This paper presents a systematic literature review on hospital supply chain management (HSCM), with the aim of identifying research gaps to guide future studies and support the development of decision support systems (DSS) that enhance healthcare supply chain performance. Using the PRISMA 2020 methodology, 117 articles were initially retrieved from the Web of Science database, of which 53 articles were based on real data and retained for analysis. The review classified research across three key dimensions: type of data, methodological approach, and supply chain function. Results show that inventory management is the least studied function, addressed in in only 6 articles (11.3%) of articles, despite its importance in reducing shortages and waste. From a methodological perspective, traditional approaches (13.2%) and pure ML/AI (30.2%) were underutilized, while hybrid methods dominate (56.6%). These findings highlight two underexplored areas. That is, there is limited use of traditional and AI/ML approaches and insufficient focus on inventory management within HSCM. Addressing these gaps will be vital for developing robust DSS solutions capable of strengthening forecasting, enhancing operational resilience, and improving overall efficiency. Future research should therefore place greater emphasis on inventory management and expand the application of AI/ML methodologies in HSCM.