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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 2 of 2 Research Studies DisplayedHua Y, Wang L, Nguyen V
A deep learning approach for transgender and gender diverse patient identification in electronic health records.
The research described in this article used transgender and gender diverse (TGD) populations as a case study to build an accurate patient gender identity predictive, deep learning model; the goal was to address challenges in identifying relevant patient-level information from electronic health record (EHR) data. Participants were adult patients in a large healthcare system in Boston, MA. The deep learning model significantly outperformed rule-based algorithms. The researchers concluded that future work should evaluate additional diverse data sources for more generalizable algorithms.
AHRQ-funded; HS028916.
Citation: Hua Y, Wang L, Nguyen V .
A deep learning approach for transgender and gender diverse patient identification in electronic health records.
J Biomed Inform 2023 Nov; 147:104507. doi: 10.1016/j.jbi.2023.104507..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Vulnerable Populations
Thompson HM
Stakeholder experiences with gender identity data capture in electronic health records: implementation effectiveness and a visibility paradox.
Advocates have endorsed transgender visibility via gender identity (GI) data capture with the advent of the Affordable Care Act and electronic health record (EHR) requirements. Visibility in data in order to enumerate a population contrasts with ways in which other LGBT and public health scholars have deployed these concepts. This article aims to assess the effectiveness of GI data capture in EHRs and implications for trans health care quality improvements and research.
AHRQ-funded; HS026385.
Citation: Thompson HM .
Stakeholder experiences with gender identity data capture in electronic health records: implementation effectiveness and a visibility paradox.
Health Educ Behav 2021 Feb;48(1):93-101. doi: 10.1177/1090198120963102.
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Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Health Services Research (HSR), Vulnerable Populations, Sex Factors