Critical airway-related incidents and near misses in anaesthesia: a qualitative study of a critical incident reporting system
Bibliographic record
Abstract
BACKGROUND: Many serious adverse events in anaesthesia are retrospectively rated as preventable. Anonymous reporting of near misses to a critical incident reporting system (CIRS) can identify structural weaknesses and improve quality, but incidents are often underreported. METHODS: This prospective qualitative study aimed to identify conceptions of a CIRS and reasons for underreporting at a single Swiss centre. Anaesthesia cases were screened to identify critical airway-related incidents that qualified to be reported to the CIRS. Anaesthesia providers involved in these incidents were individually interviewed. Factors that prevented or encouraged reporting of critical incidents to the CIRS were evaluated. Interview data were analysed using the Framework method. RESULTS: Of 3668 screened airway management procedures, 101 cases (2.8%) involved a critical incident. Saturation was reached after interviewing 21 anaesthesia providers, who had been involved in 42/101 critical incidents (41.6%). Only one incident (1.0%) had been reported to the CIRS, demonstrating significant underreporting. Interviews revealed highly variable views on the aims of the CIRS with an overall high threshold for reporting a critical incident. Factors hindering reporting of cases included concerns regarding identifiability of the reported incident and involved healthcare providers. CONCLUSIONS: Methods to foster anonymity of reporting, such as by national rather than departmental critical incident reporting system databases, and a change in culture is required to enhance reporting of critical incidents. Institutions managing a critical incident reporting system need to ensure timely feedback to the team regarding lessons learned, consequences, and changes to standards of care owing to reported critical incidents. Consistent reporting and assessment of critical incidents is required to allow the full potential of a critical incident reporting system.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".