What Is Lean Management in Health Care? Development of an Operational Definition for a Cochrane Systematic Review
Bibliographic record
Abstract
Industrial improvement approaches such as Lean management are increasingly being adopted in health care. Synthesis is necessary to ensure these approaches are evidence based and requires operationalization of concepts to ensure all relevant studies are included. This article outlines the process utilized to develop an operational definition of Lean in health care. The literature search, screening, data extraction, and data synthesis processes followed the recommendations outlined by the Cochrane Collaboration. Development of the operational definition utilized the methods prescribed by Kinsman et al. and Wieland et al. This involved extracting characteristics of Lean, synthesizing similar components to establish an operational definition, applying this definition, and updating the definition to address shortcomings. We identified two defining characteristics of Lean health-care management: (1) Lean philosophy, consisting of Lean principles and continuous improvement, and (2) Lean activities, which include Lean assessment activities and Lean improvement activities. The resulting operational definition requires that an organization or subunit of an organization had integrated Lean philosophy into the organization's mandate, guidelines, or policies and utilized at least one Lean assessment activity or Lean improvement activity. This operational definition of Lean management in health care will act as an objective screening criterion for our systematic review. To our knowledge, this is the first evidence-based operational definition of Lean management in health care.
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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 | 0.261 | 0.522 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.018 |
| Bibliometrics | 0.092 | 0.054 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".