Can the surgical checklist reduce the risk of wrong site surgery in orthopaedics? - can the checklist help? Supporting evidence from analysis of a national patient incident reporting system
Bibliographic record
Abstract
BACKGROUND: Surgical procedures are now very common, with estimates ranging from 4% of the general population having an operation per annum in economically-developing countries; this rising to 8% in economically-developed countries. Whilst these surgical procedures typically result in considerable improvements to health outcomes, it is increasingly appreciated that surgery is a high risk industry. Tools developed in the aviation industry are beginning to be used to minimise the risk of errors in surgery. One such tool is the World Health Organization's (WHO) surgery checklist. The National Patient Safety Agency (NPSA) manages the largest database of patient safety incidents (PSIs) in the world, already having received over three million reports of episodes of care that could or did result in iatrogenic harm. The aim of this study was to estimate how many incidents of wrong site surgery in orthopaedics that have been reported to the NPSA could have been prevented by the WHO surgical checklist. METHODS: The National Reporting and Learning Service (NRLS) database was searched between 1st January 2008- 31st December 2008 to identify all incidents classified as wrong site surgery in orthopaedics. These incidents were broken down into the different types of wrong site surgery. A Likert-scale from 1-5 was used to assess the preventability of these cases if the checklist was used. RESULTS: 133/316 (42%) incidents satisfied the inclusion criteria. A large proportion of cases, 183/316 were misclassified. Furthermore, there were fewer cases of actual harm [9% (12/133)] versus 'near-misses' [121/133 (91%)]. Subsequent analysis revealed a smaller proportion of 'near-misses' being prevented by the checklist than the proportion of incidents that resulted in actual harm; 18/121 [14.9% (95% CI 8.5-21.2%)] versus 10/12 [83.3% (95%CI 62.2-104.4%)] respectively. Summatively, the checklist could have been prevented 28/133 [21.1% (95%CI 14.1-28.0%)] patient safety incidents. DISCUSSION: Orthopaedic surgery is a high volume specialty with major technical complexity in terms of equipment demands and staff training and familiarity. There is therefore an increased propensity for errors to occur. Wrong-site surgery still occurs in this specialty and is a potentially devastating situation for both the patient and surgeon. Despite the limitations of inclusion and reporting bias, our study highlights the need to match technical precision with patient safety. Tools such as the WHO surgical checklist can help us to achieve this.
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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 | 0.152 | 0.533 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".