Smoothing inpatient discharges decreases emergency department congestion: a system dynamics simulation model
Bibliographic record
Abstract
BACKGROUND: Timely access to emergency patient care is an important quality and efficiency issue. Reduced discharges of inpatients at weekends are a reality to many hospitals and may reduce hospital efficiency and contribute to emergency department (ED) congestion. OBJECTIVE: To evaluate the daily number of ED beds occupied by inpatients after evenly distributing inpatient discharges over the course of the week using a computer simulation model. METHODS: Simulation modelling study from an academic care hospital in Toronto, Canada. Daily historical data from the general internal medicine (GIM) department between 15 January and 15 December for two years, 2005 and 2006, were used for model building and validation, respectively. RESULTS: There was good agreement between model simulations and historical data for both ED and ward censuses and their respective lengths of stay (LOS), with the greatest difference being +7.8% for GIM ward LOS (model: 9.3 days vs historical: 8.7 days). When discharges were smoothed across the 7 days, the number of ED beds occupied by GIM patients decreased by approximately 27-57% while ED LOS decreased 7-14 hours. The model also demonstrated that patients occupying hospital beds who no longer require acute care have a considerable impact on ED and ward beds. CONCLUSIONS: Smoothing out inpatient discharges over the course of a week had a positive effect on decreasing the number of ED beds occupied by inpatients. Despite the particular challenges associated with weekend discharges, simulation experiments suggest that discharges evenly spread across the week may significantly reduce bed requirements and ED LOS.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".