In healthcare facilities, answering the questions from the patients and their companions about the health problems is regarded as an essential task. With the current shortage of medical personnel resources and an increase in the patient-to-clinician ratio, staff in the medical field have consequently devoted less time to answering questions for each patient. However, studies have shown that correct healthcare information can positively improve patients' knowledge, attitudes, and behaviors. Therefore, delivering correct healthcare knowledge through a question-answering system is crucial. In this article, we develop an interactive healthcare question-answering system that uses attention-based models to answer healthcare-related questions. Attention-based transformer models are utilized to efficiently encode semantic meanings and extract the medical entities inside the user query individually. These two features are integrated through our designed fusion module to match against the pre-collected healthcare knowledge set, so that our system will finally give the most accurate response to the user in real-time. To improve the interactivity, we further introduce a recommendation module and an online web search module to provide potential questions and out-of-scope answers. Experimental results for question-answer retrieval show that the proposed method has the ability to retrieve the correct answer from the FAQ pairs in the healthcare domain. Thus, we believe that this application can bring more benefits to human beings.