Lean thinking in total nursing care for mechanically ventilated patients: A new concept in ICU
Bibliographic record
Abstract
Background: Lean approach is one of the coming revolutions for a better, improved, high-value-based care to maximize the benefit from nursing care activities. Additionally, it can shorten the mechanical ventilation duration and the total intensive care unit stay with a time and cost effective process. Lean is an improvement strategy based on the concept of eliminating the waste and creation of value-added care practices to the patients. Applying lean strategy for mechanically ventilated patients requires critical evaluation of all steps of the care to identify which add value and which do not.Methods: A descriptive research design was used in the current study and two tools were used for data collection in this study: “Lean assessment observational checklist of total care for mechanically ventilated patients”, and “Critical care nurses’ self-report about waste during total care of mechanically ventilated patients”.Results: The differences between value added and non-value added care practice items were not statistically significant in ventilator and patient care practices items (p = .232 and .884) respectively, while there was no statistical difference between the value added and non-value added care practice items in tube care. The differences between the time consumed in all care practices items were statistically significant (p < .001). According to the nurses' self-report, direct care for patients was ranked as the first care category that can increase the cost and effort followed by the indirect care category.Conclusions: Not all care items for mechanically ventilated patients have been added value to the patients. Waste outcomes as reported by nurses resulted in increase their efforts, time of care, in addition to increase the cost of 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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".