Clinical data is often captured in unstructured texts and scattered in different health information systems. This complicates the aggregation of information in the process of clinical decision making. However, having a quick overview and an efficient representation of relevant aspects of a patient's health status are crucial for this process. While accessing patient data and perusing clinical documents, relevant details need to be discovered quickly. In this paper, we introduce an approach to visualize relevant information from clinical documents by tag clouds. The conventional tag clouds visualize the content of a document using the terms they are containing shown in different sizes with the size calculated based on the term frequency. Important facts and diagnostic results with low occurrence in a text may be ignored by this naïve method. In this paper, we therefore adapt the conventional tag clouds by information extraction and a guidelines-based classification schema, so that the clinical concerns can be visualized more correctly. The aspects are extracted according to a classification schema developed by clinical experts. We evaluate the approach on a set of radiology reports for cervical spine treatment.