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Development of a Natural Language Processing Algorithm to Identify and Evaluate Transgender Patients in Electronic
Jesse M Ehrenfeld1, Keanan Gabriel Gottlieb2, Lauren Brittany Beach3
1Vanderbilt University, Departments of Anesthesiology, Surgery, Biomedical Informatics, Health Policy; Nashville, Tennessee.
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.
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