TY - JOUR
T1 - Development of a natural language processing algorithm to identify and evaluate transgender patients in electronic health record systems
AU - Ehrenfeld, Jesse M.
AU - Gottlieb, Keanan Gabriel
AU - Beach, Lauren Brittany
AU - Monahan, Shelby E.
AU - Fabbri, Daniel
N1 - Funding Information:
The project described is supported by the National Institute on Minority Health and Health Disparities (NIMHD) Grant Number U54MD008173, a component of the National Institutes of Health (NIH) and Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIMHD or NIH.
Publisher Copyright:
© 2019 Ethnicity and Disease, Inc.. All rights reserved.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Electronic health records
KW - Natural language processing
KW - Transgender
KW - Utilization
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U2 - 10.18865/ED.29.S2.441
DO - 10.18865/ED.29.S2.441
M3 - Article
C2 - 31308617
AN - SCOPUS:85069941515
SN - 1049-510X
VL - 29
SP - 441
EP - 450
JO - Ethnicity and Disease
JF - Ethnicity and Disease
ER -
