OBJECTIVE: To adapt and validate an algorithm to ascertain transgender and gender diverse (TGD) patients within electronic health record (EHR) data. METHODS: Using a previously unvalidated algorithm of identifying TGD persons within administrative claims data in a multistep, hierarchical process, we validated this algorithm in an EHR data set with self-reported gender identity. RESULTS: Within an EHR data set of 52 746 adults with self-reported gender identity (gold standard) a previously unvalidated algorithm to identify TGD persons via TGD-related diagnosis and procedure codes, and gender-affirming hormone therapy prescription data had a sensitivity of 87.3% (95% confidence interval [CI] 86.4-88.2), specificity of 98.7% (95% CI 98.6-98.8), positive predictive value (PPV) of 88.7% (95% CI 87.9-89.4), and negative predictive value (NPV) of 98.5% (95% CI 98.4-98.6). The area under the curve (AUC) was 0.930 (95% CI 0.925-0.935). Steps to further categorize patients as presumably TGD men versus women based on prescription data performed well: sensitivity of 97.6%, specificity of 92.7%, PPV of 93.2%, and NPV of 97.4%. The AUC was 0.95 (95% CI 0.94-0.96). CONCLUSIONS: In the absence of self-reported gender identity data, an algorithm to identify TGD patients in administrative data using TGD-related diagnosis and procedure codes, and gender-affirming hormone prescriptions performs well.

OBJECTIVES: To propose an approach for semantic and functional data harmonization related to sex and gender constructs in electronic health records (EHRs) and other clinical systems for implementors, as outlined in the National Academies of Sciences, Engineering, and Medicine (NASEM) report Measuring Sex, Gender Identity, and Sexual Orientation and the Health Level 7 (HL7) Gender Harmony Project (GHP) product brief "Gender Harmony-Modeling Sex and Gender Representation, Release 1." MATERIALS AND METHODS: Authors from both publications contributed to a plan for data harmonization based upon fundamental principles in informatics, including privacy, openness, access, legitimate infringement, least intrusive alternatives, and accountability. RESULTS: We propose construct entities and value sets that best align with both publications to allow the implementation of EHR data elements on gender identity, recorded sex or gender, and sex for clinical use in the United States. We include usability- and interoperability-focused reasoning for each of these decisions, as well as suggestions for cross-tabulation for populations. DISCUSSION AND CONCLUSION: Both publications agree on core approaches to conceptualization and measurement of sex- and gender-related constructs. However, some clarifications could improve our ability to assess gender modality, alignment (or lack thereof) between gender identity and assigned gender at birth, and address both individual-level and population-level health inequities. By bridging the GHP and NASEM recommendations, we provide a path forward for implementation of sex- and gender-related EHR elements. Suggestions for implementation of gender identity, recorded sex or gender, and sex for clinical use are provided, along with semantic and functional justifications.

The systematic documentation of sexual orientation and gender identity data in electronic health records can improve patient-centered care and help to identify and address health disparities affecting sexual and gender minority populations. Although there are existing guidelines for sexual orientation and gender identity data among adult patients, there are not yet standard recommendations for pediatric patients. In this article, we discuss methods that pediatric primary care organizations can use to collect and document sexual orientation and gender identity information with children and adolescents in electronic health records. These recommendations take into consideration children's developmental stages, the role of caregivers, and the need to protect the privacy of this information. We also focus on the current limitations of electronic health records in capturing the nuances of sexual and gender minority identities and make suggestions for addressing these limitations.

OBJECTIVE: Accurate representation of clinical sex and gender identity in interoperable clinical systems is a major challenge for organizations intent on improving outcomes for sex- and gender-marginalized people. Improved data collection has been hindered by the historical approach that presumed a single, often binary, datum was sufficient. We describe the Health Level Seven International (HL7) Gender Harmony logical model that proposes an improved approach. MATERIALS AND METHODS: The proposed solution was developed via an American National Standards Institute (ANSI)-certified collaborative balloted process. As an HL7 Informative Document, it is an HL7 International-balloted consensus on the subject of representing sex and representing gender in clinical systems based on work of the gender harmony project led by the HL7 Vocabulary Work Group. RESULTS: The Gender Harmony Model is a logical model that provides a standardized approach that is both backwards-compatible and an improvement to the meaningful capture of gender identity, recorded sex or recorded gender, a sex for clinical use, the name to use, and pronouns that are affirmative and inclusive of gender-marginalized people. CONCLUSION: Most clinical systems and current standards in health care do not meaningfully address, nor do they consistently represent, sex and gender diversity, which has impeded interoperability and led to suboptimal health care. The Gender Harmony Project was formed to create more inclusive health information exchange standards to enable a safer, higher-quality, and embracing healthcare experience. The Gender Harmony Model provides the informative guidance for standards developers to implement a more thorough technical design that improves the narrow binary design used in many legacy clinical systems.

Journal of the American Medical Informatics Association, ocab196, https://doi.org/10.1093/jamia/ocab196 In the originally published version of this manuscript, there were several errors, which are listed in this corrigendum as follows: The acronym “SCFU” in the sections “Sex and gender in clinical systems”, “Health Level 7 Version 2”, “Digital Imaging and Communications in Medicine” and “Gender harmony logical model elements” should read “SFCU”. The acronym “2SLGBTQIA+” which appears in the “Background” section has been added to “Table 1: Glossary of additional key terms” with the following definition “acronym meaning two-spirit, lesbian, gay, bisexual, transgender, queer, intersexual, asexual, while “+” indicates that some individuals may express gender and sexuality in others ways”. These have been corrected online.

Recent advances in electronic health records and health information technology are providing new opportunities to improve the quality of care for transgender and gender diverse people, a population that experiences significant health disparities. This article recommends changes to electronic health record systems that have the potential to optimize gender-affirming care. Specifically, we discuss the importance of creating an anatomical inventory form that captures organ diversity, and of developing clinical decision support tools and population health management systems that consider each patient's gender identity, sex assigned at birth, and anatomy.

Lesbian, gay, bisexual, transgender, and queer (LGBTQ) people experience significant health disparities across the life course and require health care that addresses their unique needs. Collecting information on the sexual orientation and gender identity (SO/GI) of patients and entering SO/GI data in electronic health records has been recommended by the Institute of Medicine, the Joint Commission, and the Health Resources and Services Administration as fundamental to improving access to and quality of care for LGBTQ people. Most healthcare organizations, however, have yet to implement a system to collect SO/GI data due to multiple barriers. This report addresses those concerns by presenting recommendations for planning and implementing high-quality SO/GI data collection in primary care and other health care practices based on current evidence and best practices developed by a federally qualified health center and leader in LGBTQ health care.