Fast, Structured Clinical Documentation via Contextual Autocomplete
This addresses the time-consuming task of clinical note-taking for doctors, though it appears incremental as it applies existing neural network techniques to a specific domain.
The paper tackles the problem of slow clinical documentation by developing a real-time autocompletion system that suggests relevant clinical concepts as doctors draft notes, reducing keystroke burden by 67% in live hospital settings.
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.