Papers › A Natural Language Processing System for National COVID-19 Surveillance in the US...
A Natural Language Processing System for National COVID-19 Surveillance in the US Department of Veterans Affairs
Alec Chapman, Kelly Peterson, Augie Turano, Tam{\'a}ra Box, Katherine Wallace, Makoto Jones
Timely and accurate accounting of positive cases has been an important part of the response to the COVID-19 pandemic. While most positive cases within Veterans Affairs (VA) are identified through structured laboratory results, some patients are tested or diagnosed outside VA so their clinical status is documented only in free-text narratives. We developed a Natural Language Processing pipeline for identifying positively diagnosed COVID19 patients and deployed this system to accelerate chart review. As part of the VA national response to COVID-19, this process identified 6,360 positive cases which did not have corresponding laboratory data. These cases accounted for 36.1{\%} of total confirmed positive cases in VA to date. With available data, performance of the system is estimated as 82.4{\%} precision and 94.2{\%} recall. A public-facing implementation is released as open source and available to the community.
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