The Effect of a Lean Quality Improvement Implementation Program on Surgical Pathology Specimen Accessioning and Gross Preparation Error Frequency
Bibliographic record
Abstract
Few reports have documented the effectiveness of Lean quality improvement in changing anatomic pathology patient safety. We used Lean methods of education; hoshin kanri goal setting and culture change; kaizen events; observation of work activities, hand-offs, and pathways; A3-problem solving, metric development, and measurement; and frontline work redesign in the accessioning and gross examination areas of an anatomic pathology laboratory. We compared the pre- and post-Lean implementation proportion of near-miss events and changes made in specific work processes. In the implementation phase, we documented 29 individual A3-root cause analyses. The pre- and postimplementation proportions of process- and operator-dependent near-miss events were 5.5 and 1.8 (P < .002) and 0.6 and 0.6, respectively. We conclude that through culture change and implementation of specific work process changes, Lean implementation may improve pathology patient safety.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".