34 Improving language concordant care in the paediatric emergency department
Bibliographic record
Abstract
Abstract Background Paediatric patients and families with limited English proficiency (LEP) experience worse health outcomes. Use of professional medical interpreter services (IS) helps ensure that all patients receive high quality care, regardless of their preferred language of communication. Objectives The aim of this quality improvement (QI) study was to improve language concordant care within a paediatric emergency department (ED) in downtown Toronto by increasing the rate of over-the-phone interpreter use by 50% by July 2024 and to understand common reasons for interpreter non-use. Design/Methods Using the model for improvement, we assembled an interdisciplinary team that included physicians, ED registration clerks, triage nurses, and language services staff to understand current workflow and develop targeted interventions aimed at addressing key drivers through multiple plan-do-study-act (PDSA) cycles. Interventions included introduction of a standardized script to capture language preference at ED registration, EMR-integrated preferred language notification and smart text template in provider notes for standardized IS use or non-use documentation, and departmental education. Our primary outcome measure was rate of over-the-phone IS use for all ED visits. Process measures were rate of EMR documentation of patients and caregivers with non-English language preference (NELP) and rate of provider documentation of IS use or non-use. Balancing measures included time to provider initial assessment (PIA) and ED length of stay. Results The baseline period (November 2022-April 2023) revealed an over-the-phone IS rate of 5.2% for all ED visits. The initial results from the first 6 months of the intervention period demonstrated an increase in physician documentation of IS use or non-use in patients with NELP, from 10% to 76%. Additionally, 18.6% of patients or caregivers with NELP had IS use per provider notes. Over the next 9 months of the intervention period, special cause variation is anticipated in the rate of IS use and rate of EMR documentation of NELP. The most common reasons for IS non-use were: ‘deemed unnecessary by provider’, ‘declined by family’ and ‘use of family member/friend as interpreter’. There was no change in PIA or ED length of stay for patients with NELP. Conclusion This QI initiative increased consistent provider documentation of IS use in ED patient records in one of the most linguistically diverse cities in the world. Future efforts will focus on exploring additional ways to capture language preference beyond registration, facilitating IS use in the ED, and addressing top reasons for non-use of IS in patients and caregivers with NELP.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".