Investigating Indicators of Waiting Time and Length of Stay in Emergency Departments
Bibliographic record
Abstract
PURPOSE: To investigate potential indicators of patients' waiting time and length of stay in emergency departments (ED) at the Ministry of Health (MOH) hospitals in order to determine the causes of delayed patient care and to recommend clinical implications to achieve a better ED system. MATERIALS AND METHODS: This exploratory study was conducted in the EDs at four tertiary hospitals of MOH. A random sample of 1360 people was tested from December 2019 to February 2020. Data included patient Canadian Triage Acuity and System (CTAS) level, registration time, triage time, physician examination time, decision time, and disposition time. Descriptive statistics, multivariate analysis, multiple linear regression analysis and Pearson correlation were used according to SPSS (version 24). RESULTS: The findings showed that 89.6% of total emergency patients were categorized as levels 3, 4 and 5. Around 73.5% of emergency patients stayed less than 4 hours due to registration or triage to disposition, while 26.5% of those patients stayed more than 4 hours. CONCLUSION: The majority of patients' total stay in EDs was less than 4 hours. According to ED international standard of length of stay, this is appropriate. The highest effective indicator in total length of stay was the decision to disposition time in EDs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".