The evolution of digital healthcare necessitates robust and adaptable network solutions to manage diverse and critical traffic types in a 6G environment. This paper presents a simulation-based evaluation of the classic optimisation Cybertwin framework for digital health traffic management in a 6G digital health network, utilising the Sionna simulation framework. By leveraging tailored classical optimisation techniques, Cybertwins dynamically adapt network parameters for various traffic types, including FeMBB, ERLLC, LDHMC, umMTC, and ELPC. The results demonstrate significant improvements in throughput, latency, jitter, Packet Delivery Ratio, and energy consumption compared to a baseline raw simulation metrics scenario, highlighting the potential of Cybertwins to enhance network performance for healthcare applications. This study establishes a foundation for integrating advanced machine learning methods into Cybertwin optimisation and expanding the simulation environment to include more complex mobility models and heterogeneous network elements. These advancements will support scalable, reliable, and efficient 6G networks capable of meeting the stringent demands of future digital health systems.