Root-cause analysis: swatting at mosquitoes versus draining the swamp
Bibliographic record
Abstract
Many healthcare systems recommend root-cause analysis (RCA) as a key method for investigating critical incidents and developing recommendations for preventing future events. In practice, however, RCAs vary widely in terms of their conduct and the utility of the recommendations they produce.1 ,2 RCAs often fail to explore deep system problems that contributed to safety events3 due to the limited methods used, constrained time and meagre financial/human resources to conduct RCAs.4 Furthermore, healthcare organisations often lack the mandate and authority required to develop and implement sophisticated and effective corrective actions.4 Consequently, corrective actions primarily aim at changing human behaviour rather than system-based changes.5 ,6 In this issue of BMJ Quality and Safety , Kellogg et al 7 confirm these concerns about RCAs. Reviewing 302 RCAs conducted over an 8-year period at a US academic medical centre, the authors report the most common solution types as training, process change and policy reinforcement. Serious events (eg, retained surgical sponges) recurred repeatedly despite conducting RCAs. These findings highlight the long overdue need to enhance the effectiveness of RCAs. James Reason (of the Swiss Cheese Model8) once characterised the goal of error investigations as draining the swamp not swatting mosquitoes.8 Critical incidents arise from the interplay between active failures (eg, not double checking for allergies before administering a medication) and latent conditions9 (eg, workload for the nurse and reliance on human memory for a critical safeguard when electronic systems with built-in reminders exist). Returning to Reason's analogy, we do not want to spend our time and expend our resources swatting at the mosquitoes of ‘not double checking’. Rather, we want to drain the swamp of the many latent conditions that make not double checking more likely to occur. Too often, RCA teams focus on the …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.071 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".