ArticleEthnicity & disease2019
Development of a Natural Language Processing Algorithm to Identify and Evaluate Transgender Patients in Electronic Health Record Systems.
Article in Ethnicity & disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Pooled it
- Stigmatizing Language in Gender-Expansive Patient Records: Corpus Development, Disparity Analysis, and Natural Language Processing-Based Detection Study.Journal of medical Internet research · 2026Article
- Development of a Keyword-Based Search Strategy to Identify Gender Diverse Youth in Electronic Medical Records.Transgender health · 2026Article
- Surgical and hormonal gender-affirming care: cross-domain determinants and artificial intelligence-enabled expansion.International journal for equity in health · 2026Review
- Addressing barriers and advancing equitable colorectal cancer screening in the lesbian, gay, bisexual, transgender, and queer/questioning population.World journal of methodology · 2026Review
- A scoping review of models to identify transgender patients in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- When algorithms infer gender: revisiting computational phenotyping with electronic health records data.Biology of sex differences · 2025Review
- Natural Language Processing for Identification of Hospitalized People Who Use Drugs: Cohort Study.JMIR AI · 2025Article
- Integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence for healthcare: a scoping review.PLOS digital health · 2025Article
- Validating a Case Definition for Transgender Adults Using Administrative Data.JAMA network open · 2025Article
- Beyond no harm: Advancing research on artificial intelligence for sexual and reproductive health and rights.npj women's health · 2025Article
- Article
- Transgender Individuals and Digital Health.Current HIV/AIDS reports · 2022Review
- Validation of a Natural Language Processing Algorithm for the Extraction of the Sleep Parameters from the Polysomnography Reports.Healthcare (Basel, Switzerland) · 2022Article
- Article
- Change in Gender on Record and Transgender Adults' Mental or Behavioral Health.American journal of preventive medicine · 2022Article
- A Single-Center Study of Adherence to Breast Cancer Screening Mammography Guidelines by Transgender and Non-Binary Patients.Annals of surgical oncology · 2022Article
- Implementation of Gender Identity and Assigned Sex at Birth Data Collection in Electronic Health Records: Where Are We Now?International journal of environmental research and public health · 2021Article
- Elevated Rates of Violence Victimization and Suicide Attempt Among Transgender and Gender Diverse Patients in an Urban, Safety Net Health System.World medical & health policy · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.