Purpose: We describe a novel algorithm for identifying transgender people and determining their male-to-female (MTF) or female-to-male (FTM) identity in electronic medical records of an integrated health system.
Methods: A computer program scanned Kaiser Permanente Georgia electronic medical records from January 2006 through December 2014 for relevant diagnostic codes, and presence of specific keywords (e.g., "transgender" or "transsexual") in clinical notes. Eligibility was verified by review of de-identified text strings containing targeted keywords, and if needed, by an additional in-depth review of records. Once transgender status was confirmed, FTM or MTF identity was assessed using a second program and another round of text string reviews.
Results: Of 813,737 members, 271 were identified as possibly transgender: 137 through keywords only, 25 through diagnostic codes only, and 109 through both codes and keywords. Of these individuals, 185 (68%, 95% confidence interval [CI]: 62%-74%) were confirmed as definitely transgender. The proportions (95% CIs) of definite transgender status among persons identified via keywords, diagnostic codes, and both were 45% (37%-54%), 56% (35%-75%), and 100% (96%-100%). Of the 185 definitely transgender people, 99 (54%, 95% CI: 46%-61%) were MTF, 84 (45%, 95% CI: 38%-53%) were FTM. For two persons, gender identity remained unknown. Prevalence of transgender people (per 100,000 members) was 4.4 (95% CI: 2.6-7.4) in 2006 and 38.7 (95% CI: 32.4-46.2) in 2014.
Conclusions: The proposed method of identifying candidates for transgender health studies is low cost and relatively efficient. It can be applied in other similar health care systems.
Keywords: Algorithm; Electronic medical records; Prevalence; Transgender.
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