Big data in healthcare contain a huge amount of tacit knowledge that brings great value to healthcare activities such as diagnosis, decision support, and treatment. However, effectively exploring and exploiting knowledge on such big data sources exposes many challenges for both managers and technologists. In this study, we therefore propose a healthcare knowledge management system that ensures the systematic knowledge development process on various data in hospitals. It leverages big data technologies to capture, organize, transfer, and manage large volumes of medical knowledge, which cannot be handled with traditional data-processing technologies. In addition, machine-learning algorithms are used to derive knowledge at a higher level in supporting diagnosis and treatment. The orchestration of a knowledge system, big data, and artificial intelligence brings many advances to healthcare. Our research results show that the system fully ensures the knowledge development process, serving for knowledge exploration and exploitation to improve decision-making in healthcare. The knowledge system is illustrated for the detection and classification of high blood pressure and brain hemorrhage in text and CT/MRI image formats, respectively, from medical records of hospitals. It can support doctors to accurately diagnose the diseases to give appropriate treatment regimens.