|Title||Underserved populations with missing race ethnicity data differ significantly from those with structured race/ethnicity documentation.|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Sholle ET, Pinheiro LC, Adekkanattu P, Davila MA, Johnson SB, Pathak J, Sinha S, Li C, Lubansky SA, Safford MM, Campion TR|
|Journal||J Am Med Inform Assoc|
|Date Published||2019 Aug 01|
OBJECTIVE: We aimed to address deficiencies in structured electronic health record (EHR) data for race and ethnicity by identifying black and Hispanic patients from unstructured clinical notes and assessing differences between patients with or without structured race/ethnicity data.
MATERIALS AND METHODS: Using EHR notes for 16 665 patients with encounters at a primary care practice, we developed rule-based natural language processing (NLP) algorithms to classify patients as black/Hispanic. We evaluated performance of the method against an annotated gold standard, compared race and ethnicity between NLP-derived and structured EHR data, and compared characteristics of patients identified as black or Hispanic using only NLP vs patients identified as such only in structured EHR data.
RESULTS: For the sample of 16 665 patients, NLP identified 948 additional patients as black, a 26%increase, and 665 additional patients as Hispanic, a 20% increase. Compared with the patients identified as black or Hispanic in structured EHR data, patients identified as black or Hispanic via NLP only were older, more likely to be male, less likely to have commercial insurance, and more likely to have higher comorbidity.
DISCUSSION: Structured EHR data for race and ethnicity are subject to data quality issues. Supplementing structured EHR race data with NLP-derived race and ethnicity may allow researchers to better assess the demographic makeup of populations and draw more accurate conclusions about intergroup differences in health outcomes.
CONCLUSIONS: Black or Hispanic patients who are not documented as such in structured EHR race/ethnicity fields differ significantly from those who are. Relatively simple NLP can help address this limitation.
|Alternate Journal||J Am Med Inform Assoc|
|PubMed Central ID||PMC6696506|
Underserved populations with missing race ethnicity data differ significantly from those with structured race/ethnicity documentation.
Submitted by chz4003 on October 15, 2019 - 4:17pm