A Computable Phenotype for the Identification of Sexual and Gender Minorities in Electronic Health Records.
Year: 2023
Type: conference-paper
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.
Source: PubMed
Institutions: University of Florida, Temple University, Weill Cornell Medicine
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
Awards: Together: Transforming and Translating Discovery to Improve Health, Using Electronic Health Records from a Large Clinical Data Research Network to Understand Cancer Burden and Cancer Risks Among Transgender and Gender Nonconforming (TGNC) Individuals, G9000030512, G9000036590, Artificial Intelligence and Counterfactually Actionable Responses to End HIV (AI-CARE-HIV)
OpenAlex ID: W4390889573