Most rule learning systems posit hard decision boundaries for continuous attributes and point estimates of rule accuracy, with no measures of variance, which may seem arbitrary to a domain expert. These hard boundaries/points change with small perturbations to the training data. Moreover, rule induction typically produces a large number of rules that must be filtered and interpreted by an analyst. We describe a method of combining rules over multiple bootstrap replications of rule induction so as to reduce the total number of rules presented to an analyst and to provide measures of variance to continuous attribute decision boundaries and accuracy-point estimates. The method is illustrated with perioperative data.