Designing a chest tube checklist using a multidimensional human factor approach: a hierarchical task analysis, a failure mode and effect analysis, and a bow-tie analysis
Bibliographic record
Abstract
BACKGROUND: Chest tubes are a standard procedure. Despite it being a regular intervention, complication rates can be up to 30%, impacting patient's health, morbidity, and mortality. Checklists have been shown to reduce complications for other medical procedures. Data on implementing a checklist are increasing, while data on the development are still low. A guide on analysing and designing a chest tube accordingly has not yet been published. Therefore, the aim of the study is to introduce a structured design of a chest tube checklist based on analyses using human factor analysis tools. METHODS: A multifactor analysis was performed focusing on chest tube insertion and possible complications using hierarchical task analysis, a failure mode and effective analysis (FMEA), and a bow-tie analysis. The data were collected through a workshop in addition to published literature on chest tube complications. RESULTS: The FMEA revealed events with a high level of risk priority numbers (RPNs). In total, 247 RPNs were calculated. Failure modes adding up to 147 RPNs were summarized as 'malposition of chest tube'. 'Lack of sterility' accounted for 100 RPNs. The FMEA revealed the importance of indication, preparation, and equipment in assessing risk and ultimate failure. 'Malpositioning of chest tube' was used for the bow-tie analysis. Contributing factors were extracted from the FMEA, preventative controls, recovery barriers and consequences analysed the lack of sterility was addressed by 'preventative controls', recovery barriers and consequences in preparation, equipment, and chest tube insertion. Based on these findings, a checklist was designed. CONCLUSIONS: Human factor analysis tools can be used to analyse procedure's complications and its causes. Future data will need to show the effectiveness of the checklist.
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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 | 0.026 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".