Fast, Structured Clinical Documentation via Contextual Autocomplete

Abstract

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 well-structured medical hierarchies to make 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.

Publication
Proceedings of the Machine Learning for Healthcare Conference
Divya Gopinath
Divya Gopinath
Master’s student
Monica Agrawal
Monica Agrawal
PhD Student

Monica’s research interests include reasoning over longitudinal clinical notes, building more intelligent electronic health records, studying user-ML interactions in clinical settings, and developing algorithms that can incorporate domain knowledge.

David Sontag
David Sontag
Associate Professor of EECS

My research focuses on advancing machine learning and artificial intelligence, and using these to transform health care.

Related