Investigating Indicators of Waiting Time and Length of Stay in Emergency Departments
Bibliographic record
Abstract
Nojoud Al Nhdi,1,2 Hajar Al Asmari,1 Abdulellah Al Thobaity1 1Nursing Department, Applied Medical Sciences, Taif University, Taif, Saudi Arabia; 2Nursing Department, Health directorate in Jeddah, East Jeddah Hospital, Jeddah, Saudi ArabiaCorrespondence: Abdulellah Al ThobaityNursing Department, Applied Medical Sciences, Taif University, Taif, Saudi ArabiaEmail a.thobaity@tu.edu.saPurpose: 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.Keywords: emergency department, length of stay, indicators, waiting time, Canadian Triage and Acuity Scale
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".