GASTON: an architecture for the acquisition and execution of clinical guideline-application tasks

Recently, studies have shown the benefits of using clinical guidelines in the practice of medicine. There have been numerous efforts to develop clinical decision support systems that support guideline-based care in an automated fashion, covering a wide range of clinical settings and tasks. Despite these efforts, only a few systems progressed beyond the prototype stage and the research laboratory. For guideline-based clinical decision support systems to be successful, a balance must be made between intuitive bur imprecise representations usually encountered by most of today's systems and representations that support a strong underlying clinical performance model. The project described in this paper tries to achieve such a balance. It presents the GASTON architecture that contains a set of reusable software components for the application of guidelines, including design-time components to facilitate the guideline authoring process based on guideline representation models along with execution-time components for building decision support systems that incorporate these guidelines. This architecture was used to develop several guideline representation models such as a rule-based representation to model rule-based guidelines and guideline representation models that address more complex tasks. Also, decision support systems that incorporate these models were developed with the architecture. For the representation and application of various classes of guidelines, rules were also viewed as instances of more complex tasks. By identifying similar characteristics of sets of rules, we developed several tasks such as a drug interaction and drug contraindication task. Based on these models, we have developed and validated guidelines and decision support systems for use in several application domains such as intensive care, family physicians and psychiatry. In order to be able to represent more complex time-oriented plans, new guideline representation models are being developed.