Underserved populations with missing race ethnicity data differ significantly from those with structured race/ethnicity documentation.

ABSTRACT:

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 d

SUBMITTER: Sholle ET 

PROVIDER: S-EPMC6696506 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature