This paper describes a model for automated information retrieval in which questions posed by clinical users are analyzed to establish common syntactic and semantic patterns. The patterns are used to develop a set of general-purpose questions called generic queries. These generic queries are used in responding to specific clinical information needs. Users select generic queries in one of two ways. The user may type in questions, which are then analyzed, using natural language processing techniques, to identify the most relevant generic query; or the user may indicate patient data of interest and then pick one of several potentially relevant questions. Once the query and medical concepts have been determined, an information source is selected automatically, a retrieval strategy is composed and executed, and the results are sorted and filtered for presentation to the user. This work makes extensive use of the National Library of Medicine's Unified Medical Language System (UMLS): medical concepts are derived from the Metathesaurus, medical queries are based on semantic relations drawn from the UMLS Semantic Network, and automated source selection makes use of the Information Sources Map. The paper describes research currently under way to implement this model and reports on experience and results to date.