Use of natural language processing to uncover racial bias in obstetrical documentation.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
  • Additional Information
    • Source:
      Publisher: Elsevier Country of Publication: United States NLM ID: 8911831 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-4499 (Electronic) Linking ISSN: 08997071 NLM ISO Abbreviation: Clin Imaging Subsets: MEDLINE
    • Publication Information:
      Original Publication: [New York, NY] : Elsevier, [c1989-
    • Subject Terms:
    • Abstract:
      Natural Language Processing (NLP), a form of Artificial Intelligence, allows free-text based clinical documentation to be integrated in ways that facilitate data analysis, data interpretation and formation of individualized medical and obstetrical care. In this cross-sectional study, we identified all births during the study period carrying the radiology-confirmed diagnosis of fibroid uterus in pregnancy (defined as size of largest diameter of >5 cm) by using an NLP platform and compared it to non-NLP derived data using ICD10 codes of the same diagnosis. We then compared the two sets of data and stratified documentation gaps by race. Using fibroid uterus in pregnancy as a marker, we found that Black patients were more likely to have the diagnosis entered late into the patient's chart or had missing documentation of the diagnosis. With appropriate algorithm definitions, cross referencing and thorough validation steps, NLP can contribute to identifying areas of documentation gaps and improve quality of care.
      Competing Interests: Declaration of competing interest H.F. and O.S.Y. Are employed by Gynisus Inc. All other authors report no conflict of interest.
      (Copyright © 2024 Elsevier Inc. All rights reserved.)
    • Contributed Indexing:
      Keywords: Artificial intelligence; Fibroids; Natural language processing; Racial bias
    • Publication Date:
      Date Created: 20240501 Date Completed: 20240513 Latest Revision: 20240513
    • Publication Date:
      20240514
    • Accession Number:
      10.1016/j.clinimag.2024.110164
    • Accession Number:
      38691911