Fast, Structured Clinical Documentation via Contextual Autocomplete

Divya Gopinath, Monica Agrawal, Luke Murray, Steven Horng, David Karger, David Sontag
Proceedings of the 5th Machine Learning for Healthcare Conference, PMLR 126:842-870, 2020.

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 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v126-gopinath20a, title = {Fast, Structured Clinical Documentation via Contextual Autocomplete}, author = {Gopinath, Divya and Agrawal, Monica and Murray, Luke and Horng, Steven and Karger, David and Sontag, David}, booktitle = {Proceedings of the 5th Machine Learning for Healthcare Conference}, pages = {842--870}, year = {2020}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {126}, series = {Proceedings of Machine Learning Research}, month = {07--08 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v126/gopinath20a/gopinath20a.pdf}, url = {https://proceedings.mlr.press/v126/gopinath20a.html}, 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 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.} }
Endnote
%0 Conference Paper %T Fast, Structured Clinical Documentation via Contextual Autocomplete %A Divya Gopinath %A Monica Agrawal %A Luke Murray %A Steven Horng %A David Karger %A David Sontag %B Proceedings of the 5th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2020 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v126-gopinath20a %I PMLR %P 842--870 %U https://proceedings.mlr.press/v126/gopinath20a.html %V 126 %X 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.
APA
Gopinath, D., Agrawal, M., Murray, L., Horng, S., Karger, D. & Sontag, D.. (2020). Fast, Structured Clinical Documentation via Contextual Autocomplete. Proceedings of the 5th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 126:842-870 Available from https://proceedings.mlr.press/v126/gopinath20a.html.

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