The reliability of network Intrusion Detection Systems (IDS) research in healthcare mainly depends on the availability and realism of network security datasets. However, due to strict privacy regulations and the sensitivity of patient data, real hospital traffic is rarely accessible. For that reason, current researchers rely heavily on publicly available datasets, which are typically synthetically generated or based on simulated devices in laboratory environments. This study presents an analysis of real network traffic captured from a hospital environment and a network feature-based comparison between the real environment and two publicly available datasets. The results indicate that several window-based features exhibit major shifts between real and synthetic datasets. In contrast, flow-based features present greater stability across publicly available datasets and the real hospital network. These findings reveal that while public datasets capture some general properties of hospital traffic, they fail to reproduce its full heterogeneity and temporal dynamics. The study highlights the need for more realistic data generation and validation methods to improve the reliability and transferability of IDS solutions in healthcare environments.