  We present a system that uses a learned autocompletion mechanism to
facilitate rapid creation of semi-structured clinical documentation. We
dynamically suggest relevant clinical concepts as a doctor drafts a note by
leveraging features from both unstructured and structured medical data. By
constraining our architecture to shallow neural networks, we are able to make
these suggestions in real time. Furthermore, as our algorithm is used to write
a note, we can automatically annotate the documentation with clean labels of
clinical concepts drawn from medical vocabularies, making notes more structured
and readable for physicians, patients, and future algorithms. To our knowledge,
this system is the only machine learning-based documentation utility for
clinical notes deployed in a live hospital setting, and it reduces keystroke
burden of clinical concepts by 67% in real environments.
