Providing Inclusive Care for Transgender Patients: Capturing Sex and Gender in the Electronic Medical Record
Bibliographic record
Abstract
BACKGROUND: Providing a positive patient experience for transgender individuals includes making the best care decisions and providing an inclusive care environment in which individuals are welcomed and respected. Over the past decades, introduction of electronic medical record (EMR) systems into healthcare has improved quality of care and patient outcomes through improved communications among care providers and patients and reduced medical errors. Promoting the highest standards of care for the transgender populations requires collecting and documenting detailed information about patient identity, including sex and gender information in both the EMR and laboratory information system (LIS). CONTENT: As EMR systems are beginning to incorporate sex and gender information to accommodate transgender and gender nonconforming patients, it is important for clinical laboratories to understand the importance and complexity of this endeavor. In this review, we highlight the current progress and gaps in EMR/LIS to capture relevant sex and gender information. SUMMARY: Many EMR and LIS systems have the capability to capture sexual orientation and gender identity (SOGI). Fully integrating SOGI into medical records can be challenging, but is very much needed to provide inclusive care for transgender individuals.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".