The Use of Queueing and Simulative Analyses to Improve an Overwhelmed Pharmacy Call Center
Bibliographic record
Abstract
Like many others, the St. Louis Veterans Administration Medical Center (VAMC) Pharmacy help desk receives far more calls than can be processed by current staffing levels. The objective of the study is to improve pharmaceutical services provided by the call center, by using queueing theory and discrete event dynamic simulation to analyze incoming telephone traffic to the help desk. Queueing and simulation models using both archival and hand-gathered data over a 1-year period were created, compared, and presented in order to determine the minimum quantities of staff needed to reach the desired service threshold. The simulation model was validated in comparison with real-world data. Results suggest that telephone traffic congestion in this setting may be alleviated by increasing the number of staff responsible for telephone services from 2 to 6 throughout the week, with an additional one serving on Monday. Both queueing and simulative models can be used to improve overwhelm pharmacy call centers, by determining the theoretical minimal staff needed to reach a service threshold.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".