Identifying a list of healthcare ‘never events’ to effect system change: a systematic review and narrative synthesis
Bibliographic record
Abstract
BACKGROUND: Never events (NEs) are patient safety incidents that are preventable and so serious they should never happen. To reduce NEs, several frameworks have been introduced over the past two decades; however, NEs and their harms continue to occur. These frameworks have varying events, terminology and preventability, which hinders collaboration. This systematic review aims to identify the most serious and preventable events for targeted improvement efforts by answering the following questions: Which patient safety events are most frequently classified as never events? Which ones are most commonly described as entirely preventable? METHODS: For this narrative synthesis systematic review we searched Medline, Embase, PsycINFO, Cochrane Central and CINAHL for articles published from 1 January 2001 to 27 October 2021. We included papers of any study design or article type (excluding press releases/announcements) that listed NEs or an existing NE framework. RESULTS: Our analyses included 367 reports identifying 125 unique NEs. Those most frequently reported were surgery on the wrong body part, wrong surgical procedure, unintentionally retained foreign objects and surgery on the wrong patient. Researchers classified 19.4% of NEs as 'wholly preventable'. Those most included in this category were surgery on the wrong body part or patient, wrong surgical procedure, improper administration of a potassium-containing solution and wrong-route administration of medication (excluding chemotherapy). CONCLUSIONS: To improve collaboration and facilitate learning from errors, we need a single list that focuses on the most preventable and serious NEs. Our review shows that surgery on the wrong body part or patient, or the wrong surgical procedure best meet these criteria.
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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 | 0.106 | 0.321 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.032 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".