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This thesis covers a wide range of aspects related to predictive business process monitoring with a focus on process outcomes: from model training and evaluation to the practical application of prediction models. The first contribution of the thesis is a taxonomy and a comparative experimental evaluation of existing predictive monitoring methods. Secondly, the thesis proposes a framework that exploits textual data payload in addition to the commonly used numeric and categorical data. Thirdly, a novel quality dimension of temporal prediction stability and a metric for measuring it are introduced. Lastly, the thesis proposes a framework for generating alarms based on predictive models, in order to optimize the net cost of negative outcomes in a business process.