Development of a Natural Language Processing Algorithm to Identify and Evaluate Transgender Patients in Electronic Health Record Systems. Ethn Dis 2019;29(Suppl 2):441-450
Date
07/17/2019Pubmed ID
31308617Pubmed Central ID
PMC6604788DOI
10.18865/ed.29.S2.441Scopus ID
2-s2.0-85069941515 (requires institutional sign-in at Scopus site) 24 CitationsAbstract
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.
Author List
Ehrenfeld JM, Gottlieb KG, Beach LB, Monahan SE, Fabbri DAuthor
Jesse Ehrenfeld MD, MPH Sr Associate Dean, Director, Professor in the Advancing a Healthier Wisconsin Endowment department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AdolescentAdult
Aged
Aged, 80 and over
Algorithms
Child
Electronic Health Records
Female
Humans
Male
Middle Aged
Natural Language Processing
Transgender Persons
Young Adult
