Hospitals face numerous challenges in the complex field of healthcare, especially when it comes to managing medical equipment assets. The growing variety and criticality of medical devices pose significant obstacles to efficient maintenance programs. In particular, conflicting priorities often emerge across hospital departments regarding factors like asset functionality, age, risk levels, and other relevant complexity dimensions. This highlights the need for an inclusive equipment prioritization approach in which stakeholders from various hospital sections can participate. In this context, this study utilizes semantic technologies to assess the criticality of hospital assets by promoting shared understanding between different hospital functions, with a focus on medical device prioritization. Specifically, an ontology-driven model serves as the foundation to enable more efficient data integration, consistent evaluations, and coordinated maintenance workflows, alongside improved cross-departmental interoperability. The model harnesses technologies like OWL (Web Ontology Language), SWRL (Semantic Web Rule Language), and SQWRL (Semantic Query Web Rule Language) to represent domain knowledge and derive data-driven, context-aware decisions. A demonstration illustrates the proposed approach's potential for streamlining device criticality assessments through semantic knowledge formalization and reasoning capabilities. The framework's flexibility further allows its extension to adjacent hospital management and information sharing tasks, exploiting quantified criticality measures. Overall, this study shows that semantically modeling equipment priorities aids complex, collaborative decision making across hospital departments. 

Decisionmaking, Ontology, Priority assets, Priority medical device, Semantic technology.