Development of a Natural Language Processing Algorithm to Identify and Evaluate Transgender Patients in Electronic Health Record Systems

Jesse M. Ehrenfeld, Keanan Gabriel Gottlieb, Lauren Brittany Beach, Shelby E. Monahan, Daniel Fabbri

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Objective: To create a natural language processing (NLP) algorithm to identify transgender patients in electronic health records. Design: We developed an NLP algorithm to identify patients (keyword + billing codes). Patients were manually reviewed, and their health care services categorized by billing code. Setting: Vanderbilt University Medical Center. Participants: 234 adult and pediatric transgender patients. Main Outcome Measures: Number of transgender patients correctly identified and categorization of health services utilized. Results: We identified 234 transgender patients of whom 50% had a diagnosed mental health condition, 14% were living with HIV, and 7% had diabetes. Largely driven by hormone use, nearly half of patients attended the Endocrinology/Diabetes/Metabolism clinic. Many patients also attended the Psychiatry, HIV, and/or Obstetrics/Gynecology clinics. The false positive rate of our algorithm was 3%. Conclusions: Our novel algorithm correctly identified transgender patients and provided important insights into health care utilization among this marginalized population.

Original languageEnglish (US)
Pages (from-to)441-450
Number of pages10
JournalEthnicity & disease
Volume29
DOIs
StatePublished - 2019

Keywords

  • Electronic Health Records
  • Natural Language Processing
  • Transgender
  • Utilization

ASJC Scopus subject areas

  • Epidemiology

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