Comments on "Using Lean-Based Systems Engineering to Increase Capacity in the Emergency Department"
Bibliographic record
Abstract
White BA, Chang Y, Grabowski B G. Using Lean-Based Systems Engineering to Increase Capacity in the Emergency Department. West J Emerg Med. 2014;15(7):770–776. To the editor: We read with interest the article by White et al., “Using Lean-Based Systems Engineering to Increase Capacity in the Emergency Department,” in which the authors conclude that Lean could improve emergency department (ED) throughput and capacity. A number of other studies have also suggested that Lean is beneficial in addressing the problem of ED wait times. As in White et al., the vast majority of these studies have been conducted in single centers and/or as before-after evaluations.1–6 Moreover, publication bias likely also plays a role in the consistency of these findings since positive evaluations are more likely to be published.7 Although White et al. compared changes in ED length of stay with a concurrent population in their own center, it is not possible to generalize beyond this particular ED. We recently published a large multi-center controlled study of Lean in Ontario, Canada, (http://www.annemergmed.com/article/S0196-0644%2814%2900516-2/fulltext) and found that while there were reductions in ED length of stay among the 36 hospitals that participated in the Lean program, similar reductions were observed among the 63 matched control hospitals over the same period. In our study, context was also important. Because Lean was part of a broader ED wait time strategy, including wait time targets, public reporting, and targeted financial incentives, it was clear that a wide array of incentives had an effect on wait times in all EDs across the region. Our conclusion is that single-center and before-after studies do not provide rigorous or generalizable evidence that Lean is effective in reducing ED length of stay. Decisions to implement should be based on solid evidence, since Lean initiatives typically require the engagement of external consultants and/or the dedication of significant internal resources for their development and implementation.
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 | 0.011 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.028 | 0.031 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".