Meeting demand: A multi-method approach to optimizing hospital language interpreter staffing
Bibliographic record
Abstract
Objective: The objective of this paper is to highlight a study on optimizing the full-time equivalent (FTE) for Spanish and Mandarin interpreters at Christiana Care Health System. In this study, there were multiple challenges that needed to be addressed, and a multi-method approach was taken.Methods: These methods include: (1) time-motion study to quantify interpreter workflow and variability of duration of time needed for each task; (2) an integer program to optimize the number of interpreters needed per hour based on historical demand patterns for interpreter services; (3) Discrete-Event Simulation (DES) to examine the use of agency interpreters in order to meet demand; (4) cost modelling to convert FTEs and the use of agency interpreters into overall costs to the hospital; and (5) sensitivity analysis to evaluate alternative number of interpreter FTEs and their corresponding costs to the hospital.Results: Overall cost to the hospital is predicted to decrease with additional FTE interpreters, up to a threshold level above which the cost will start to increase. Through this innovative methodology used in this paper, we predict that hiring 3.5 more FTEs for Spanish interpreters will result in 9.07% of cost savings, and predict that hiring one FTE for Mandarin interpreters will result in 25.87% in cost savings compared to the current expense of providing Mandarin language interpretation.Conclusions: Contrary to intuition, increasing number of FTEs results in cost savings. Besides the financial benefit, hospitals will also be able to ensure the quality of health services that Limited English Proficiency (LEP) patients and families receive.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".