A deep learning approach for transgender and gender diverse patient identification in electronic health records.

Journal: Journal of biomedical informatics
PMID: 37778672

Abstract

BACKGROUND: Although accurate identification of gender identity in the electronic health record (EHR) is crucial for providing equitable health care, particularly for transgender and gender diverse (TGD) populations, it remains a challenging task due to incomplete gender information in structured EHR fields.

Authors

  • Yining Hua
    Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Liqin Wang
    Brigham and Women's Hospital, Boston, MA, USA.
  • Vi Nguyen
    Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. Electronic address: vnguyen31@bwh.harvard.edu.
  • Meghan Rieu-Werden
    Division of General Medicine, Massachusetts General Hospital, Boston, MA, USA. Electronic address: mrieuwerden@mgh.harvard.edu.
  • Alex McDowell
    Health Policy Research Institute, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA; Department of Health Care Policy, Harvard Medical School, Boston, MA, USA. Electronic address: amcdowell4@mgh.harvard.edu.
  • David W Bates
    Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
  • Dinah Foer
    Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA; Division of Allergy and Clinical Immunology, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
  • Li Zhou
    School of Education, China West Normal University, Nanchong, Sichuan, China.

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