce an unexpected one. Thus, we have demonstrated the capability of the Spock system to apply guidelines encoded in the Hybrid-Asbru intermediate representation, when an EPR is not available.
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