ArticleJournal of the American Medical Informatics Association : JAMIA2026
A scoping review of models to identify transgender patients in electronic health records.
Article in Journal of the American Medical Informatics Association : JAMIA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
objectiveElectronic health records (EHRs) lack a widely adopted standard for recording transgender and gender diverse (TGD) status, complicating research on TGD health. Computational models have been developed to identify TGD individuals in EHRs; however, gaps remain in understanding which components contribute to stronger phenotyping approaches. This scoping review evaluates EHR-based models for identifying TGD individuals, focusing on identifier types, performance, external validation, and ethical reporting to guide best practices. MATERIALS AND
methodsWe searched PubMed, CINAHL, Web of Science, and Embase for peer-reviewed articles published before January 2024, following PRISMA-ScR guidelines. Included studies used EHR data to identify TGD individuals, verified TGD status, reported or allowed calculation of positive predictive value (PPV), and listed identifiers. Two authors screened and extracted data. We categorized models by data type and logic (structured, unstructured, and multimodal), summarized PPV distributions, and synthesized author-reported ethical considerations.
resultsFourteen studies describing 50 models met inclusion criteria. Models using TGD-related diagnostic codes alone (n = 11) or requiring both structured and unstructured data (n = 6) showed the highest mean PPVs (85.3% and 97.1%). Models validated on larger confirmed TGD cohorts reported more stable performance, but external validation was rare. Most studies minimally addressed ethics; only 3 described protective measures or stakeholder engagement. DISCUSSION: Phenotyping of TGD individuals in EHR data remains heterogeneous in design and ethical transparency. Reported PPVs should be interpreted cautiously, as performance is influenced by study design, sample size, and verification methods.
conclusionsOur recommendations emphasize the components that strengthen phenotyping approaches-identifier choice, multimodal intersection logic, validation practices, and ethical safeguards-rather than endorsing any single model.
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