Wrong-site, wrong-procedure, and retained foreign object events in out-of-hospital settings: analysis of closed medico-legal complaints in Canada (2012–2021)
Bibliographic record
Abstract
BACKGROUND: Surgical sentinel events (SSEs) are serious safety incidents associated with significant patient harm and medico-legal consequences for healthcare teams and institutions. SSEs include wrong-site surgeries, wrong procedures, and unintentional retention of foreign objects. SSEs occur in hospitals and out-of-hospital operating spaces (physician offices or ambulatory surgical centres). It is unclear how the resource constraints and workflow differences of an out-of-hospital setting contribute to SSEs. METHODS: We conducted a retrospective review and descriptive content analysis of all out-of-hospital SSEs reported to the Canadian Medical Protective Association (CMPA) between 2012 and 2021. Medico-legal files, medical records, and peer expert opinions were analyzed to identify the contributing factors to out-of-hospital wrong-site, wrong-procedure, and retained-object SSEs. RESULTS: A total of 276 medico-legal complaints involved a wrong-site, wrong-procedure or retained-object SSE, of which 24 (24/276; 9%) occurred out of hospital. Only twenty of these out-of-hospital complaints were included in the qualitative content analysis. We identified five main contributing factor categories to out-of-hospital SSEs. These categories included (1) incomplete preoperative verification, (2) inadequate intraoperative surgical counts, (3) insufficient review of patient medical records, (4) surgery performed without the necessary resources, and (5) administrative errors or office disorganization. Half of the complaints were assigned more than one contributing factor. The majority of out-of-hospital SSEs (19/20; 95%) resulted in an unfavourable outcome for the operating physician and most (18/20; 90%) required additional healthcare resources to resolve or mitigate the consequences of the SSE. CONCLUSIONS: Recognizing the contributing factors to an out-of-hospital SSE enables targeted improvements in facility protocols to support patient safety. Some factors identified in this dataset overlap with hospital-based contributing factors previously identified in literature (incomplete preoperative verification and inadequate surgical counts), whereas other novel factors are associated with the practice environment of an out-of-hospital setting (resource constraints, office disorganization). Addressing the identified contributing factors may mitigate the risk of SSEs in all facilities.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".