Relieving emergency department crowding: Simulating the effects of improving patient flow over time
Bibliographic record
Abstract
Background: Emergency Departments (ED) are challenged with excess demand for services and inadequate system capacity.Crowding at two independent EDs within a health system prompted an examination of the potential effects of improving patientthroughput. The objective of this study was to determine the effects of reducing ED dwell time on temporal patterns of patientflow and demand for ED resources.Methods: Separate discrete event simulation (DES) models were developed for the EDs of a 1,000-bed urban medical centerand a 560-bed community medical center using patient flow information. These models characterized the effects of reducingpatient dwell time on ED care area census (i.e., staffing needs), waiting room census, total length of stay (LOS) and waiting time. Dwell time was defined as the time interval from when a patient entered the main ED care area to when the patient exited the ED by discharge or hospital admission. Total LOS is defined as the entire time interval from ED from arrival to exit (includingwaiting time).Results: DES results for each site demonstrate how natural patient arrivals and common hospital admission processes generatecommon temporal patterns of decreased crowding. Improving flow translates to most substantial reductions in waiting timeand waiting room census during evening hours (17:00 to 22:00 hours). Significant effects on ED care area census and staffingdemands are lagged, not occurring until overnight hours (2:00 to 8:00 hours). We reduced patient dwell time in 5% incrementswithin the urban ED (16.2 min) and community ED (13.5 min) from 5% to 15%. For example, a 10% decrease in dwell timeat the urban ED (32.4 min) and community ED (27.0 min) resulted in respective decreases in evening waiting room census by49% (10.8 patients) and 26% (3.5 patients) during evening hours and ED care area census by 16% (3.6 patients) and 11% (2.0patients) overnight.Conclusions: DES results suggest that increasing ED efficiency will most significantly decrease delays experienced by eveningarrivals and provide opportunities to decrease care area census and reduce staff overnight.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".