This study investigates the spatial classification of hospitals to evaluate the potential for centralized and decentralized healthcare logistics systems supported by additive manufacturing (AM) technology. Using a case study of 24 hospitals in Thailand, geographic coordinates (latitude and longitude) were analyzed, and the K-Means clustering algorithm was applied to determine the optimal number of hospital clusters. The analysis identified three spatially coherent clusters. These cluster centroids were then visualized using Quantum Geographic Information System (QGIS) to map their geographic distribution. The results suggest that geographic clustering can inform the strategic placement of centralized AM production units, serving as potential printing hubs near healthcare service points. Ongoing work aims to compare centralized and decentralized AM network models by evaluating lead time, healthcare demand, and logistics costs, to inform the optimal design of AM networks for healthcare applications.