The increasing prevalence of electronic health records (EHR), along with the needs for enhanced clinical care, presents new challenges to information retrieval (IR). Many clinical decision-making tasks following the philosophy of Evidence-Based Medicine (EBM) rely on the ability to find relevant health records and gather sufficient clinical evidence under severe time constraints. In this work, we present a system built upon statistical IR methods for searching flat-text health records (i.e. the doctors' notes sections of EHR) for patients with particular conditions specified via a keyword query. In particular, we use multiple external repositories for query expansion, and introduce two novel model weighting methods. Cross-validation results show that our system improves a strong baseline by 30% on mean average precision (MAP), and has a promising overall performance when compared with a manual system doing the same task.