One mission of medical informatics is to provide physicians, nurses, and other health care providers with the technology and tools for interpreting large and diverse data sets, so that appropriate critical care decisions can be facilitated. Ideally, medical data visualization provides the means to transform data into information and contextual knowledge suitable for interpretation and decision-making by Van Bemmel and Musen (1997) and Coiera (1997). The authors propose a model through which data is organized into multivariate multidimensional critical care patient data visualizations (CPDV). It does this as the primary means to represent and manage complex context-based patient data at various user-defined temporal resolutions. Furthermore, user-defined spatial organization of multiple (clinically related) datasets allows rapid visualization of significant trends that are related to several co-variables. Currently, anticipated findings from usability testing support the notion that the proposed model will facilitate medical decision making in a critical care environment