biostudies-literatureStreed CGAmerican Heart AssociationDoris Duke Charitable FoundationAmerican Heart Association-American Stroke AssociationNational Heart, Lung, and Blood InstituteNHLBI NIH HHSBoston University Chobanian and Avedisian School of Medicine Department of Medicine Career Investment1047-1055https://www.ebi.ac.uk/biostudies/studies/S-EPMC10198536biostudies-literatureUnknown30(6)<h4>Objective</h4>To adapt and validate an algorithm to ascertain transgender and gender diverse (TGD) patients within electronic health record (EHR) data.<h4>Methods</h4>Using a previously unvalidated algorithm of identifying TGD persons within administrative claims data in a multistep, hierarchical process, we validated this algorithm in an EHR data set with self-reported gender identity.<h4>Results</h4>Within an EHR data set of 52 746 adults with self-reported gender identity (gold standard) a previously unvalidated algorithm to identify TGD persons via TGD-related diagnosis and procedure codes, and gender-affirming hormone therapy prescription data had a sensitivity of 87.3% (95% confidence interval [CI] 86.4-88.2), specificity of 98.7% (95% CI 98.6-98.8), positive predictive value (PPV) of 88.7% (95% CI 87.9-89.4), and negative predictive value (NPV) of 98.5% (95% CI 98.4-98.6). The area under the curve (AUC) was 0.930 (95% CI 0.925-0.935). Steps to further categorize patients as presumably TGD men versus women based on prescription data performed well: sensitivity of 97.6%, specificity of 92.7%, PPV of 93.2%, and NPV of 97.4%. The AUC was 0.95 (95% CI 0.94-0.96).<h4>Conclusions</h4>In the absence of self-reported gender identity data, an algorithm to identify TGD patients in administrative data using TGD-related diagnosis and procedure codes, and gender-affirming hormone prescriptions performs well.Journal of the American Medical Informatics Association : JAMIAValidation of an administrative algorithm for transgender and gender diverse persons against self-report data in electronic health records.PMC10198536K01 HL151902R01HL141434R01HL0925772022061NHLBI 1K01HL151902-01A1R01 HL092577AHA 20CDA35320148R01 HL141434AHA_18SFRN34110082Paasche-Orlow MKStreed CGJasuja GKKing DTangpricha VGrasso CPoteat TMukherjee MShapira-Daniels ACabral HReisner SLMayer KHBenjamin EJfalseValidation of an administrative algorithm for transgender and gender diverse persons against self-report data in electronic health records.<h4>Objective</h4>To adapt and validate an algorithm to ascertain transgender and gender diverse (TGD) patients within electronic health record (EHR) data.<h4>Methods</h4>Using a previously unvalidated algorithm of identifying TGD persons within administrative claims data in a multistep, hierarchical process, we validated this algorithm in an EHR data set with self-reported gender identity.<h4>Results</h4>Within an EHR data set of 52 746 adults with self-reported gender identity (gold standard) a previously unvalidated algorithm to identify TGD persons via TGD-related diagnosis and procedure codes, and gender-affirming hormone therapy prescription data had a sensitivity of 87.3% (95% confidence interval [CI] 86.4-88.2), specificity of 98.7% (95% CI 98.6-98.8), positive predictive value (PPV) of 88.7% (95% CI 87.9-89.4), and negative predictive value (NPV) of 98.5% (95% CI 98.4-98.6). The area under the curve (AUC) was 0.930 (95% CI 0.925-0.935). Steps to further categorize patients as presumably TGD men versus women based on prescription data performed well: sensitivity of 97.6%, specificity of 92.7%, PPV of 93.2%, and NPV of 97.4%. The AUC was 0.95 (95% CI 0.94-0.96).<h4>Conclusions</h4>In the absence of self-reported gender identity data, an algorithm to identify TGD patients in administrative data using TGD-related diagnosis and procedure codes, and gender-affirming hormone prescriptions performs well.2023-01-01T00:00:00Z2023 May2026-06-03T21:31:46.344Z2025-07-12T03:04:16.081ZS-EPMC101985363692128710.1093/jamia/ocad039