  Documentation errors increase healthcare costs and cause unnecessary patient
deaths. As the standard language for diagnoses and billing, ICD codes serve as
the foundation for medical documentation worldwide. Despite the prevalence of
electronic medical records, hospitals still witness high levels of ICD
miscoding. In this paper, we propose to automatically document ICD codes with
far-field speech recognition. Far-field speech occurs when the microphone is
located several meters from the source, as is common with smart homes and
security systems. Our method combines acoustic signal processing with recurrent
neural networks to recognize and document ICD codes in real time. To evaluate
our model, we collected a far-field speech dataset of ICD-10 codes and found
our model to achieve 87% accuracy with a BLEU score of 85%. By sampling from an
unsupervised medical language model, our method is able to outperform existing
methods. Overall, this work shows the potential of automatic speech recognition
to provide efficient, accurate, and cost-effective healthcare documentation.
