A Natural Language Processing System for National COVID-19 Surveillance in the US Department of Veterans Affairs

Alec Chapman, Kelly Peterson, Augie Turano, Tamára Box, Katherine Wallace, Makoto Jones


Abstract
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.
Anthology ID:
2020.nlpcovid19-acl.10
Volume:
Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020
Month:
July
Year:
2020
Address:
Online
Editors:
Karin Verspoor, Kevin Bretonnel Cohen, Mark Dredze, Emilio Ferrara, Jonathan May, Robert Munro, Cecile Paris, Byron Wallace
Venue:
NLP-COVID19
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
Language:
URL:
https://aclanthology.org/2020.nlpcovid19-acl.10
DOI:
Bibkey:
Cite (ACL):
Alec Chapman, Kelly Peterson, Augie Turano, Tamára Box, Katherine Wallace, and Makoto Jones. 2020. A Natural Language Processing System for National COVID-19 Surveillance in the US Department of Veterans Affairs. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Online. Association for Computational Linguistics.
Cite (Informal):
A Natural Language Processing System for National COVID-19 Surveillance in the US Department of Veterans Affairs (Chapman et al., NLP-COVID19 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.nlpcovid19-acl.10.pdf
Code
 abchapman93/VA_COVID-19_NLP_BSV