Addressing Health Disparities Among the Transgender and Non-binary Population Through the EHR: A literature review

Submitted by Allison Boatright, Stacy Stutzman, Michelle Dalton, Wendy Tolbert and Leigh Ann Breckenridge

Tags: bias discrimination diversity health disparities LGBT LGBTQ transgender

Addressing Health Disparities Among the Transgender and Non-binary Population Through the EHR: A literature review

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Lesbian, gay, bisexual, transgender, queer or questioning, intersex, asexual (LGBTQIA+)  is a community of individuals who identify themselves across spectrums of gender and sexuality. In recent years, efforts to increase inclusion and equality of individuals has prompted a change in language as well as highlighting barriers and prejudices regularly faced within all areas of life.  Exposure of discrepancies related to quality of care and inclusion of these individuals is fundamental. A study done by Russell, Pollitt and Grossman (2018) indicated improved mental health among LGBTQIA+ individuals when self-selected pronouns were used.  Because of the impact on mental health, reminding health care workers about the importance of using preferred pronouns and self-identified gender is crucial.

Within the larger group of LGBTQIA+, there is a specific gap in care among the transgender, gender nonconforming, and nonbinary populations. This population of individuals may prefer to use pronouns that differ from what society has historically assumed based on a person's assigned gender or appearance. Pronouns may include he/him/his, she/her/hers, they/them/theirs, and ze/zir/zirs.

As the 2020 U.S. census did not ask about gender identity, estimates had not been gathered to quantify the transgender and nonbinary population.  In 2021, however, the Williams Institute, a research center focused on sexual orientation and gender identity, published their results of two studies to identify demographics of this population (Wilson & Meyer, 2021). They found that more than one million adults age 18-60 living in the United States identify as nonbinary, which is approximately 11% of the LGBTQIA+ population (Wilson & Meyer, 2021). The surveys conducted also asked about stressful experiences and health and health care access among nonbinary adults (Wilson & Meyer, 2021). They found that 54% of responders feel less respected than others and 41% report poor or fair health, not including serious mental illness which was 51% (Wilson & Meyer, 2021). This data allows for more accurate assessment of the size of population as well as identifying broad areas for improvement.

The minority stress model illustrates how the processes of objective, subjective, internal, and external stressors all play a role in the subjection of minority groups to chronic stress related to stigmas. The Institute of Medicine (IOM) recommends that the "influences and impact of minority stress should be considered as central to LGBT health"" (2011).  In order to address this, a research opportunity recommended by the IOM was identified as "barriers to access (particularly related to identity disclosure and interactions with providers)"" (2011).  Addressing health care biases related to sexual orientation and gender identity is one way we can improve. Additionally, understanding culturally competent care by documenting gender identity and utilizing preferred pronouns is essential.  Biases interfere with decision making, rapport building, and effective communication; all of which can alter a patient's perceived and actual quality of care (Waryold & Kornahrens, 2020). Although complete elimination of these biases is unlikely, it is possible for providers to reduce their impact through unlearning stereotypes, education about the needs of LGBTQIA+ individuals, and increasing exposure and opportunities to care for this population (Waryold & Kornahrens, 2020). In addition, incorporating questions during the intake process of a patient including SO/GI and preferred pronouns can "support the patient's sense of belonging"" (Waryold & Kornahrens, 2020). Combined with the process of unlearning stereotypes, this can reduce experienced and expected discrimination by the transgender and nonbinary population.

According to Mahr et al, (2021), medical education has not yet recognized gender competence as a core clinical skill and competency. Assessing the compliance and understanding of healthcare professionals with the gender identification section in the EHR is a critical component. These findings may present biases, lack of education, or full compliance with the SIGI portion of the EHR. If biased opinions are identified, these negative attitudes can in part be attributed to lack of training or clinical experience in working with the transgender or gender diverse (TGD) individuals (Mahr et al., 2021).  Furthermore, addressing TGD individuals by the name they have chosen has clearly mitigated depression, suicidal ideation and suicidal behaviors.  (Mahr et al., 2021) Therefore, gender competence training should start early during health care education pathways for all health care professionals.

The newly established SO/GI sections in the electronic health record (EHR) is unchartered territory for patient satisfaction survey results. Eliminating LGBT health disparities and enhancing efforts to improve LGBT health are necessary to ensure that LGBT individuals can lead long, healthy lives ( In doing this, the patient-provider relationship is strengthened, and trust is established. 

In 2015, a huge advancement occurred for the LGBTQIA+ community when the U.S. Centers for Medicare and Medicaid Services (CMS) and the office of the National Coordinator for Health Information Technology (ONC) decided to mandate that all EHR systems certified under the Meaningful Use incentive program must include the ability to record SO/GI (Cahill et al., 2016). Thompson et al. (2021) conducted an assessment to benchmark implementation penetration and quality of gender identity-related data collection in U.S. academic health centers since the Meaningful Use incentivization for this data collection went into effect in 2014. Data was gathered from Rush University Medical Center (RUMC), an urban academic health center in Chicago. RUMC uses Epic as its electronic health record. Figure 1 indicates the new form required in RUMC's EHR and is similar to the one utilized by many other HER's across the country.

The needs assessment consisted of specifically collected data from patient's records with unplanned admissions in 2020. According to their findings, approximately one-quarter of patient records included gender identity data, and one percent of them indicated a transgender or nonbinary (TGNB) status (Thompson et al., 2021). Thompson et al. (2021) recognized that being the first analysis of this data, there would be limitations to their findings but would also reveal areas of improvement for SO/GI data collection in hospitals. They highlighted the importance of widespread training regarding gender-affirming care and inclusive language and the development of SO/GI data quality standards (Thompson et al., 2021).

According to Morris et al, (2019), the LGBTQIA+ population has increased rates of disparities with cardiovascular disease, obesity, cigarette smoking, cancer, substance abuse and suicide, to name a few, and they are also more likely to delay or avoid needed medical treatment. A contributing factor influencing these disparities can be perceived discrimination from health care providers or bias. Providers can exhibit both implicit (unconscious attitudes or beliefs) and explicit (conscious attitudes or beliefs) biases resulting in reduced quality of care and poor patient outcomes. Patients feel dismissed and undervalued, and therefore avoid taking an active part in their healthcare regimen. Poor communication and mistrust become a part of the patient/doctor relationship. Healthcare providers need to recognize implicit bias. Increased education in the form of learning sessions and classes provide information on how to recognize  bias while helping to prevent further occurrences within the LGBTQIA+ community. Learned strategies can be implemented to influence positive patient care and outcomes. In particular, implicit bias is recognized as a contributing factor to the health disparities faced by the LGBTQIA+ population and has been highlighted in professional competency objectives generated by the Association of American Medical Colleges Advisory Committee on Sexual Orientation, Gender Identity and Sex Development.  "Identified competencies include understanding how implicit LGBTQ-related bias may negatively impact interactions with patients, and developing strategies to mitigate implicit bias in health care settings"" (Morris et al., 2019). EHRs currently strive to capture data relating to a person's SO/GI to provide culturally competent, safe care, but the work continues. Patients at times, still express frustrations with EHR systems. Dunne et al, (2017), interviewed transgender patients regarding their health information in EHR systems. One example notes a patient being addressed by their assigned gender at birth name or biological sex name when their current gender was clearly listed in the EHR. Another patient talks about being called by their previous name, not the current name they are using, referring to it as their "deadname,"" the name used before their transition.  Another patient states, "I am more than just my trans identity"" (Dunne et al., 2017). Providers and healthcare teams need to be aware of how to collect SO/GI data and how to access and to update the information, if necessary, when providing patient care.

It is important for all healthcare personnel to take an active role towards recognizing their own biases as well as the importance of collecting gender identity information in EHR systems. Participating in LGBTQIA+ educational competency training will help to broaden knowledge and understanding of this population, altering attitudes and comfort levels of providers and patients. These practices can help to create an environment of safety and trust for the patient resulting in positive patient outcomes and improved patient satisfaction. 


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