Abstract
BACKGROUND: Lesbian, gay, bisexual, transgender, and queer (LGBTQ) populations have an increased risk of multiple adverse health outcomes. Capturing patient data on sexual orientation and gender identity (SOGI) in the electronic health record (EHR) can enable healthcare organizations to identify inequities in the provision of preventive health screenings and other quality of care services to their LGBTQ patients. However, organizations may not be familiar with methods for analyzing and interpreting SOGI data to detect health disparities. PURPOSE: To assess an approach for using SOGI EHR data to identify potential screening disparities of LGBTQ patients within distinct healthcare organizations. METHODS: Five US federally qualified health centers (FQHCs) retrospectively extracted three consecutive months of EHR patient data on SOGI and routine screening for cervical cancer, tobacco use, and clinical depression. The screening data were stratified across SOGI categories. Chi-Square and Fisher's Exact test were used to identify statistically significant differences in screening compliance across SOGI categories within each FQHC. RESULTS: In all FQHCs, cervical cancer screening percentages were lower among lesbian/gay patients than among bisexual and straight/heterosexual patients. In three FQHCs, cervical cancer screening percentages were lower for transgender men than for cisgender (i.e., not transgender) women. Within each FQHC, we observed statistically significant associations (P < 0.05) between SOGI categories and at least one screening measure. The small number of transgender patients, and limitations in EHR functionality, created challenges in interpretation of SOGI data. CONCLUSIONS: To our knowledge, this is the first published report of using SOGI data from EHRs to detect potential disparities in healthcare services to LGBTQ patients. Our finding that lesbian/gay and transgender male patients had lower cervical cancer screening rates compared to heterosexual, bisexual, and cisgender women, is consistent with the research literature and suggests that using SOGI EHR data to detect preventive screening disparities has value. EHR functionality should allow for cross-checking gender identity with sex assigned at birth to reduce errors in data interpretation. Additional functionality, like clinical decision support based on anatomical inventories rather than gender identity, is needed to more accurately identify services that transgender patients need.