An Environmental Scan of Sex and Gender in Electronic Health Records: Analysis of Public Information Sources

Feasibility of new patient dose management tool in digital radiography: Using clinical exposure index data of mobile chest radiography in a large university hospital

The clinical anteroposterior (AP) chest images taken with a mobile radiography system were analyzed in this study to utilize the clinical exposure index (EI) as a patient dose-monitoring tool. The digital imaging and communications in medicine header of 6048 data points exposed under the 90 kVp and 2.5 mAs were extracted using Python for identifying the distribution of clinical EI. Even under the same exposure conditions, the clinical EI distribution was 137.82-4924.38. To determine the cause, the effect of a patient's body shape on EI was confirmed using actual clinical chest AP image data binarized into 0 and 255-pixel values using Python. As a result, the relationship between the direct X-ray area of the chest AP image, the higher the clinical EI, the larger the rate of the direct X-ray area. A conversion equation was also derived to infer entrance surface dose through clinical EI based on the patient thickness. This confirmed the possibility of directly monitoring patient dose through EI without a dosimeter in real-time. Therefore, to use the clinical EI of the mobile radiography system as a patient dose-monitoring tool, the derivation method of clinical EI considers several factors, such as the relationship between patient factors.

Examining Mental Health Disorders in Overweight and Obese Pediatric Patients

INTRODUCTION

We investigated the frequency and variation in three mental health diagnoses among obese or overweight children and adolescents.

METHOD

Logistic regression was used to examine the association between the outcome variables-anxiety, depression, and adjustment disorders-with the following covariates: overweight/obesity status, sex, age, and race.

RESULTS

Findings show anxiety, depressive, and adjustment disorder diagnoses were significantly higher for overweight or obese youth in our sample. In addition, diagnosis rates for one or more of these disorders increase as children grow into adolescence. Furthermore, we found significantly higher rates of depression and significantly lower rates of anxiety among youth who live in places with higher rates of poverty.

DISCUSSION

Findings indicate a target age for providers to focus on mental health screening among overweight/obese patients: (1) early adolescence (aged 11-14 years) for depressive and adjustment disorders and (2) early childhood (aged 2-4 years) for anxiety disorder.

Digital health systems-let's talk about sex (and gender)

Historically, within digital health information systems, sex and gender have been conflated as a single concept and often have been limited to a binary answer. This has led to inappropriate care, erosion of client trust and avoidance of the healthcare system. Health leaders can improve care for all clients with technical and clinical information practice initiatives. While procurement processes could require digital health systems that utilize modern Gender, Sex, and Sexual Orientation (GSSO) terminology, for most health leaders, technical initiatives will focus on modernizing existing systems to the maximum extent possible. Terminology updates may not be immediately visible to clients, but providing the correct information to clinicians will support respectful client encounters. Simultaneously, clinical information practice initiatives can directly affect clinical encounters. Change management strategies need to include all levels of employees and redesign tools and workflows to support modernized information handling practices.

Sex trouble: Sex/gender slippage, sex confusion, and sex obsession in machine learning using electronic health records

False assumptions that sex and gender are binary, static, and concordant are deeply embedded in the medical system. As machine learning researchers use medical data to build tools to solve novel problems, understanding how existing systems represent sex/gender incorrectly is necessary to avoid perpetuating harm. In this perspective, we identify and discuss three factors to consider when working with sex/gender in research: "sex/gender slippage," the frequent substitution of sex and sex-related terms for gender and vice versa; "sex confusion," the fact that any given sex variable holds many different potential meanings; and "sex obsession," the idea that the relevant variable for most inquiries related to sex/gender is sex assigned at birth. We then explore how these phenomena show up in medical machine learning research using electronic health records, with a specific focus on HIV risk prediction. Finally, we offer recommendations about how machine learning researchers can engage more carefully with questions of sex/gender.

Overcoming technical and cultural challenges to delivering equitable care for LGBTQ+ individuals in a rural, underserved area

The lesbian, gay, bisexual, transgender, queer, or questioning (LGBTQ+) community is vulnerable to health-care disparities. Many health-care organizations are working to collect sexual orientation and gender identity in their electronic health records (EHRs), with the goal of providing more inclusive care to their LGBTQ+ patients. There are significant human and technical barriers to making these efforts successful. Based on our 5-year experience at Geisinger (an integrated health system located in a rural, generally conservative area), this case report provides insights to overcome challenges in 4 critical areas: (1) enabling the EHR to collect and use information to support the health-care needs of LGBTQ+ patients, (2) building a culture of awareness and caring, empowering members of the health-care team to break down barriers of misunderstanding and mistrust, (3) developing services to support the needs of LGBTQ+ patients, and (4) partnering with local communities to become a trusted health-care provider.

Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review

BACKGROUND

A high risk of mental health or substance addiction issues among sexual and gender minority populations may have more nuanced characteristics that may not be easily discovered by traditional statistical methods.

OBJECTIVE

This review aims to identify literature studies that used machine learning (ML) to investigate mental health or substance use concerns among the lesbian, gay, bisexual, transgender, queer or questioning, and two-spirit (LGBTQ2S+) population and direct future research in this field.

METHODS

The MEDLINE, Embase, PubMed, CINAHL Plus, PsycINFO, IEEE Xplore, and Summon databases were searched from November to December 2020. We included original studies that used ML to explore mental health or substance use among the LGBTQ2S+ population and excluded studies of genomics and pharmacokinetics. Two independent reviewers reviewed all papers and extracted data on general study findings, model development, and discussion of the study findings.

RESULTS

We included 11 studies in this review, of which 81% (9/11) were on mental health and 18% (2/11) were on substance use concerns. All studies were published within the last 2 years, and most were conducted in the United States. Among mutually nonexclusive population categories, sexual minority men were the most commonly studied subgroup (5/11, 45%), whereas sexual minority women were studied the least (2/11, 18%). Studies were categorized into 3 major domains: web content analysis (6/11, 54%), prediction modeling (4/11, 36%), and imaging studies (1/11, 9%).

CONCLUSIONS

ML is a promising tool for capturing and analyzing hidden data on mental health and substance use concerns among the LGBTQ2S+ population. In addition to conducting more research on sexual minority women, different mental health and substance use problems, as well as outcomes and future research should explore newer environments, data sources, and intersections with various social determinants of health.

Gender harmony: improved standards to support affirmative care of gender-marginalized people through inclusive gender and sex representation

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.

Toward an inclusive digital health system for sexual and gender minorities in Canada

Most digital health systems (DHS) are unable to capture gender, sex, and sexual orientation (GSSO) data beyond a single binary attribute with female and male options. This binary system discourages access to preventative screening and gender-affirming care for sexual and gender minority (SGM) people. We conducted this 1-year multi-method project and cocreated an action plan to modernize GSSO information practices in Canadian DHS. The proposed actions are to: (1) Envisage an equity- and SGM-oriented health system; (2) Engage communities and organizations to modernize GSSO information practices in DHS; (3) Establish an inclusive GSSO terminology; (4) Enable DHS to collect, use, exchange, and reuse standardized GSSO data; (5) Integrate GSSO data collection and use within organizations; (6) Educate staff to provide culturally competent care and inform patients on the need for GSSO data; and (7) Establish a central hub to coordinate efforts.

Culturally Competent Gender, Sex, and Sexual Orientation Information Practices and Electronic Health Records: Rapid Review

BACKGROUND

Outdated gender, sex, and sexual orientation (GSSO) information practices in health care contribute to health inequities for sexual and gender minorities (SGMs). Governments, statistics agencies, and health care organizations are developing and implementing modernized practices that support health equity for SGMs. Extending our work, we conducted a rapid review of grey literature to explore information practices that support quality health care for SGMs.

OBJECTIVE

The aim of this rapid review of grey literature was to elucidate modern GSSO information practices from leading agencies for adaptation, adoption, and application by health care providers and organizations seeking to modernize outdated GSSO information practices that contribute to health inequities among SGMs.

METHODS

We searched MEDLINE and Google from 2015 to 2020 with terms related to gender, sex, sexual orientation, and electronic health/medical records for English-language grey literature resources including government and nongovernment organization publications, whitepapers, data standards, toolkits, health care organization and health quality practice and policy guides, conference proceedings, unpublished academic work, and statistical papers. Peer-reviewed journal articles were excluded, as were resources irrelevant to information practices. We also screened the reference sections of included articles for additional resources, and canvassed a working group of international topic experts for additional relevant resources. Duplicates were eliminated. ATLAS.ti was used to support analysis. Themes and codes were developed through an iterative process of writing and discussion with the research team.

RESULTS

Twenty-six grey literature resources met the inclusion criteria. The overarching themes that emerged from the literature were the interrelated behaviors, attitudes, and policies that constitute SGM cultural competence as follows: shared language with unambiguous definitions of GSSO concepts; welcoming and inclusive care environments and affirming practices to reduce barriers to access; health care policy that supports competent health care; and adoption of modernized GSSO information practices and electronic health record design requirements that address invisibility in health data.

CONCLUSIONS

Health equity for SGMs requires systemic change. Binary representation of sex and gender in electronic health records (EHRs) obfuscates natural and cultural diversity and, in the context of health care, places SGM patients at risk of clinical harm because it leads to clinical assumptions. Agencies and agents in health care need to be equipped with the knowledge and tools needed to cultivate modern attitudes, policies, and practices that enable health equity for SGMs. Adopting small but important changes in the language and terminology used in technical and social health care systems is essential for institutionalizing SGM competency. Modern GSSO information practices depend on and reinforce SGM competency in health care.