Crowding Measures Associated With the Quality of Emergency Department Care: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Despite the substantial body of literature on emergency department (ED) crowding, to the best of our knowledge, there is no agreement on the measure or measures that should be used to quantify crowding. The objective of this systematic review was to identify existing measures of ED crowding that have been linked to quality of care as defined by the Institute of Medicine (IOM) quality domains (safe, effective, patient-centered, efficient, timely, and equitable). METHODS: Six major bibliographic databases were searched from January 1980 to January 2012, and hand searches were conducted of relevant journals and conference proceedings. Observational studies (cross-sectional, cohort, and case-control), quality improvement studies, quasi-experimental (e.g., before/after) studies, and randomized controlled trials were considered for inclusion. Studies that did not provide measures of ED crowding were excluded. Studies that did not provide quantitative data on the link between crowding measures and quality of care were also excluded. Two independent reviewers assessed study eligibility, completed data extraction, and assessed study quality using the Newcastle-Ottawa Quality Assessment Scale (NOS) for observational studies and a modified version of the NOS for cross-sectional studies. RESULTS: The search identified 7,413 articles. Thirty-two articles were included in the review: six cross-sectional, one case-control, 23 cohort, and two retrospective reviews of performance improvement data. Methodologic quality was moderate, with weaknesses in the reporting of study design and methodology. Overall, 15 of the crowding measures studied had quantifiable links to quality of care. The three measures most frequently linked to quality of care were the number of patients in the waiting room, ED occupancy (percentage of overall ED beds filled), and the number of admitted patients in the ED awaiting inpatient beds. None of the articles provided data on the link between crowding measures and the IOM domains reflecting equitable and efficient care. CONCLUSIONS: The results of this review provide data on the association between ED crowding measures and quality of care. Three simple crowding measures have been linked to quality of care in multiple publications.
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 | 0.022 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| 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".