Evaluating the effect of emergency department crowding on triage destination
Bibliographic record
Abstract
BACKGROUND: Emergency Department (ED) crowding has been studied for the last 20 years, yet many questions remain about its impact on patient care. In this study, we aimed to determine if ED crowding influenced patient triage destination and intensity of investigation, as well as rates of unscheduled returns to the ED. We focused on patients presenting with chest pain or shortness of breath, triaged as high acuity, and who were subsequently discharged home. METHODS: This pilot study was a health records review of 500 patients presenting to two urban tertiary care EDs with chest pain or shortness of breath, triaged as high acuity and subsequently discharged home. Data extracted included triage time, date, treatment area, time to physician initial assessment, investigations ordered, disposition, and return ED visits within 14 days. We defined ED crowding as ED occupancy greater than 1.5. Data were analyzed using descriptive statistics and the χ(2) and Fisher exact tests. RESULTS: Over half of the patients, 260/500 (52.0%) presented during conditions of ED crowding. More patients were triaged to the non-monitored area of the ED during ED crowding (65/260 (25.0%) vs. 39/240 (16.3%) when not crowded, P = 0.02). During ED crowding, mean time to physician initial assessment was 132.0 minutes in the non-monitored area vs. 99.1 minutes in the monitored area, P <0.0001. When the ED was not crowded, mean time to physician initial assessment was 122.3 minutes in the non-monitored area vs. 67 minutes in the monitored area, P = 0.0003. Patients did not return to the ED more often when triaged during ED crowding: 24/260 (9.3%) vs. 29/240 (12.1%) when ED was not crowded (P = 0.31). Overall, when triaged to the non-monitored area of the ED, 44/396 (11.1%) patients returned, whereas in the monitored area 9/104 (8.7%) patients returned, P = 0.46. CONCLUSIONS: ED crowding conditions appeared to influence triage destination in our ED leading to longer wait times for high acuity patients. This did not appear to lead to higher rates of return ED visits amongst discharged patients in this cohort. Further research is needed to determine whether these delays lead to adverse patient outcomes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".