Capturing how a patient's medical problems change over time is important for understanding the progression of a disease, its effects, and response to treatment. We describe two prototype tools that are being developed as part of a data processing pipeline for standardizing, structuring, and visualizing problems and findings documented in clinical reports associated with neuro-oncology patients. Given a list of problems and findings identified using a natural language processing (NLP) system, we have created a mapping tool that assigns an observation of a problem to one of nine classes that describe change. The second tool utilizes iconic representations of the nine classes to generate a timeline interface, enabling users to pan, zoom, and filter the data. The result of this preliminary work is an automated approach for understanding and summarizing the evolution of a problem within the patient electronic medical record.