Knowledge-based verification of clinical guidelines by detection of anomalies.
As shown in numerous studies, a significant part of published clinical guidelines is tainted with different types of semantical errors that interfere with their practical application. The adaptation of generic guidelines, necessitated by circumstances such as resource limitations within the applying organization or unexpected events arising in the course of patient care, further promotes the introduction of defects. Still, most current approaches for the automation of clinical guidelines are lacking mechanisms, which check the overall correctness of their output. In the domain of software engineering in general and in the domain of knowledge-based systems (KBS) in particular, a common strategy to examine a system for potential defects consists in its verification. The focus of this work is to present an approach, which helps to ensure the semantical correctness of clinical guidelines in a three-step process. We use a particular guideline specification language called Asbru to demonstrate our verification mechanism. A scenario-based evaluation of our method is provided based on a guideline for the artificial ventilation of newborn infants. The described approach is kept sufficiently general in order to allow its application to several other guideline representation formats.
Department of Medical Computer Sciences, University of Vienna, Spitalgasse 23, A-1090, Vienna, Austria. georg.duftschmid@akh-wien.ac.at
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D001185:Artificial Intelligence; D006801:Humans; D017410:Practice Guidelines as Topic / Q000592:standards*; D011381:Programming Languages