A conceptual model of Lean culture adoption in healthcare
Bibliographic record
Abstract
Purpose This work seeks to offer a greater understanding of Lean healthcare implementation challenges conceptually taking a situated cultural organizational change perspective. Design/methodology/approach A descriptive model of healthcare organizations’ Lean adoption trajectories is built using ripple and bridging modelization strategies from elements of three classic organizational change theories and knowledge from Lean, organizational culture, healthcare and operations management literature. Findings The “contingent Lean culture adoption” (CLCA) model suggests five theoretical trajectories the healthcare organizations may experience when conducting a Lean transformation. These trajectories evolve from a new concept of Lean cultural friction (LCF) which represents cultural friction that a healthcare organization encounters toward an ultimate Lean culture proficiency state through time. From high to low initial LCF, a healthcare organization may in its Lean proficiency course end up in three states: lower, similar or higher LCF situation. Research limitations/implications The CLCA model demonstrates the potential to be developed into a framework and possibly a Lean cultural friction theory pending further qualitative and quantitative validation. Practical implications The CLCA model may help healthcare managers to use more appropriate cultural change strategies during their organization’s Lean journey. Originality/value This work enriches the concept of Lean cultural change which may apply not only to healthcare organizations but also to other ones. It suggests the existence of a healthcare organization Lean culture proficiency archetype and introduces the notion of Lean cultural friction.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".