Building a System for the Hospital’s Emergency Departments Based on the Queuing Theory
Bibliographic record
Abstract
Reducing the waiting time in the emergency departments is one of the most important factors that saving lives depends on, which means every minute we have is saving someone else life. In a traditional system, doctors and the other health care staffs waste about 15 to 25 min's on average to check the patient information which is considered too much time in such cases. The proposed system handle this problem by designing a queuing model, that takes the patient's initial information within no more than five minutes, and then classifies his health situation, using the Canadian's system which is determined health case using one of the different five colors (grey: Non-Urgency, green: Less Urgency, yellow: Urgency, red: Emergency, blue: Resuscitation case); Thus, the medical staff can deal with the cases in the proper order. Also, the proposed system records the patient's arrival time (AT), so that the ordering in the queue done correctly in case of more than one patient is classified in the same medical case. Eventually, the researchers implemented the system as a desktop application using Python programming language to design the main interfaces, and SQL to design the databases.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".