Jesse M Ehrenfeld1, Keanan Gabriel Gottlieb2, Lauren Brittany Beach3, Shelby E Monahan4, Daniel Fabbri5. 1. Vanderbilt University, Departments of Anesthesiology, Surgery, Biomedical Informatics, Health Policy; Nashville, Tennessee. 2. Vanderbilt University Medical Center, Program for LGBTQ Health; Nashville, Tennessee. 3. Northwestern University, Institute for Sexual and Gender Minority Health & Wellbeing, Chicago, Illinois. 4. Western Kentucky University, Department of Psychology; Bowling Green, Kentucky. 5. Vanderbilt University, Departments of Biomedical Informatics & Computer Science; Nashville, Tennessee.
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.
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.
Entities:
Keywords:
Electronic Health Records; Natural Language Processing; Transgender; Utilization
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