Increasing Completion Rate and Benefits of Checklists: Prospective Evaluation of Surgical Safety Checklists With Smart Glasses
Bibliographic record
Abstract
BACKGROUND: Studies have demonstrated that surgical safety checklists (SSCs) can significantly reduce surgical complications and mortality rates. Such lists rely on traditional posters or paper, and their contents are generic regarding the type of surgery being performed. SSC completion rates and uniformity of content have been reported as modest and widely variable. OBJECTIVE: This study aimed to investigate the feasibility and potential of using smart glasses in the operating room to increase the benefits of SSCs by improving usability through contextualized content and, ideally, resulting in improved completion rates. METHODS: We prospectively evaluated and compared 80 preoperative time-out events with SSCs at a major academic medical center between June 2016 and February 2017. Participants were assigned to either a conventional checklist approach (poster, memory, or both) or a smart glasses app running on Google Glass. RESULTS: Four different surgeons conducted 41 checklists using conventional methods (ie, memory or poster) and 39 using the smart glasses app. The average checklist completion rate using conventional methods was 76%. Smart glasses allowed a completion rate of up to 100% with a decrease in average checklist duration of 18%. CONCLUSIONS: Compared with alternatives such as posters, paper, and memory, smart glasses checklists are easier to use and follow. The glasses allowed surgeons to use contextualized time-out checklists, which increased the completion rate to 100% and reduced the checklist execution time and time required to prepare the equipment during surgical cases.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".