An automated algorithm using free-text clinical notes to improve identification of transgender people. (2nd March 2021)

• Record Type: Journal Article
• Authors: Xie, Fagen; Getahun, Darios; Quinn, Virginia P.; Im, Theresa M.; Contreras, Richard; Silverberg, Michael J.; Baird, Tisha C.; Nash, Rebecca; Cromwell, Lee; Roblin, Douglas; Hoffman, Trenton; Goodman, Michael
• Is Part Of: Informatics for health & social care. Volume 46:Number 1(2021)
• Page Start: 18
• Page End: 28
• Publication Date: 2021-03-02
• Subjects: Transgender -- natural language processing -- computerized algorithm -- rule-based; Medicine -- Information services -- Periodicals; Medical informatics -- Periodicals; Medicine -- Data processing -- Periodicals; 025.0661
• DOI: 10.1080/17538157.2020.1828890
• Languages: English
• ISSNs: 1753-8157

Abstract

ABSTRACT: Accurate identification of transgender persons is a critical first step in conducting transgender health studies. To develop an automated algorithm for identifying transgender individuals from electronic medical records (EMR) using free-text clinical notes. The development and validation of the algorithm was based on data from an integrated healthcare system that served as a participating site in the multicenter Study of Transition Outcomes and Gender. The training and test datasets each contained a total of 300 individuals identified between 2006 and 2014. Both datasets underwent a full medical record review by experienced research abstractors. The validated algorithm was then implemented to identify transgender individuals in the EMR using all clinical notes of patients that received care between January 1, 2015 and June 30, 2018. Validation of the algorithm against the full chart review demonstrated a high degree of accuracy with 97% sensitivity, 95% specificity, 94% positive predictive value, and 97% negative predictive value. The algorithm classified 7, 409 individuals (3.5%) as "Definitely transgender" and 679 individuals (0.3%) as "Probably transgender" out of 212, 138 candidates with a total of 378, 641 clinical notes. The computerized NLP algorithm can support essential efforts to improve the health of transgender people.