The utility of storytelling in the interaction between healthcare providers and patients is now firmly established, but the potential use of large-scale story collections for health-related inquiry has not yet been explored. In particular, the enormous scale of storytelling in personal web logs offers investigators in health-related fields new opportunities to study the behavior and beliefs of diverse patient populations outside of clinical settings. In this paper we address the technical challenges in identifying personal stories about specific health issues from corpora of millions of web log posts. We describe a novel infrastructure for collecting and indexing the stories posted each day to English-language web logs, coupled with user interfaces designed to support targeted searches of these collections. We evaluate the effectiveness of this search technology in an effort to identify hundreds of first person and third person accounts of strokes, for the purpose of studying gender differences in the way that these health emergencies are described. Results indicate that the use of relevance feedback significantly improves the effectiveness of the search. We conclude with a discussion of sample biases that are inherent in web log storytelling and heightened by our approach, and propose ways to mitigate these biases.