Advances in mobile communications capabilities open the door for closer integration of pre-hospital and in-hospital care processes. For example, medical specialists are enabled to guide on-site paramedics and are, in turn, supplied with live vitals or visuals. Consolidating such performance-critical applications with the highly complex workings of mobile communications requires solutions both reliable and performant, but also easy to integrate with existing systems. This paper explores the application of Deep Deterministic Policy Gradient (DDPG) methods for learning a communications resource scheduling algorithm with special regards to priority users. Different from popular Deep-Q-Network methods, the DDPG is able to produce continuous-valued output. With light post-processing, the resulting scheduler is able to achieve high performance on a flexible sum-utility goal.