GOALS: Modeling Clinical Guidelines Based on TimeML Concepts

Clinical practice guidelines aim at raising the quality of healthcare. They are written in a narrative style and have to be translated into a computer-interpretable guideline (CIG) to be usable in a clinical software application. In this project we present the GOALS methodology which defines a stepwise approach to support this modeling process. The methodology is specified independently from the target CIG language and uses a guideline's text annotated with temporal concepts provided by TimeML as a starting point. It describes step-by-step how parts of the guideline's model can be generated and finally assessed by means of an evaluation scheme. By means of a scenario-based evaluation we show the applicability of GOALS by translating temporally-related sentences of a clinical protocol into its semi-formal model. Thus, we conclude that this methodology indeed supports the translation process.