Evaluating Emergency Department Resource Capacity Using Simulation
Bibliographic record
Abstract
Emergency departments open 24 hours every day and may receive patients at any time most unexpectedly. Adequate resource planning to meet the various demands in emergency departments is arguably amongst the most critical challenges faced by hospital administrators due to the complexity of the system and diverse patient flows. In this paper, we present a computer simulation model to evaluate resource utilization among personnel and physical resources in a typical emergency department (ED) of a government hospital in Malaysia. The model allows administrators to see patient movement flows as well as how these flows are affected by resource capacity level in the ED. The use of this simulation model helps to evaluate ED operations, provides useful insight for possible areas of improvement, and directs the allocation of specific resources for maximal impact. Three scenarios were tested to find out the impact of patient surge on ED performance measures. Results from the scenarios show that a 30% increase of attendances according to triage zones affects most of the performance measures. In addition, medical assistants are the critical resources with average utilization greater than 70%.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".