Amanda Robinson


2018

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From dictations to clinical reports using machine translation
Gregory Finley | Wael Salloum | Najmeh Sadoughi | Erik Edwards | Amanda Robinson | Nico Axtmann | Michael Brenndoerfer | Mark Miller | David Suendermann-Oeft
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

A typical workflow to document clinical encounters entails dictating a summary, running speech recognition, and post-processing the resulting text into a formatted letter. Post-processing entails a host of transformations including punctuation restoration, truecasing, marking sections and headers, converting dates and numerical expressions, parsing lists, etc. In conventional implementations, most of these tasks are accomplished by individual modules. We introduce a novel holistic approach to post-processing that relies on machine callytranslation. We show how this technique outperforms an alternative conventional system—even learning to correct speech recognition errors during post-processing—while being much simpler to maintain.

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An automated medical scribe for documenting clinical encounters
Gregory Finley | Erik Edwards | Amanda Robinson | Michael Brenndoerfer | Najmeh Sadoughi | James Fone | Nico Axtmann | Mark Miller | David Suendermann-Oeft
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

A medical scribe is a clinical professional who charts patient–physician encounters in real time, relieving physicians of most of their administrative burden and substantially increasing productivity and job satisfaction. We present a complete implementation of an automated medical scribe. Our system can serve either as a scalable, standardized, and economical alternative to human scribes; or as an assistive tool for them, providing a first draft of a report along with a convenient means to modify it. This solution is, to our knowledge, the first automated scribe ever presented and relies upon multiple speech and language technologies, including speaker diarization, medical speech recognition, knowledge extraction, and natural language generation.