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

Yongqiu Li, Xing He, Christopher Wheldon, Yonghui Wu, Mattia Prosperi, Elizabeth A. Shenkman, Michael S. Jaffee, Jingchuan Guo, Fei Wang, Yi Guo, Jiang Bian

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)1057-1066
Number of pages10
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2023
StatePublished - 2023

Keywords

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

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