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Related Experiment Video

Updated: Jan 21, 2026

A Contrast of Three Inoculation Techniques used to Determine the Race of Unknown Fusarium oxysporum f.sp. niveum Isolates
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Underserved populations with missing race ethnicity data differ significantly from those with structured

Evan T Sholle1, Laura C Pinheiro2, Prakash Adekkanattu1

  • 1Information Technologies & Services Department, Weill Cornell Medicine, New York, New York, USA.

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Natural language processing (NLP) identified more Black and Hispanic patients in electronic health records (EHRs), revealing demographic differences. This method improves understanding of underrepresented groups in health research.

Keywords:
electronic health recordethnicitynatural language processingrace

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Area of Science:

  • Medical Informatics
  • Health Disparities Research
  • Natural Language Processing in Healthcare

Background:

  • Structured electronic health record (EHR) data often has deficiencies in capturing race and ethnicity information.
  • Accurate demographic data is crucial for understanding health disparities and ensuring equitable care.

Purpose of the Study:

  • To address limitations in structured EHR race/ethnicity data by using natural language processing (NLP) to identify Black and Hispanic patients from unstructured clinical notes.
  • To assess differences in patient characteristics between those identified through NLP versus structured data.

Main Methods:

  • Developed rule-based NLP algorithms to classify patients as Black or Hispanic using EHR notes from 16,665 patients.
  • Evaluated NLP algorithm performance against a gold standard.
  • Compared demographic and clinical characteristics of patients identified via NLP only versus structured EHR data only.

Main Results:

  • NLP identified 948 additional Black patients (26% increase) and 665 additional Hispanic patients (20% increase).
  • Patients identified as Black or Hispanic by NLP only were older, more likely male, less commercially insured, and had higher comorbidity.
  • Significant differences exist between patients identified through NLP and those documented in structured EHR data.

Conclusions:

  • Structured EHR race and ethnicity data have quality issues that can be mitigated.
  • Supplementing structured data with NLP-derived race and ethnicity information can improve population demographic assessment.
  • NLP offers a valuable tool to enhance the accuracy of race and ethnicity data in EHRs, leading to more precise health outcome research.