It is common for PubMed users to repeatedly modify their queries (search terms) before retrieving documents relevant to their information needs. To assist users in reformulating their queries, we report the implementation and usage analysis of a new component in PubMed called Related Queries, which automatically produces query suggestions in response to the original user's input. The proposed method is based on query log analysis and focuses on finding popular queries that contain the initial user search term with a goal of helping users describe their information needs in a more precise manner. This work has been integrated into PubMed since January 2009. Automatic assessment using clickthrough data show that each day, the new feature is used consistently between 6% and 10% of the time when it is shown, suggesting that it has quickly become a popular new feature in PubMed.