Implementation of Gender Identity and Assigned Sex at Birth Data Collection in Electronic Health Records: Where Are We Now?

Abstract

In 2015, the United States Department of Health and Human Services instantiated rules mandating the inclusion of sexual orientation and gender identity (SO/GI) data fields for systems certified under Stage 3 of the Meaningful Use of Electronic Health Records (EHR) program. To date, no published assessments have benchmarked implementation penetration and data quality. To establish a benchmark for a U.S. health system collection of gender identity and sex assigned at birth, we analyzed one urban academic health center’s EHR data; specifically, the records of patients with unplanned hospital admissions during 2020 (N = 49,314). Approximately one-quarter of patient records included gender identity data, and one percent of them indicated a transgender or nonbinary (TGNB) status. Data quality checks suggested limited provider literacy around gender identity as well as limited provider and patient comfort levels with gender identity disclosures. Improvements are needed in both provider and patient literacy and comfort around gender identity in clinical settings. To include TGNB populations in informatics-based research, additional novel approaches, such as natural language processing, may be needed for more comprehensive and representative TGNB cohort discovery. Community and stakeholder engagement around gender identity data collection and health research will likely improve these implementation efforts.

Sex and Gender Equity in Prehospital Electrocardiogram Acquisition

“…However, the documentation platform in use during the study period (RescueNet; ZOLL Medical; Chelmsford, Massachusetts USA) recorded "gender" but offered only a choice between male and female for a required response, a common limitation of data systems. 24 In practice, front-line providers take in a variety of information and cues to complete this field, including how patients refer to and present themselves, how bystanders and family refer to the patient, observed patient characteristics, and how patient information is recorded in the provincial health care database. Acknowledging a lack of non-binary options as well as limited training in this area, 24,25 and in common with similar studies, 26 this paper will use the term gender (woman, man) to describe how patients are documented in the case of potential ACS calls.…”

“…24 In practice, front-line providers take in a variety of information and cues to complete this field, including how patients refer to and present themselves, how bystanders and family refer to the patient, observed patient characteristics, and how patient information is recorded in the provincial health care database. Acknowledging a lack of non-binary options as well as limited training in this area, 24,25 and in common with similar studies, 26 this paper will use the term gender (woman, man) to describe how patients are documented in the case of potential ACS calls. It will follow terms in print when referring to other studies.…”

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

“…If gender identity was recorded in medical records, it was included in the social history section or in progress notes but may or may not have been actively used in providing care. 22 2010s to present: A decade of sex confusion and sex obsession As Paisley Currah has articulated, binary sex identifiers have long been tied to distribution of rights and resources. 23 In particular, sex markers provided an external method of determining who was entitled to particular benefits and, prior to Obergefell v. Hodges, who could marry each other.…”

“…Previous literature about EHRs and transgender people advocate for the importance of collecting SOGI information and applaud its increased integration into new and existing systems. [12][13][14][15][16][17][18][19][20][21][22] However, most of these works also assume that sex is a coherent concept with a consistent meaning, even for transgender people. Work across many disciplines, including transgender studies, suggests otherwise and casts doubt upon the legitimacy of assumptions around what sex is, let alone how EHR systems record it.…”

Sex Trouble: Common pitfalls in incorporating sex/gender in medical machine learning and how to avoid them

“…3.2 Problems with Sex as Ground Truth: Sex Obsession, Sex Confusion, and Sex/Gender Slippage In recent years, there has been a laudable effort to improve the experience of LGBTQIA+ patients through the addition of gender-related fields to EHR systems (Lau et al, 2020;Keuroghlian, 2021;Thompson et al, 2021). While the introduction of gender identity and other fields in EHR such as pronouns and preferred names can help facilitate a more welcoming environment for trans, non-binary, and gender non-conforming people, the addition of these fields has been mostly limited to patient-facing care.…”