In recent years, many online health communities (OHCs) are established to provide the patients with the services of disease prevention and self-management. Patients in those online health communities discuss their health conditions and share their experiences with other patients using narrative texts in the posts. Those posts contain a vast amount of patients' information, including drugs, symptoms, conditions, etc. They are really valuable for clinical research. However, it is hard for information systems to automatically extract the clinical knowledge from those posts to provide knowledge-based services to online patients. This paper investigates the characteristics of those post contents and accordingly proposes an encoding method with Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) to process the texts into encoded and structured contents to help the analytic system in the OHCs to discover the biomedical knowledge. Based on our experimental result, the proposed method can effectively extract the biomedical knowledge from the posts and enhance the ability of clinical knowledge discovery online to improve the patient support.