De-implementation in Nursing: A Systematic Review of Strategies to Stop Low-Value Care to Improve Patient Safety and Workload Reduction
Bibliographic record
Abstract
Background: Continued delivery of low-value nursing care—practices outdated, ineffective, or even harmful—compromises patient safety and contributes to unnecessary nursing workload. While the new implementation of evidence is the focus, the systematic process of removing such practices, known as de-implementation, is needed for healthcare quality improvement. Aim: The aim of this review study is to synthesize current evidence on de-implementation in nursing, present its theoretical basis, enumerate typical low-value practices, and identify effective ways to stop them for improved patient outcomes and optimal workflow in nursing. Methods: An integrated literature review was conducted by combining results from current empirical studies, systematic reviews, and quality improvement reports on de-implementation and low-value care in nursing and interprofessional settings. Results: Routine Foley catheterization, unnecessary vital sign monitoring, and liberal physical restraint use are strong de-implementation candidates, the review implies. Successful strategies are multifaceted, including audit and feedback, clinical decision support in electronic health records, nurse-initiated protocols, and sending out professional campaigns like Choosing Wisely. Success is highly dependent on strong clinical leadership, a psychological safety culture, and interprofessional collaboration to counter cognitive inertia and embedded professional norms. Conclusion: De-implementation is an ethical and pragmatic imperative to nursing. Systematically eliminating low-value care is essential to sustain patient safety, reduce iatrogenic harm, and allow nurses to focus their skills on high-value, individualized 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 | 0.023 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".