Emergency Department Overcrowding Following Systematic Hospital Restructuring Trends at Twenty Hospitals over Ten Years
Bibliographic record
Abstract
OBJECTIVE: Hospital restructuring often results in fewer inpatient beds, increased ambulatory services, and closures of hospitals or emergency departments (EDs). The authors sought to determine the impact of systematic hospital restructuring on ED overcrowding. METHODS: Time series analyses of average monthly overcrowding for EDs in Toronto, Ontario, Canada, from 1991 and 2000 (n = 20 hospitals, 120 months) were conducted. Autoregression models evaluated the rate of increase of overcrowding before and during systematic restructuring. A secondary analysis included total ED visits, patient age, and sex distribution as covariates. Seasonality was assessed by means of spectral analysis. RESULTS: Severe and moderate overcrowding averaged 3% and 14% of the time each month, respectively, over the whole period. Before restructuring (n = 74 months), severe and moderate overcrowding averaged 0.5% and 9% per month, respectively; during restructuring (n = 46 months), the monthly averages were 6% and 23%, respectively. Neither severe nor moderate overcrowding was increasing before restructuring. During restructuring, however, both increased significantly (severe 0.2% per month [p < 0.0001]; moderate 0.5% per month [p < 0.0001]). Similar results were found after controlling for ED utilization. Female gender independently predicted increased overcrowding; older age predicted reduced moderate overcrowding; number of total visits was not a predictor. Spectral analysis revealed significant seasonality in overcrowding. CONCLUSIONS: Hospital restructuring was associated with increased ED overcrowding, even after controlling for utilization and patient demographics. Restructuring should proceed slowly to allow time for monitoring of its effects and modification of the process, because the impact of incremental reductions in hospital resources may be magnified as maximum operating capacity is approached.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".