Clinical information retrieval (IR) presents several challenges including terminology mismatch and granularity mismatch. One of the main objectives in clinical IR is to fill the semantic gap among the queries and documents and go beyond keywords matching. To address these issues, in this paper we attempt to use semantic information to improve the performance of clinical IR systems by representing queries in an expressive and meaningful context. To model a query context initially we model and develop query domain ontology. The query domain ontology represents concepts closely related with query concepts. Query context represents concepts extracted from query domain ontology and weighted according to their semantic relatedness to query concept(s). The query context is then exploited in query expansion and patients records re-ranking for improving clinical retrieval performance. We evaluate our approach on the TREC Medical Records dataset. Results show that our proposed approach significantly improves the retrieval performance compare to classic keyword-based IR model.