Evaluation of the Patients’ Queue Status at Emergency Department of Nemazee Hospital and How to Decrease It, 2014
Bibliographic record
Abstract
BACKGROUND: Patients, who seek care in emergency department, are waiting in queue and the health care provision in the department seems to be too overcrowded; the extended waiting time increases dissatisfaction and delays admission of new patients. In most of the hospitals considered to be overcrowded, the discharge rate of patients is managed by the use of “theory of queues”. This study was done to observe waiting time of patients in emergency department by “queue theory analysis” and computer simulator in an Iranian hospital. METHODS: This is a cross-sectional study in which simulation software (Arena, version 14) was used to build the 8 models. They run in a period of one month. The input information for the models was extracted from the hospital database and through sampling. The objective of this study was to evaluate the response variables of “waiting time” and “number waiting” of each level. RESULT: In level 2A, with increased number of beds with 20 beds, the waiting time decreased to 0.45 minutes and the percentage of deaths declined to 26.2%, but the number of discharge from this level declined, too. In level 3 with increased number of beds 2 times, waiting time decreased to 74 minutes and the percentage of death declined to 3.7% but the number of discharge from this level to ICU declined, too. CONCLUSION: This study showed the magnitude of ED overcrowding in Nemazee hospital. Increasing the bed capacity in the ED could reduce the waiting time in each part of ED.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".