Near-misses are an opportunity to improve patient safety
Bibliographic record
Abstract
PURPOSE OF REVIEW: Near-miss investigations in high reliability organizations (HROs) aim to mitigate risk and improve system safety. Healthcare settings have a higher rate of near-misses and subsequent adverse events than most high-risk industries, but near-misses are not systematically reported or analyzed. In this review, we will describe the strategies for near-miss analysis that have facilitated a culture of safety and continuous quality improvement in HROs. RECENT FINDINGS: Near-miss analysis is routine and systematic in HROs such as aviation. Strategies implemented in aviation include the Commercial Aviation Safety Team, which undertakes systematic analyses of near-misses, so that findings can be incorporated into Standard Operating Procedures (SOPs). Other strategies resulting from incident analyses include Crew Resource Management (CRM) for enhanced communication, situational awareness training, adoption of checklists during operations, and built-in redundancy within systems. SUMMARY: Health care organizations should consider near-misses as opportunities for quality improvement. The systematic reporting and analysis of near-misses, commonplace in HROs, can be adapted to health care settings to prevent adverse events and improve clinical outcomes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".