ArXiv Preprint
To identify patients who are hospitalized because of COVID-19 as opposed to
those who were admitted for other indications, we compared the performance of
different computable phenotype definitions for COVID-19 hospitalizations that
use different types of data from the electronic health records (EHR), including
structured EHR data elements, provider notes, or a combination of both data
types. And conduct a retrospective data analysis utilizing chart review-based
validation. Participants are 586 hospitalized individuals who tested positive
for SARS-CoV-2 during January 2022. We used natural language processing to
incorporate data from provider notes and LASSO regression and Random Forests to
fit classification algorithms that incorporated structured EHR data elements,
provider notes, or a combination of structured data and provider notes.
Results: Based on a chart review, 38% of 586 patients were determined to be
hospitalized for reasons other than COVID-19 despite having tested positive for
SARS-CoV-2. A classification algorithm that used provider notes had
significantly better discrimination than one that used structured EHR data
elements (AUROC: 0.894 vs 0.841, p < 0.001), and performed similarly to a model
that combined provider notes with structured data elements (AUROC: 0.894 vs
0.893). Assessments of hospital outcome metrics significantly differed based on
whether the population included all hospitalized patients who tested positive
for SARS-CoV-2 versus those who were determined to have been hospitalized due
to COVID-19. This work demonstrates the utility of natural language processing
approaches to derive information related to patient hospitalizations in cases
where there may be multiple conditions that could serve as the primary
indication for hospitalization.
Feier Chang, Jay Krishnan, Jillian H Hurst, Michael E Yarrington, Deverick J Anderson, Emily C O’Brien, Benjamin A Goldstein
2023-02-03
