Exploring delay points at the emergency department
Bibliographic record
Abstract
Background The increasing time spent in the emergency department is becoming a global problem contributing to overcrowding. The increased length of stay in the emergency department can negatively affect patients' perception of care, contributes to high morbidity and mortality rates, and increased aggression towards staff. Therefore, understanding the delay points will help administrators and policy makers channel resources to the areas that require improvement. Methods This is a cross-sectional descriptive study to evaluate the delay points in the emergency department. The study was conducted at level IV community hospital in British Columbia. One hundred sixty-seven participants were recruited using a consecutive convenience sampling. Results The total sample size of this study was 167 and the age of the respondents ranged from 18-101 years. There were more females (50.9%) than males (47.9%) or queer individuals. The care point with the longest wait time was tests to physician reassessment (median time 65 minutes), followed by physician to Imaging (median time 52 minutes) and finally nurse to physician assessment (median time 45 minutes). Despite the prolonged length of stay in emergency department, most participants enjoyed the courtesy of staff (74.7%, good-very good) and 59.9% indicated that they would recommend this emergency department to others. Conclusion Tests and waiting for physician reassessments are important points in the patient journey in the emergency department that can prolong length of stay. Future studies are needed to determine whether various interventions such as point of care testing, utilizing the Lean Model and improving physician services can help reduce lengths of stay in the emergency department.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".