The time dimension is very important when reasoning with clinical data. Unfortunately, the task of temporal reasoning is inherently computationally expensive. As the problems tackled by clinical decision support systems become more varied, increased demands will be placed on the temporal reasoning component, which may lead to slow response times. This paper addresses this problem. It describes a temporal reasoning system called RASTA that uses a distributed algorithm that enables it to deal with large data sets. The algorithm also supports a variety of configuration options, enabling RASTA to deal with a range of application requirements.
