The main purposes of clinical decision-support systems (CDSS) are disseminating evidence-based medical knowledge (EBM), supporting a continued medical education, and improving clinical decision making and care. These purposes are traditionally achieved by using solutions that are relatively transparent and explainable to the end user. However, the development and maintenance of such solutions is resource demanding. Currently, there are four challenges existing in CDSSs when adapting to new circumstances. That is, when facing new knowledge, new diseases, different organizations and users with different skills, usually one needs to update the existing CDSS or develop a new CDSS, which requires lots of time and efforts. Hence, this paper aims for reusing an existing CDSS code by virtue of inputs from authorized medical domain expert users, and with minimal requirement of knowledge and software engineers. To facilitate knowledge elicitation and end-user development, an ACKTUS-based architecture for CDSS development and management is presented that contains: I) A knowledge base and a content management system built on Semantic Web technology to achieve modularity, reusability, customisation, and the possibility to allow medical experts to model the medical knowledge and to structure the information that builds up the design of the user interface; II) A user interface and an graphical user interface generator that automatically generates the user interface whenever the user logs in, so that the interface is synchronised with updates of the knowledge base; III) An inference engine that utilizes patient-specific data and applies various rules in the knowledge base to conduct the reasoning and decision making. These modules can be reused when adapting to new situations. A CDSS for dementia diagnosis is developed and used as an example in the presentation of the generic architecture. A pilot study of the CDSS is presented involving four medical professionals with different levels of expertise. The results show how the generic approach allows for easy knowledge representation and management of EBM, supports a continued medical education and may improve clinical decision making and care provision.