Hospital capacity management based on the queueing theory
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The purpose of this paper is to focus on the contributions of queueing theory to hospital capacity management to improve organizational performance and deal with increased demand in the healthcare sector. Design/methodology/approach Models were applied to six months of inpatient records from a university hospital to determine operation measures such as utilization rate, waiting probability, estimated bed capacity, capacity simulations and demand behavior assessment. Findings Irrespective of the findings of the queueing model, the results showed that there is room for improvement in capacity management. Balancing admissions and the type of patient over the week represent a possible solution to optimize bed and nurse utilization. Patient mixing results in a highly sensitive delay rate due to length of stay (LOS) variability, with variations in both the utilization rate and the number of beds. Practical implications The outcomes suggest that operational managers should improve patient admission management, as well as reducing variability in LOS and in admissions during the week. Originality/value The queueing theory revealed a quantitative portrait of the day-by-day reality in a fast and flexible manner which is very convenient to the task of management.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it