LGBTQ+ Perspectives on Conducting Genomic Research on Sexual Orientation and Gender Identity.

We conducted in-depth, semi-structured interviews with LGBTQ+-identified individuals (n = 31) to explore the range of LGBTQ+ perspectives on genomic research using either sexual orientation or gender identity (SOGI) data. Most interviewees presumed that research would confirm genetic contributions to sexual orientation and gender identity. Primary hopes for such confirmation included validating LGBTQ+ identities, improved access to and quality of healthcare and other resources, and increased acceptance in familial, socio-cultural, and political environments. Areas of concern included threats of pathologizing and medicalizing LGBTQ+ identities and experiences, undermining reproductive rights, gatekeeping of health or social systems, and malicious testing or misuse of genetic results, particularly for LGBTQ+ youth. Overall, interviewees were divided on the acceptability of genomic research investigating genetic contributions to sexual orientation and gender identity. Participants emphasized researchers' ethical obligations to LGBTQ+ individuals and endorsed engagement with LGBTQ+ communities throughout all aspects of genomic research using SOGI data.

Development of a Natural Language Processing Algorithm to Identify and Evaluate Transgender Patients in Electronic Health Record Systems.

Objective: To create a natural language processing (NLP) algorithm to identify transgender patients in electronic health records. Design: We developed an NLP algorithm to identify patients (keyword + billing codes). Patients were manually reviewed, and their health care services categorized by billing code. Setting: Vanderbilt University Medical Center. Participants: 234 adult and pediatric transgender patients. Main Outcome Measures: Number of transgender patients correctly identified and categorization of health services utilized. Results: We identified 234 transgender patients of whom 50% had a diagnosed mental health condition, 14% were living with HIV, and 7% had diabetes. Largely driven by hormone use, nearly half of patients attended the Endocrinology/Diabetes/Metabolism clinic. Many patients also attended the Psychiatry, HIV, and/or Obstetrics/Gynecology clinics. The false positive rate of our algorithm was 3%. Conclusions: Our novel algorithm correctly identified transgender patients and provided important insights into health care utilization among this marginalized population.