We propose a new approach to detect emerging patterns of keywords in the chief complaint data. Our semantic scan statistic has three steps: automatically inferring a set of topics (probability distributions over words) from the data using Latent Dirichlet Allocation (2), classifying each chief complaint to the most likely topic, and then performing a spatial scan using the case counts for each topic. We compare three variants of the semantic scan: static (topics are learned from historical data and do not change from day to day), dynamic (topics are recalculated each day using the most recent two weeks of data) and incremental (not only using the static topics but also learning additional 'emerging' topics that differ substantially from the static topics).