Improving patient safety: an economic perspective on the role of nurses
Bibliographic record
Abstract
AIMS: This paper synthesises patient safety research and insights from economic theory to generate guidance for nurse managers. The paper describes the key roles nurses and nurse managers can play in improving patient safety, and explains how insights from health economics can help inform and enhance this role, helping nurse managers to set priorities for improvement and for future research. BACKGROUND: Awareness of the need to improve patient safety is high, but insufficient attention has been paid to the cost-effectiveness of safety improvements, leading to difficulty in setting priorities. This paper suggests specific methods that nurses can and should use to prioritize and evaluate safety improvements. EVALUATION: This is a review article, synthesising the results of research on patient safety. KEY ISSUES: Because of their close connection to patients, nurses (and nurse managers in particular) have key roles to play in improving patient safety. Improving patient safety will also benefit nurses and other practitioners directly, because caregivers suffer lasting distress from being involved in incidents that harm patients. Reducing harmful incidents should also reduce attrition and alleviate chronic staffing shortages. Insights from health economics can help nurse managers to set priorities for improvement and to more effectively evaluate the changes made. CONCLUSIONS: Evidence on the costs and effects of most safety improvements is still lacking. Nurses can and should take a leadership role in implementing changes and evaluating their costs and effects. IMPLICATIONS FOR NURSING MANAGEMENT: To lead improvements in patient safety, nurse managers need to learn to use the Plan-Do-Study-Act Improvement Cycle, and need to develop an awareness of and ability to measure the costs and effects of changes. These changes would allow nurse managers to better make the business case for patient safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".