Length of stay in the emergency department and its associated input-, throughput-, and output factors at two hospitals in Sweden
Bibliographic record
Abstract
Abstract Background Prolonged emergency department length of stay (EDLOS) is a worldwide issue associated with increased mortality, decreased patient satisfaction and poor quality of care. The factors influencing EDLOS have not been comprehensively studied in the context of Swedish EDs. This study’s objective is to determine the input-, throughput- and output factors associated with EDLOS, at two urban EDs in Sweden. Methods Data was collected from two hospitals. All patient visits during the two-year study period were included. Patients who left without being seen by a physician were excluded. The explanatory factors included patient characteristics, medical data, and hospital bed occupancy data. Multi-variable linear regression analysis was used to test the associations between the factors and EDLOS. Results The top contributors to prolonged EDLOS were diagnostic imaging, which added between 64 and 149 min of EDLOS, diagnostic testing at central laboratory (53–99 min), followed by intra-ED zone transfer (46–94 min). Arriving during crowding or being admitted during high hospital bed occupancy had a significant but relatively small absolute effect on the outcome. Conclusions Throughput factors had far greater impact on EDLOS than both input- and output factors. Adapting strategies to the structural and procedural characteristics of each setting may enhance the effectiveness of improvement efforts. Clinical trial number Not applicable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".