A rapid review of gender, sex, and sexual orientation documentation in electronic health records
Bibliographic record
Abstract
OBJECTIVE: The lack of precise and inclusive gender, sex, and sexual orientation (GSSO) data in electronic health records (EHRs) is perpetuating inequities of sexual and gender minorities (SGM). We conducted a rapid review on how GSSO documentation in EHRs should be modernized to improve the health of SGM. MATERIALS AND METHODS: We searched MEDLINE from 2015 to 2020 with terms for gender, sex, sexual orientation, and electronic health/medical records. Only literature reviews, primary studies, and commentaries from peer-reviewed journals in English were included. Two researchers screened citations and reviewed articles with help from a third to reach consensus. Covidence, Excel, and Atlas-TI were used to track articles, extract data, and synthesize findings, respectively. RESULTS: Thirty-five articles were included. The 5 themes to modernize GSSO documentation in EHRs were (1) creating an inclusive, culturally competent environment with precise terminology and standardized data collection; (2) refining guidelines for identifying and matching SGM patients with their care needs; (3) improving patient-provider relationships by addressing patient rights and provider competencies; (4) recognizing techno-socio-organizational aspects when implementing GSSO in EHRs; and (5) addressing invisibility of SGM by expanding GSSO research. CONCLUSIONS: The literature on GSSO documentation in EHRs is expanding. While this trend is encouraging, there are still knowledge gaps and practical challenges to enabling meaningful changes, such as organizational commitments to ensure affirming environments, and coordinated efforts to address technical, organizational, and social aspects of modernizing GSSO documentation. The adoption of an inclusive EHR to meet SGM needs is a journey that will evolve over time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.037 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".