METHODS
Authors searched the National Library of Medicine’s PubMed, Scopus, and the American Psychological Association Psyc Info’s databases to identify studies published in the United States that applied CPs to identify TG people within electronic health care data. Multiple combinations of search terms included: “transgender” “electronic health records” “computational phenotype” and “electronic medical records” (full search strategy in Supplemental Table 1 ). The electronic search included all papers published through September 2022. Our narrative review focused on research articles applying algorithms to electronic health care databases to identify TG patients. We excluded studies that used surveys, did not use data from the United States, used qualitative methodologies, or lesbian, gay, bisexual, transgender, and queer (LGBTQ) research that did not include TG people or separate their results. We excluded these studies as we wanted to focus on current measures within the United States healthcare system, where gender identity information is not often available. We also wanted to ensure United States health insurance codes were utilized, as this is an emerging area of identifying TG patients in large databases. Two reviewers (T.G.B. and J.H.C.) independently reviewed the citation index for possible inclusion, while discrepancies were resolved through consensus. Papers were reviewed and analyzed through Covidence.7 While not a comprehensive systematic review, we follow the PRISMA statement to report our results in the spirit of transparency and reproducibility.