Advancing language concordant care: a multimodal medical interpretation intervention
Bibliographic record
Abstract
BACKGROUND: Ensuring language concordant care through medical interpretation services (MIS) allows for accurate information sharing and positive healthcare experiences. The COVID-19 pandemic led to a regional halt of in-person interpreters, leaving only digital MIS options, such as phone and video. Due to longstanding institutional practices, and lack of accessibility and awareness of these options, digital MIS remained underused. A Multimodal Medical Interpretation Intervention (MMII) was developed and piloted to increase digital MIS usage by 25% over an 18-month intervention period for patients with limited English proficiency. METHODS: Applying quality improvement methodology, an intervention comprised digital MIS technology and education was trialled for 18 months. To assess intervention impact, the number of digital MIS minutes was measured monthly and compared before and after implementation. A questionnaire was developed and administered to determine healthcare providers' awareness, technology accessibility and perception of MIS integration in the clinical workflow. RESULTS: Digital MIS was used consistently from the beginning of the COVID-19 pandemic (March 2020) and over the subsequent 18 months. The total number of minutes of MIS use per month increased by 44% following implementation of our intervention. Healthcare providers indicated that digital MIS was vital in facilitating transparent communication with patients, and the MMII ensured awareness of and accessibility to the various MIS modalities. CONCLUSION: Implementation of the MMII allowed for an increase in digital MIS use in a hospital setting. Providing digital MIS access, education and training is a means to advance patient-centred and equitable care by improving accuracy of clinical assessments and communication.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".