Staffing and Scheduling Emergency Rooms in Two Public Hospitals: A Case Study
Bibliographic record
Abstract
Emergency Rooms (ER) in hospitals are considered as an integral part of the health care system. The number of patients arriving to the ER constitutes a significant percentage of the total patients who demand health services from a hospital. Therefore insuring the ER services around the hour is very crucial to maximize patients' care. In addition, the efficient allocation and utilization of nurses and physicians is one of the most important issues facing ER administrators. Although demand on ER services in hospitals at Baghdad increases dramatically at certain incidents, we observed that the ERs, where we conducted the study, are overstaffed with nurses and physicians around the day. However, it is, always, desirable to operate any emergency room with minimum staff, while maintaining the quality of patient care. This paper simulates the patients' arrivals to determine the adequate number of nurses and physicians, required, over 24 hours, at the ERs of two large public hospitals at the city of Baghdad. The simulation results were adjusted and used to determine the number of physicians and nurses in each ER for one week, 3-shift working day. The analysis conducted in this paper revealed that it is possible to downsize the current number of physicians by an average of 28%, and the number of nurses by about 55% while maintaining emergency services around the hour. The results could be translated into lower operating expenses of the ER, and better utilization of staff resources in other parts of the hospital.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".