Measuring and Analyzing Waiting Time Indicators of Patients’ Admitted in Emergency Department: A Case Study
Bibliographic record
Abstract
BACKGROUND & AIMS: Measuring and analyzing of provided services times in Emergency Department is the way to improves quality of hospital services. The present study was conducted with aim measuring and analyzing patients waiting time indicators in Emergency Department in a general hospital in Iran. MATERIAL & METHODS: This cross-sectional, observational study was conducted during April to September 2012. The study population consisted of 72 patients admitted to the Emergency Department at Baharlo hospital. Data collection was carried out by workflow forms. Data were analyzed by t. test and ANOVA. RESULTS: The average waiting time for patients from admission to enter the triage 5 minutes, the average time from triage to physician visit 6 minute and the average time between examinations to leave ED was estimated 180 minutes. The total waiting time in the emergency department was estimated at about 210 minutes. The significant correlation between marital status of patients (P=0.03), way of arrive to ED (P=0.02) and type of shift work (P=0.01) with studied time indicators were observed. CONCLUSION: According to results and comparing with similar studies, the average waiting time of patients admitted to the studied hospital is appropriate. Factors such as: Utilizing clinical governance system and attendance of resident Emergency Medicine Specialist have performed an important role in reducing of waiting times in 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".