Speech to text models have become a very useful tool for hospitals. Hospitals can use automatic transcriptions to be able to reduce workload on doctors and clinicians since they do not have to manually record information. This automation can give them more time to meet with more patients and increase the efficiency of hospital work. However, an unexplored application of speech-to-text are emergency calls. The most common use for automated transcriptions are to document what doctors are doing and are given time to proofread for errors. This work focuses on the problem of transcriptions of emergency call data. Our work curates this emergency call data and models it as a medical transcription problem in hopes that the transcriptions can be used later for medical decision making. The heavy background noise and poor audio quality that comes with emergency radio are the reason this problem is challenging to solve. The results of this experiment show a modest increase to the accuracy of transcribing the emergency hospital recordings.