In recent years, sophisticated visualization methods have been developed to support both, the logical structure and the time-oriented aspects of computer-executable clinical treatment plans. However, visualizing the effects of applying treatment plans as well as supporting the exploration of effects on the patient's condition are still largely unresolved tasks. To fill this gap, we have developed a prototype that enhances known visualization methods to communicate the processes of treatment plan application together with their effects on a patient's condition in an easily understandable way. Our prototype combines the advantages of enhanced visual recognition of patterns with traditional information of parameters' development. Thus, it provides means (1) to assess success or failure of previously applied treatment plans, (2) to explore the effects of each applied clinical action on the patient's condition, and (3) to identify sub-optimal treatment choices. These means help physicians to optimize their treatment choices and enable developers of clinical practice guidelines (CPGs) to investigate and readjust these treatment plans.

Mapping concepts from medical terminologies, such as the UMLS, to medical documents is a prerequisite for many tasks of (automatically) processing documents. Due to the nature of the UMLS and the tools to accomplish the mapping, it is not always possible to achieve a correct and unambiguous mapping. This drawback led us to the development of an editor for correcting the obtained information. Our editor, MapFace, visualizes information received by the MetaMap Transfer (MMTx) program and enables users to edit and correct this information on both a conceptual and a syntactical level. By means of this functionality we are able to provide further processing steps with correct and appropriate input. Furthermore, the visualization features enable users to validate or even generate hypotheses, as well as support the better understanding of medical text. 1.

Transforming a text-based clinical guideline in a computer-interpretable form is a time-consuming and demanding task due to the various users involved, who have different technical and medical background. In the past, different guideline representation languages and supporting tools have been developed, however, these approaches seldom address the various users' demands and needs in the different steps of the guidelines' life cycle. Our approach is oriented on the guideline life-cycle and takes the requirements and the interactions of the various actors into account to formalize guidelines in a computer-interpretable guideline representation by using a semi-automatic way based on NLP techniques. We analyzed the guideline life-cycle and the roles of the actors to build such a model. This model is prototypical implemented and showed the usefulness and utility for the various users.

Clinical practice guidelines are widely used to support medical staff in treatment planning and decision-making, whereas, the classification of different recommendations in the CPGs are one of the most important information sources to use. However, there is a lack of consensus amongst guideline developers, regarding those classification schemes. To address this problem, we mapped the different graded and ungraded evidence information used by different guideline developing organizations into our meta schema. In this paper we describe how guideline representation languages, such as Asbru and PROforma can be extended to model our meta schema.