Advancing Health Equity During the COVID-19 Pandemic through Digital Medical Interpretation Platforms
Bibliographic record
Abstract
Abstract Background: Medical Interpretation Services (MIS) is the gold-standard that should be used during clinical assessments with patients who have limited English proficiency (LEP) or have hearing loss. The COVID-19 pandemic has highlighted the urgent need for clear, concise medical communication between patients and clinicians to prevent the spread of COVID-19 and ensure public safety. Cost of MIS is covered by the provincial health authority in Alberta; however, it is not consistently utilized across the province.Aim: To implement digital MIS in the Emergency Department (ED) of one urban teaching hospital, improving accuracy of clinical assessment and to provide patient-centered communication. Methods: Applying quality improvement methodology, an intervention comprised of digital MIS technology and education was trialed for 6 months. To assess intervention effect, the number of MIS minutes and calls were measured monthly and a questionnaire was developed and administered to determine ED healthcare providers’ awareness, technology accessibility and perception of MIS integration into the clinical workflow. Results: Digital MIS was utilized consistently in the ED from the beginning of the COVID-19 pandemic (March 2020) and over the subsequent six months. The cost avoidance due to digital MIS usage was estimated to be $19,612.16. ED healthcare providers indicated that digital MIS helped smooth communication with patients and reduced the time it took to gather and provide accurate information. Conclusion: Providing digital MIS access, education and training is a means to advance health equity, by improving accuracy of clinical assessment and patient-centered care in the ED.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".