A Computable Phenotype for the Identification of Sexual and Gender Minorities in Electronic Health Records

AMIA Annu Symp Proc. 2024 Jan 11:2023:1057-1066. eCollection 2023.


Sexual gender minorities, including lesbian, gay, and bisexual (LGB) individuals face unique challenges due to discrimination, stigma, and marginalization, which negatively impact their well-being. Electronic health record (EHR) systems present an opportunity for LGB research, but accurately identifying LGB individuals in EHRs is challenging. Our study developed and validated a rule-based computable phenotype (CP) to identify LGB individuals and their subgroups using both structured data and unstructured clinical narratives from a large integrated health system. Validating against a sample of 537 chart-reviewed patients, our three best performing CP algorithms balancing different performance metrics, each achieved sensitivity of 1.000, PPV of 0.982, and F1-score of 0.875 in identifying LGB individuals, respectively. Applying the three best-performing CPs, our study also found that the LGB population is younger and experiences a disproportionate burden of adverse health outcomes, particularly mental health distress.

MeSH terms

  • Bisexuality / psychology
  • Electronic Health Records
  • Female
  • Humans
  • Mental Disorders* / epidemiology
  • Mental Health
  • Sexual and Gender Minorities*