The impact of delays to admission from the emergency department on inpatient outcomes
Bibliographic record
Abstract
BACKGROUND: We sought to determine the impact of delays to admission from the Emergency Department (ED) on inpatient length of stay (LOS), and IP cost. METHODS: We conducted a retrospective analysis of 13,460 adult (>or= 18 yrs) ED visits between April 1 2006 and March 30 2007 at a tertiary care teaching hospital with two ED sites in which the mode of disposition was admission to ICU, surgery or inpatient wards. We defined ED Admission Delay as ED time to decision to admit > 12 hours. The primary outcomes were IP LOS, and total IP cost. RESULTS: Approximately 11.6% (n = 1558) of admitted patients experienced admission delay. In multivariate analysis we found that admission delay was associated with 12.4% longer IP LOS (95% CI 6.6% - 18.5%) and 11.0% greater total IP cost (6.0% - 16.4%). We estimated the cumulative impact of delay on all delayed patients as an additional 2,183 inpatient days and an increase in IP cost of $2,109,173 at the study institution. CONCLUSIONS: Delays to admission from the ED are associated with increased IP LOS and IP cost. Improving patient flow through the ED may reduce hospital costs and improve quality of care. There may be a business case for investments to reduce emergency department admission delays.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".