How to design Lean interventions to enable impact, sustainability and effectiveness. A mixed-method study
Bibliographic record
Abstract
Objective: This study’s aim was to assess how various organisational designs affect Lean interventions’ success. Refinement of design and analytics contributes to the knowledge of organisational change management, and promote sound investment in quality improvement.Methods: A panel of 11 experienced Lean consultants ranked the success of 17 Lean interventions implemented at a university hospital. This was done by assessing their impact on outcome, the sustainability of the improved work processes and the effectiveness regarding degree of goal achievement. The potential relationship between the interventions’ rank, organisation, targets for improvement, and use of time and resources, was analysed by a linear mixed model.Results: 30 percent of the interventions were assessed as successful, 60 percent as moderately successful, and 10 percent as unsuccessful. Employee and safety-staff representation (β 0.22 [CI 0.07–0.37]), top management attendance (β 0.14 [CI 0.10–0.18]), patient-related goals (β 0.13 [CI 0.06–0.20]) and hours in work-groups (β 0.01 [CI 0.00–0.01]) were related to impact on outcome. Interventions that ranged across divisions (β -0.45 [CI -0.75– -0.19]), employee and safety-staff representation (β 0.44 [CI 0.29–0.60]), comprehensive project organisation (β 0.22 [CI 0.08–0.36]) and patient-related goals (β 0.18 [CI 0.11–0.26]) were related to sustainability. Interventions that ranged across divisions (β -1.39 [CI -1.96– -0.81]), comprehensive project organisation (β 0.30 [CI 0.18–0.43]), employee and safety-staff representation (β 0.25 [CI 0.89–0.41]), limited top-management attendance (β -0.18 [CI -0.28– -0.08]), multi-disciplinary teams composed of several professions (β 0.16 [CI 0.08–0.24]) and patient-related goals (β 0.15 [CI 0.04–0.19]) were all related to a higher degree of effectiveness.Conclusions: To achieve quality improvement in hospitals, policymakers are advised to invest in time and a comprehensive project organisation. Furthermore, the interventions should engage multidisciplinary teams including employee and safety-staff representatives and pursue improvement for patients, across divisions. The methods applied constitute a framework for future research.
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 | 0.131 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".