Processes and tools to improve teamwork and communication in surgical settings: a narrative review
Bibliographic record
Abstract
Patient safety has become a global priority to support reducing harm associated with healthcare delivery.1 In Canada, patient safety incidents (PSI) are the third leading cause of death behind heart disease and stroke and are associated with an additional cost to the healthcare system of $2.75 billion each year.2 PSIs occur across the healthcare continuum, but over half are associated with surgical care, which consists of preoperative, intraoperative and postoperative care.3 4 Globally, four main threats to surgical safety have been identified: (1) insufficient recognition of safety as a public health concern, (2) lack of available data related to surgical outcomes, (3) the inconsistent implementation of existing safety practices, and (4) the complexity of the surgical setting.5 The WHO Guidelines for Safe Surgery, published in 2009, have increased and highlighted the importance of surgical safety worldwide. However, key gaps related to complexity of surgical processes still remain to be addressed. A leading cause of these events is communication failure between care providers during surgical care, and between transition points during ‘hand-offs’ or ‘handovers’.6 Information shared at these transition points is required to facilitate continuity of information and patient care, and to prevent medical errors.7 This has resulted in national organisations, such as the Canadian Patient Safety Institute (CPSI), identifying surgical safety as a key priority. In a joint review by the Canadian Medical Protective Association (CMPA) and the Healthcare Insurance Reciprocal of Canada (HIROC), data from 2004 to 2013, which consisted of 2974 legal cases, were reviewed and nearly half of the incidents occurred due to system-level factors, rather than physician or healthcare provider (HCP)-level factors.8 A frequent system-level issue was lack of adherence to protocols, such as use of the surgical safety checklist (SSC), which is intended to improve team communication.8 …
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".