An emerging public health application of connected and automated vehicle (CAV) technologies is to reduce response times of emergency medical service (EMS) by indirectly coordinating traffic. Therefore, in this work we study the CAV-assisted corridor clearance for EMS vehicles from a short term deployment perspective. Existing research on this topic often overlooks the impact of EMS vehicle disruptions on regular traffic, assumes 100% CAV penetration, relies on real-time traffic signal timing data and queue lengths at intersections, and makes various assumptions about traffic settings when deriving optimal model-based CAV control strategies. However, these assumptions pose significant challenges for near-term deployment and limit the real-world applicability of such methods. To overcome these challenges and enhance real-world applicability in near-term, we propose a model-free approach employing deep reinforcement learning (DRL) for designing CAV control strategies, showing its reduced overhead in designing and greater scalability and performance compared to model-based methods. Our qualitative analysis highlights the complexities of designing scalable EMS corridor clearance controllers for diverse traffic settings in which DRL controller provides ease of design compared to the model-based methods. In numerical evaluations, the model-free DRL controller outperforms the model-based counterpart by improving traffic flow and even improving EMS travel times in scenarios when a single CAV is present. Across 19 considered settings, the learned DRL controller excels by 25% in reducing the travel time in six instances, achieving an average improvement of 9%. These findings underscore the potential and promise of model-free DRL strategies in advancing EMS response and traffic flow coordination, with a focus on practical near-term deployment.