Electronic Medical Record (EMR) databases contain a large amount of temporal events such as diagnosis dates for various symptoms. Analyzing disease progression pathways in terms of these observed events can provide important insights into how diseases evolve over time. Moreover, connecting these pathways to the eventual outcomes of the corresponding patients can help clinicians understand how certain progression paths may lead to better or worse outcomes. In this paper, we describe the Outflow visualization technique, designed to summarize temporal event data that has been extracted from the EMRs of a cohort of patients. We include sample analyses to show examples of the insights that can be learned from this visualization.