A vast amount of electronic healthcare information is now available, ranging from online healthcare articles to patient electronic health records. These electronic data sets contain valuable information that can guide the decisions of both clinical professionals and patients. However, the data are often difficult to analyze, in part because they often contain multiple facets of information. For example, patient records have information on demographics, diagnoses, medications, lab results, and symptoms. To address this challenge, we have been exploring interactive visual analysis techniques that help visualize such healthcare information in an intuitive manner and enable the discovery of actionable insights. In this paper, we present a review of three different techniques. First, we describe a visual analytic system named FacetAtlas that helps users navigate a large set of disease-related documents and understand multidimensional relationships for key semantic concepts such as symptoms and treatments. We then present SolarMap, an alternative technique to FacetAtlas that adds visual representations of facet keyword clusters to expose greater information about semantic relationships. Finally, we describe the DICON (Dynamic Icon) visualization tool, which allows users to interactively view and refine similar multidimensional patient clusters.