An increasing number of studies have been proposed for community healthcare services (CHS) that connect users and physicians. Complex descriptions of diseases and symptoms make CHS difficult to match users’ amateur questions with physicians. In this study, we propose the task of question-physician routing to address this issue by matching questions and physicians. Vocabulary gaps, nonreciprocal commutations, and limited user profiling information in CHS make question-physician routing a challenging research problem. To address the above challenges, we propose a new question-routing approach to match a physician with a given question, where we argue that the assistance of external medical knowledge helps to improve the performance of question-physician routing. Specifically, we propose a medical question-routing framework, namely deep medical question routing (DMQR), which integrates external medical knowledge into the routing process. DMQR can be divided into three main components: First, we construct a medical knowledge extractor to extract knowledge from a medical knowledge base constructed based on external knowledge resources. Then, we propose a knowledge interpreter to integrate medical knowledge into the given question for a better representation. Lastly, we route top-k relevant candidate physicians to the question by employing a hierarchical multi-label classifier. To further enhance the robustness and efficiency of question-physician routing, we propose a virtual adversarial medical question routing optimization method, v-DMQR, and an efficient optimization way that uses anchor question-physician pairs, namely av-DMQR. Since there is no existing data in the literature, we collect a large-scale dataset from real-world CHS scenarios. Extensive experiments performed on our dataset verify the effectiveness of our proposed approach. Moreover, we find that our proposed approach significantly outperforms state-of-the-art baselines on medical question routing in terms of Recall, MAP, and MRR metrics.