Interpretation Services in a Canadian Emergency Department: How Often Are They Utilized for Patients With Limited English Proficiency?
Bibliographic record
Abstract
Introduction Patients with limited English proficiency (LEP) face barriers to communication leading to inferior health outcomes when compared with English-proficient patients. Professional interpretation services have been shown to improve healthcare outcomes for patients with LEP but are often underutilized. Methods We conducted a retrospective chart review of all patients who visited the Kingston Health Sciences Centre's ED and urgent care centre between July 2015 and August 2021 and identified as having a non-English preferred language. The demographic and visit information of LEP patients who used LanguageLine (Monterey, CA) were compared to LEP patients who did not use the service. Variables were analysed using t-tests and chi-squared tests. A survey distributed to ED physicians and residents collected perspectives on the facilitators/barriers to LanguageLine use. Results Of the 37,500 visits from LEP patients between 2015 and 2021, 118 (0.31%) used LanguageLine. LEP patients were more likely to access LanguageLine if they were younger (p < 0.001), had a more acute Canadian Triage Acuity Scale (CTAS) score (p < 0.001), and spoke Arabic (p<0.001). All 16 staff/residents who responded to the survey (30% response rate) had at least one LEP patient in the preceding month, and 3/16 (19%) accessed LanguageLine for these patients. Further, 5/16 (31%) reported never using the service, with 4/5 (80%) unaware the service existed. Among those aware of LanguageLine, 7/12 (58%) reported the availability of an ad-hoc interpreter as a reason for not accessing the service. Conclusion Interpretation services are underutilized for LEP patients in the ED, with less than 1% of these patients accessing LanguageLine. Patients were more likely to access LanguageLine if they were younger, spoke Arabic, and had a more acute triage score. Most ED physicians were either unaware of or not accessing LanguageLine despite seeing LEP patients. Future work should aim to improve the use of language services and patient-centred care for LEP patients in the ED.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".