Abstract
Purpose:
Electronic health records (EHR) offer a unique opportunity to systematically collect sexual orientation and gender identity (SOGI) data. This study examined the prevalence and determinants of SOGI reporting in an EHR-based cohort of transgender and gender diverse (TGD) individuals.
Methods:
We identified TGD people with and without SOGI documentation across four Kaiser Permanente health plans from January 1, 2022-2024. TGD status was determined through clinical notes, diagnostic codes, and SOGI data based on a previously established cohort. Factors associated with SOGI reporting were assessed using log-binomial regression, yielding prevalence ratios (PR) and the 95% confidence intervals (CI).
Results:
Among 23,060 TGD individuals, 71% had SOGI documentation in the EHR. Reporting varied by sociodemographic and clinical characteristics. For example, compared to those <20 years, SOGI reporting was higher for those aged 21-59 (PRs 1.10-1.21; 95% CIs 1.06-1.24) and lower for those >60 (0.93; 0.88-0.99). Documentation was slightly lower for those assigned male at birth (0.98; 0.97-1.00) and varied by race and ethnicity (e.g., Hispanic: 0.97; 0.95-0.99; Other: 1.02; 0.98-1.05 vs. White).
Conclusions:
KP’s EHRs captured SOGI data for over 70% of TGD individuals, though more research is needed to understand factors associated with missing data not captured in structured fields.
Keywords: transgender, sexual orientation, gender identity, electronic health records
Introduction
Transgender and gender diverse (TGD) individuals experience health disparities driven by stigma and barriers to care, including intersecting experiences of gender, race and socioeconomic status.1,2 Studies have found that TGD persons are at greater risk for mental health issues,3 cardiovascular disease,4 5 and certain cancers;6 yet are less likely to receive preventive services.7 Despite documented disparities, high-quality data to address TGD-specific care needs remain limited. 8
Barriers to high-quality TGD care and health research include limited systematic collection of population-level sexual orientation and gender identity (SOGI) data. Electronic health records (EHRs) offer a unique opportunity to facilitate SOGI documentation and support clinical decisions – such as screenings, dosage calculations, and lab interpretations – often depend on sex assigned at birth. Without SOGI data in the EHR, providers risk misgendering9 or using dead names10, potentially causing distress for TGD patients when seeking care. Additionally, lack of SOGI reporting perpetuates underrepresentation of TGD persons in health research11, which has negative implications on quality improvement, research efforts, and policy activities.
Federal agencies and national organizations have recommended routine SOGI data collection in EHRs. 12,13 Despite this, studies report high levels of missing data, 14-16 and few have examined factors influencing SOGI documentation among TGD persons.17,18
Kaiser Permanente (KP) serves a diverse patient population and has been a national leader in LGBTQ+ healthcare since 2013, particularly for its inclusive and equitable policies and practices.19,20 In 2019, KP began integrating a SOGI module into its Epic™ EHR system. This study assessed SOGI documentation completeness and factors associated with SOGI reporting among TGD persons enrolled in KP health plans.
Methods
Study design and Participants
We used data from the Study of Transition, Outcomes and Gender 2 (STRONG2) to conduct a cross-sectional examination of frequency and determinants of SOGI reporting among TGD members at KP sites in Georgia (KPGA), Northern California (KPNC), Southern California (KPSC), and Mid-Atlantic States (KPMAS). STRONG2 aimed to assess the health of TGD persons, particularly after gender-affirming treatment. Cohort assembly methods are described elsewhere.21,22 Briefly, the three-step algorithm identified TGD individuals via EHR search (step 1), validation (step 2), and classification of transmasculine or transfeminine status based on sex assigned at birth (step 3). Inclusion required at least two of three indicators: validated clinical notes, relevant diagnostic codes, or SOGI data. Step 3 relied on diagnosis codes, clinical notes, and pharmacy records to classify transmasculine (sex assigned female at birth) and transfeminine (sex assigned male at birth) individuals, including non-binary identities. For this study, transmasculine and transfeminine were used as umbrella terms to describe individuals assigned female or male at birth, respectively, including nonbinary individuals. This approach enabled consistent categorization across the cohort, particularly for individuals without self-reported gender identity via SOGI documentation and for those whose nonbinary identities are not accurately represented by binary terms such as transman or transwoman. Each validated TGD member was matched with up to 10 males and 10 females without TGD evidence by birth year, race and ethnicity, site, and enrollment year. The STRONG2 cohort includes 29,914 TGD individuals matched to 598,280 non-TGD referents.
To assess recent SOGI reporting, we analyzed a subset of the STRONG2 cohort with confirmed TGD and transmasculine/transfeminine status enrolled at four KP sites between January 1, 2022, and January 1, 2024. The study was conducted in partnership with the STRONG2 coordinating center at Emory University and approved by the Institutional Review Boards at participating institutions with a waiver of informed consent.
Measures
The primary outcome was binary: presence of SOGI documentation in the EHR. The Epic™ SOGI module captures pronouns, gender identity, sex assigned at birth, sexual orientation, partner genders, and organ inventory. KP clinicians collect this data during routine encounters by asking about sex assigned at birth (“what sex were you assigned at birth?”), gender identity (“how would you describe your gender identity?”), and pronouns (“what are your pronouns?”).23 STRONG2 TGD members were considered to have SOGI-confirmed status if they self-identified as transgender or genderqueer, or if sex assigned at birth conflicted with reported gender identity. We also examined demographic and clinical variables from EHR data. Demographics included age (≤20, 20-39, 40-49, 50-59, and ≥60 years), sex assigned at birth (female, male), race and ethnicity (non-Hispanic Asian, non-Hispanic White, non-Hispanic Black, Hispanic, non-Hispanic Other, and Unknown), site (KPGA, KPMAS, KPNC, and KPSC), Medicaid status (any vs. none), and residence in a poverty area (defined as a neighborhood with >20% households with incomes below the federal poverty cut-off).24 Clinical variables included comorbidities (Charlson index: 0, 1, ≥2)25 and healthcare utilization, measured by average annual encounters (<10, 10-29, and ≥30 encounters), including virtual, in-person, phone, inpatient, outpatient, and ED visits.
Statistical Methods
Data were analyzed using SPSS Version 29.0 (IBM Corp.). Frequency and distributions of SOGI documentation were calculated for each independent variable. Log-binomial regression was used to estimate crude and adjusted prevalence ratios (PR) with 95% confidence intervals (CI); adjusted models included all independent variables of interest.
Results
As shown in Table 1, a total of 23,060 TGD persons enrolled in KP healthcare systems were included in this analysis, with 71% (n=16,312) having SOGI documentation in the EHRs. Most cohort members were transmasculine (assigned female at birth), comprising 62% of those with SOGI data. Among TGD individuals with SOGI data, 6,529 (40%) identified as trans men, 5422 (33%) as trans women, and 4,361 (27%) as non-binary. Compared with TGD persons without SOGI documentation in the EHR, those with SOGI data were more likely to be assigned female sex at birth (62% vs. 59%), aged 20-39 years old (64% vs. 53%), and non-Hispanic White (54% vs. 48%). Additionally, TGD members with SOGI data were more likely to be members of KPNC (61% vs. 39%), not covered by Medicaid insurance (75% vs. 73%), and have >10 annual healthcare encounters (69% vs. 62%) when compared with those without SOGI data. The proportion of TGD members with and without SOGI documentation who resided in a poverty area were similar (91% vs 90%). TGD members without SOGI reporting were more likely to have a comorbidity index score >2 when compared with those with SOGI documentation (7% vs. 5%).
Table 1.
Descriptive characteristics of study participants with and without SOGI documentation in the EHR*
| Participant characteristics | Has SOGI documented in the EHR | |||
|---|---|---|---|---|
| Yes | No | |||
| n=16,312 | % | n=6,748 | % | |
| Sex assigned at birth | ||||
| Female | 10130 | 62.1 | 3969 | 58.8 |
| Male | 6182 | 37.9 | 2779 | 41.2 |
| Gender identity | ||||
| Trans man | 6529 | 40.0 | - | - |
| Trans woman | 5422 | 33.2 | ||
| Non-binary | 4361 | 26.7 | - | - |
| Current age (years)** | ||||
| <20 | 2742 | 16.8 | 1659 | 24.6 |
| 20-39 | 10514 | 64.5 | 3562 | 52.8 |
| 40-49 | 1534 | 9.4 | 583 | 8.6 |
| 50-59 | 817 | 5.0 | 402 | 6.0 |
| ≥60 | 705 | 4.3 | 542 | 8.0 |
| Race and ethnicity | ||||
| Asian, non-Hispanic | 1452 | 8.9 | 564 | 8.4 |
| Black, non-Hispanic | 1132 | 6.9 | 462 | 6.8 |
| Hispanic | 3561 | 21.8 | 1951 | 28.9 |
| Other***, non-Hispanic | 589 | 3.6 | 194 | 2.9 |
| Unknown | 774 | 4.7 | 335 | 5.0 |
| White, non-Hispanic | 8804 | 54.0 | 3242 | 48.0 |
| Study site | ||||
| Georgia | 299 | 1.8 | 216 | 3.2 |
| Mid-Atlantic States | 1043 | 6.4 | 269 | 4.0 |
| Northern California | 9961 | 61.1 | 2616 | 38.8 |
| Southern California | 5015 | 30.7 | 3650 | 54.1 |
| Medicaid coverage | ||||
| No | 12153 | 74.5 | 4925 | 73.0 |
| Yes | 4159 | 25.5 | 1823 | 27.0 |
| Care utilization (average number of encounters/year)† | ||||
| <10 | 5131 | 31.5 | 2505 | 37.1 |
| 10-29.9 | 6181 | 37.9 | 2499 | 37.0 |
| ≥30.00 | 5000 | 30.7 | 1744 | 25.8 |
| Residence in poverty area§ | ||||
| No | 1556 | 9.5 | 734 | 10.9 |
| Yes | 14756 | 90.5 | 6014 | 89.1 |
| Charlson comorbidity index | ||||
| 0 | 12951 | 79.4 | 5359 | 79.4 |
| 1 | 2554 | 15.7 | 945 | 14.0 |
| ≥2 | 807 | 4.9 | 444 | 6.6 |
Includes persons enrolled after 2022
As of 01/01/2024
Includes Native Hawaiians/Pacific Islanders, American Indians/Alaskan Natives, non-Hispanic persons of multiple race categories with particular category unknown, and non-Hispanic persons identified as Other with values that do not fit any other value
Defined as average annual number of all encounters between 2022 and 2024
Defined as >20% of neighborhood households with income below federal poverty cutoff
In unadjusted models, the prevalence of SOGI documentation varied by age, sex assigned at birth, race and ethnicity, study site, Medicaid coverage, care utilization, residence in a poverty area, and clinical comorbidities. For example, as shown in Table 2, TGD persons whose ethnicity was Hispanic had a lower prevalence of SOGI documentation (65%) in the EHR when compared with non-Hispanic White cohort members (73%). Additionally, TGD persons residing in a poverty area had a slightly higher (71%) prevalence of SOGI documentation in the EHR when compared with those who lived in more affluent neighborhoods (68%). In the multivariable model, most associations observed in the univariate analysis, apart from residence in a poverty area, were similar (Table 2). For example, compared with the youngest (<20 years) age group, prevalence of documented SOGI status was higher in adults 21-39, 40-49 and 50-59 years of age with PR (95% CI) estimates of 1.21 (1.18-1.24), 1.17 (1.13-1.21) and 1.10 (1.06-1.15), respectively, but lower in the oldest cohort members (PR=0.93; 95% CI: 0.88-0.99). Additionally, using the KPNC site as a reference, the adjusted PR was 0.72 (95% CI: 0.67, 0.77) for KPGA, 0.98 (95% CI: 0.96, 1.01) for KPMAS, and 0.73 (95% CI: 0.71, 0.74) for KPSC. The corresponding PRs (95% CIs) for TGD persons with 10-29.9 and >30 annual visits were 1.08 (1.06, 1.10) and 1.16 (1.14, 1.19), respectively, when compared with TGD persons with <10 annual visits. Other factors associated with a lower prevalence of SOGI reporting were Hispanic ethnicity, male sex assigned at birth, Medicaid coverage, and a Charlson index score ≥2.
Table 2.
Factors associated with SOGI documentation in the EHR among TGD patients*
| Participant characteristics | % SOGI reporting |
Unadjusted
models |
Adjusted model | ||||
|---|---|---|---|---|---|---|---|
| PR | 95% CI | PR | 95% CI | ||||
| Current age (years)** | |||||||
| <20 | 62.3 | 1.00 | Reference | 1.00 | Reference | ||
| 20-39 | 74.7 | 1.20 | 1.17 | 1.23 | 1.21 | 1.18 | 1.24 |
| 40-49 | 72.5 | 1.16 | 1.12 | 1.20 | 1.17 | 1.13 | 1.21 |
| 50-59 | 67.0 | 0.91 | 0.86 | 0.96 | 1.10 | 1.06 | 1.15 |
| ≥60 | 56.5 | 0.62 | 0.61 | 0.64 | 0.93 | 0.88 | 0.99 |
| Sex assigned at birth | |||||||
| Female | 71.8 | 1.00 | Reference | 1.00 | Reference | ||
| Male | 69.0 | 0.96 | 0.94 | 0.98 | 0.98 | 0.97 | 1.00 |
| Race and ethnicity | |||||||
| Asian, non-Hispanic | 72.0 | 0.99 | 0.96 | 1.01 | 0.98 | 0.96 | 1.01 |
| Black, non-Hispanic | 71.0 | 0.97 | 0.94 | 1.00 | 0.98 | 0.95 | 1.01 |
| Hispanic | 64.6 | 0.88 | 0.86 | 0.90 | 0.97 | 0.95 | 0.99 |
| Other, non-Hispanic*** | 75.2 | 1.03 | 0.99 | 1.07 | 1.02 | 0.98 | 1.05 |
| Unknown | 69.8 | 0.95 | 0.92 | 0.99 | 1.00 | 0.97 | 1.04 |
| White, non-Hispanic | 73.1 | 1.00 | Reference | 1.00 | Reference | ||
| Study site | |||||||
| Georgia | 58.1 | 0.73 | 0.68 | 0.79 | 0.72 | 0.67 | 0.77 |
| Mid-Atlantic States | 79.5 | 1.00 | 0.97 | 1.03 | 0.98 | 0.96 | 1.01 |
| Northern California | 79.2 | 1.00 | Reference | 1.00 | Reference | ||
| Southern California | 57.9 | 0.73 | 0.72 | 0.75 | 0.73 | 0.71 | 0.74 |
| Medicaid coverage | |||||||
| No | 71.2 | 1.00 | Reference | 1.00 | Reference | ||
| Yes | 69.5 | 0.98 | 0.96 | 1.00 | 0.98 | 0.96 | 1.00 |
| Care utilization (average number of encounters/year)† | |||||||
| <10 | 67.2 | 1.00 | Reference | 1.00 | Reference | ||
| 10-29.9 | 71.2 | 1.06 | 1.04 | 1.08 | 1.08 | 1.06 | 1.10 |
| ≥30.00 | 74.1 | 1.10 | 1.08 | 1.13 | 1.16 | 1.14 | 1.19 |
| Residence in poverty area§ | |||||||
| No | 67.9 | 1.00 | Reference | 1.00 | Reference | ||
| Yes | 71.0 | 1.05 | 1.02 | 1.08 | 1.00 | 0.97 | 1.03 |
| Charlson comorbidity index | |||||||
| 0 | 70.7 | 1.00 | Reference | 1.00 | Reference | ||
| 1 | 73.0 | 1.03 | 1.01 | 1.06 | 1.00 | 0.98 | 1.02 |
| ≥2 | 64.5 | 0.91 | 0.87 | 0.95 | 0.95 | 0.92 | 0.99 |
Includes persons enrolled after 2022
As of 01/01/2024
Includes Native Hawaiians/Pacific Islanders, American Indians/Alaskan Natives, non-Hispanic persons of multiple race categories with particular category unknown, and non-Hispanic persons identified as Other with values that do not fit any other value
Defined as average annual number of all encounters between 2022 and 2024
Defined as >20% of neighborhood households with income below federal poverty cutoff
Abbreviations: PR = prevalence ratios, CI = confidence interval, SOGI = sexual orientation and gender identity, TGD = transgender and gender diverse
Note: All estimates in the adjusted model were derived from a single logistic regression model that includes all covariates listed in the table.
Discussion
This study provides new insights into SOGI reporting in the EHRs of a large TGD cohort within an integrated health system. Over 70% of TGD individuals had SOGI documentation in the EHR – exceeding national averages – and several sociodemographic and clinical factors were associated with SOGI documentation.
Over 70% of TGD cohort members had confirmatory SOGI data in their EHRs, exceeding prior estimates. For example, a study of over a thousand US health centers found that only 23-37% of patients had SOGI documentation.18 Difficulties overcoming systematic and social barriers, including data field integration and cultural competence, have been identified as key barriers to SOGI documentation.26 In contrast, a Veterans Affairs (VA) study reported that nearly 70% of TGD veterans had self-reported gender identity documentation in the EHR,27 suggesting integrated care systems may offer increased opportunities for SOGI collection. Higher rates in our study may also reflect improved practices over time, as earlier studies (e.g., Grasso et al., 2019) reported lower rates. Comparison should be interpreted cautiously due to differences in cohort identification. For example, our use of three data sources – including SOGI data – may yield higher documentation rates than studies using fewer sources or including individuals at different stages of gender identity. However, excluding SOGI data reduced documentation only slightly (71% to 69%), indicating significant overlap across sources.
We identified several demographic and clinical factors associated with SOGI reporting. Most associations from univariate analyses remained unchanged after adjustment, except for residence in a poverty area. Some findings align with prior studies, while others, to our knowledge, have not been previously reported. Our finding that SOGI documentation differs across age groups, sex assigned at birth, health center location, and race and ethnicity is consistent with previous reports.15,17,28 Additionally, at least one previous study examined differences in SOGI reporting across insurance types and found no statistically significant difference,28 though the population of interest was not limited to TGD individuals. To our knowledge, differences in SOGI reporting for TGD patients by care utilization and Charlson index score have not been previously reported and may require further exploration. For example, patients with a greater number of care visits may provide more opportunities for care providers to fill the SOGI text field in the EHR. Additionally, patients with comorbid conditions may represent more complex cases, leading care teams to manage competing health priorities and less time to complete the SOGI field.
The associations observed in the present study should be interpreted with consideration of potential limitations. First, our finding of 71% SOGI reporting reflects individuals receiving care in integrated healthcare systems with comprehensive EHRs, which may not represent all TGD persons. Moreover, inclusion was limited to individuals with EHR indicators of TGD status, potentially excluding those earlier in their transition or without diagnostic codes, clinical notes, or SOGI documentation. For the ~30% of individuals without SOGI data, gender identity could not be directly confirmed, and classification relied on sex assigned at birth, which may not fully reflect individuals’ lived identities – limiting generalizability. Second, the cross-sectional design limits causal inferences and is affected by temporal ambiguity. To better understand changes in SOGI documentation patterns over time, longitudinal studies with date-specific SOGI fields are necessary. Third, we acknowledge that using a binary classification of gender identity simplifies what is often a fluid and context-dependent experience of gender. Nevertheless, this approach has scientific merit, as it enables us to conduct rigorous analysis and draw meaningful conclusions that can inform efforts to promote the wellbeing and representation of TGD individuals in health research. Fourth, while we evaluated several sociodemographic and clinical predictors of SOGI documentation, there may be other important factors not considered (e.g., provider characteristics, clinical context and cultural and societal attitudes), which likely cannot be easily ascertained from structured data. Additionally, patients’ fear of disclosure or provider discomfort inquiring about SOGI was not considered because such information would require different methods of data collection. These types of patient- and provider-reported data warrant future research, perhaps with the use of mixed-methods studies to gain a comprehensive understanding of factors influencing SOGI reporting.
Conclusion
Our findings suggest that KP’s EHRs effectively captured SOGI data for over 70% of TGD persons. However, reporting varied by member characteristics, including age, sex assigned at birth, race and ethnicity, care region, insurance type, care utilization, and clinical comorbidities. To address differences in recording practices, future research should explore factors associated with missing SOGI data that may not be captured in structured fields – including provider characteristics, clinical context, and broader cultural or societal influences. Advancing this understanding will help health systems improve TGD individuals’ representation in health research and enhance their quality of care.
Highlights.
71% of 23,060 TGD persons had SOGI data in the EHRs, exceeding national averages.
SOGI documentation varied by sociodemographic and clinical factors.
Funding statement:
This work was supported by the National Institute on Aging of the National Institutes of Health under award number R01AG066956. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
List of Abbreviations and Acronyms
- TGD
transgender and gender diverse
- SOGI
sexual orientation and gender identity
- EHR
electronic health record
- KP
Kaiser Permanente
- STRONG2
Study of Transition, Outcomes and Gender 2
- KPGA
Kaiser Permanente Georgia
- KPNC
Kaiser Permanente Northern California
- KPSC
Kaiser Permanente Southern California
- KPMAS
Kaiser Permanente Mid-Atlantic States
- PR
Prevalence ratios
- CI
Confidence intervals
- VA
Veterans Affairs
Footnotes
Competing interest statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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