Chronic diseases are the major source of morbidity, mortality, and resource utilization. Large-scale longitudinal databases are rapidly proliferating in both single- and multi-institutional settings, providing clinical data on a broad range of patients who receive ‘real world’ management. Although bias and changing medical management may limit the types of questions that can be addressed using the data contained in longitudinal clinical databases, many initial hypotheses can be generated from the data. Because chronic diseases persist over long periods of time, understanding the impact of temporal relationships, and of concurrent clinical events and contexts is critical to meaningful interpretation of clinical data. Adapting techniques initially developed for the physical sciences and for statistical process control can produce visual displays of clinical data that capture complex temporal and contextual information. With these tools, investigators can quickly explore vast quantities of clinical data, and discover new temporal relationships and emerging trends.