Minimizing ED Waiting Times and Improving Patient Flow and Experience of Care
Bibliographic record
Abstract
We conducted a pre- and postintervention analysis to assess the impact of a process improvement project at the Cambridge Hospital ED. Through a comprehensive and collaborative process, we reengineered the emergency patient experience from arrival to departure. The ED operational changes have had a significant positive impact on all measured metrics. Ambulance diversion decreased from a mean of 148 hours per quarter before changes in July 2006 to 0 hours since April 2007. ED total length of stay decreased from a mean of 204 minutes before the changes to 132 minutes. Press Ganey patient satisfaction scores rose from the 12th percentile to the 59th percentile. ED patient volume grew by 11%, from a mean of 7,221 patients per quarter to 8,044 patients per quarter. Compliance with ED specific quality core measures improved from a mean of 71% to 97%. The mean rate of ED patients that left without being seen (LWBS) dropped from 4.1% to 0.9%. Improving ED operational efficiency allowed us to accommodate increasing volume while improving the quality of care and satisfaction of the ED patients with minimal additional resources, space, or staffing.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".