Applying Speculative Computation to Guideline-based Decision Support Systems

Abstract

Clinical Practice Guidelines, as evidence-based recommendations are the ideal support for Clinical Decision Support Systems. The intricacies of a guideline execution tool are related with the establishment of a care flow with an appropriate order between procedures and the modelling of decision points. One of such decision points is the choice between alternative tasks based on trigger conditions regarding a patient's state. It may be the case that, when there is the need to choose one of the alternative tasks, the system does not possess all the required information to do so, thus rendering impossible to reach an outcome. Speculative Computation and Abduction may increase the efficiency of this process by allowing the system to advance the computation of a solution, even while it is waiting for a response from the information sources. This work provides the basis for a Speculative Computation framework able to cope with decisions of clinical care flows. The methods developed herein were devised to support practitioners and to improve patient-centred medicine by providing maps of the most likely evolution of a patient, even when the information is incomplete.