Are the benefits of lean rapid process improvement workshops in healthcare worth the investment?
Bibliographic record
Abstract
Background: Many organizations have adopted Lean tools to improve healthcare, but few studies adequately evaluate the effectiveness of Lean tools, such as Rapid Process Improvement Workshops (RPIWs).Objective: To evaluate the effectiveness of RPIWs conducted in surgical services at two hospital sites from economic and statistical perspectives.Methods: Retrospective data over three years from the two interventions sites were used for a cost-benefit analysis in the form of Return on Investment (ROI). The Interrupted Time Series (ITS) method was used to analyze the trends of selected process measures such as surgical volumes, overtime, and sick time hours during intervention and post-intervention periods at the two sites. Also, comparable data from two control sites were used to statistically compare the trends of some of the process measures between the intervention and control sites.Results: The cumulative effects of the six RPIWs performed at each site were examined. The results did not produce any evidence to indicate that the outcomes justify the investments. The ITS analysis revealed no indication of systematic and sustained change in the pattern of process measures at the intervention sites as a result of RPIWs. Nor did they provide significant or conclusive evidence when comparing the process measures between the intervention and control sites.Conclusions: This study identifies some of the difficulties of empirically calculating the ROI of RPIWs, and provides evidence that any realized benefits due to RPIWs implemented in two hospitals were not worth the investment. Such a result may lead us to challenge any unfounded claims of high monetary benefits from Lean tools or similar quality improvement initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".