TY - JOUR
T1 - A Computable Phenotype for the Identification of Sexual and Gender Minorities in Electronic Health Records
AU - Li, Yongqiu
AU - He, Xing
AU - Wheldon, Christopher
AU - Wu, Yonghui
AU - Prosperi, Mattia
AU - Shenkman, Elizabeth A.
AU - Jaffee, Michael S.
AU - Guo, Jingchuan
AU - Wang, Fei
AU - Guo, Yi
AU - Bian, Jiang
N1 - Publisher Copyright:
©2023 AMIA - All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Bisexuality/psychology
KW - Electronic Health Records
KW - Female
KW - Humans
KW - Mental Disorders/epidemiology
KW - Mental Health
KW - Sexual and Gender Minorities
UR - http://www.scopus.com/inward/record.url?scp=85182541600&partnerID=8YFLogxK
UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10785915/
M3 - Article
C2 - 38222414
AN - SCOPUS:85182541600
SN - 1559-4076
VL - 2023
SP - 1057
EP - 1066
JO - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
ER -