Healthcare professionals and inter (or multi) disciplinary academia have been paying more attention to decision support systems (DSS) for improved decision making during their health service processes or management, as well as clinical practices. Although there have been numerous DSS applications in the healthcare industry, it has been intended to provide a categorical snapshot view of current
implementations or academic work at specific DSS types for better understanding the application domains by addressing the gap in the literature. To achieve this, it has been focused on DSS applications in healthcare specifically by concentrating on two main types: model-driven and knowledge-driven. In this context, relevant information systems and medical science literatures were reviewed. For health service problems like hospital placement decisions and homecare route planning, model-driven DSS applications are used for optimization and modelling. Both conventional operations research techniques like optimization, decision analysis, simulation, and multi-criteria decision making, as well as contemporary ones like heuristic search, benefit from these applications. In addition, artificial intelligence techniques help health decision makers via knowledge-driven DSS applications, specifically clinical decision support systems (CDSS). Artificial intelligence applications can also assist health professionals in enhancing their decision-making abilities by incorporating complex operational rules and developing such procedures as single-or multi-agent systems. This research focuses on what to emphasis on while designing a DSS in the healthcare setting, such as which programming or modelling languages to employ and how to transform a model-driven DSS into a knowledgedriven DSS, or how to create the DSS more intelligent. Overall, this study indicates a present course for DSS and offers useful knowledge for both scholars and professionals in the healthcare domain.