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.