Using Simulation to Model Improvements in Pediatric Bed Placement in an Acute Care Hospital
Bibliographic record
Abstract
OBJECTIVE: The objective of this project was to use an interdisciplinary approach to analyze strategies through simulation technology for improving patient flow in a pediatric hospital. BACKGROUND: Various statistics have been offered on the number of children admitted annually to hospitals. For administrators, particularly in smaller systems, the financial burden of equipping and staffing pediatric units often outweighs the moral desire to maintain a pediatric unit as a viable option for patients and pediatricians. METHODS: Discrete event simulation was used to model current operations of a pediatric unit. Cost analysis was conducted using simulation reflecting various percentages of patients being referred to a discharge holding area (DHA) upon discharge and of the use of all private rooms. RESULTS: Both DHA and private rooms resulted in increased patient volumes. CONCLUSIONS: Administrators should consider the use of a DHA and/or private rooms to ease the census strains of pediatric units and the resultant revenue of this service.
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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 | 0.001 | 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 | 0.001 |
| 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 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".