This paper presents an automated scoring approach for a formative assessment tool aimed at helping learner physicians enhance their communication skills through simulated patient interactions. The system evaluates transcribed learner responses by detecting key communicative behaviors, such as acknowledgment, empathy, and clarity. Built on an adapted version of the ACTA scoring framework, the model achieves a mean binary F1 score of 0.94 across 8 clinical scenarios. A central contribution of this work is the investigation of how to balance scoring accuracy with scalability. We demonstrate that synthetic training data offers a promising path toward reducing reliance on large, annotated datasets—making automated scoring more accurate and scalable.