Evaluation of an Emergency Department Lean Process Improvement Program to reduce length of stay
Bibliographic record
Abstract
In recent years, Lean principles have been applied to improve wait times in the Emergency Department (ED). In 2009, an ED Process Improvement Program based on Lean methods was introduced in Ontario as part of a broad strategy to reduce ED length of stay (LOS) and improve patient flow. This study sought to determine the effect of this program on ED wait times and quality of care. We conducted a retrospective cohort study of all ED visits at program and control sites over 3 program waves from April 1, 2007 to June 30, 2011 in Ontario, Canada. Time series analyses of outcomes before and after the program and difference-in-differences analyses comparing changes in program sites with control sites were conducted. In before-after models among program sites alone, 90 percentile ED LOS did not change in Wave 1 (-14 minutes [95% CI -47, 20]) but decreased after Wave 2 (-87 [95% CI -108, -66]) and Wave 3 (-33 [95% CI -50, -17]); median ED LOS decreased after Wave 1 (-18 [95% CI -24, -12]), Wave 2 (-23 [95% CI -27, -19]), and Wave 3 (-15 [95% CI -18, -12]); in all Waves, decreases were observed in time to physician assessment, left without being seen rates, and 72-hour ED revisit rates. In the difference-in-difference models, where changes in program sites were compared with controls, the program was associated with no change in the 90 percentile ED LOS in Wave 2 (17 [95% CI -0.2, 33]) and increases in Wave1 (23 [95% CI 0.9, 45] and Wave 3 (31 [95% CI 10, 51]); modest reductions in median ED LOS in Waves 2 and 3 alone; and a decrease in time to physician assessment in Wave 3 alone. Although the program reduced ED waiting times, it appeared that its benefits were diminished or disappeared when compared with control sites, which were exposed to system-wide initiatives such as public reporting and pay-for-performance. This study suggests that further evaluation of the effectiveness of Lean methods in the ED is warranted before widespread implementation.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".