Impact of a Preoperative Safety Checklist on Perioperative Quality Outcomes and Operative Efficiency
Bibliographic record
Abstract
INTRODUCTION: Additional preoperative safety checklist requirements at Penn State Health were recently implemented on the morning of surgery. We evaluate whether this added safety policy impacted reported adverse patient safety events and institutional operating room performance indicators. METHODS: Key operating room performance indicators from fiscal years 2016 through 2019 quarter 2 were reviewed. Additional attestation requirements by the attending surgeon were implemented at start of fiscal year 2018 (July 1, 2017). All reported perioperative patient safety events during this time were reviewed with events classified into one of 8 select categories. RESULTS: Total operative case volume was 49,894 cases in fiscal years 2016 and 2017, and 36,533 in fiscal years 2018 and 2019 through quarter 2 (p <0.10). Mean operating room use was 81% in both groups (p <0.46). First case on time start rates decreased from 79.5% to 58%, respectively (p <0.0001). Mean turnover time between cases also increased from 35 minutes to 40 minutes, respectively (p <0.0001). There were 252 patient events (0.51%) in fiscal years 2016 and 2017 compared to 94 (0.26%) events in fiscal years 2018 and 2019 through quarter 2 following implementation of the surgical safety checklist (p <0.0001). The number of incomplete preoperative checklists (39 vs 14, p=0.008) and missing/incomplete patient identification bands (15 vs 2, p=0.004) were also decreased between fiscal years 2016 and 2017, and fiscal years 2018 and 2019 through quarter 2, respectively. CONCLUSIONS: A day-of-surgery safety checklist decreased the number of reported patient safety events but with a negative impact on standard metrics of operating room productivity for 18 months following implementation. Further study is necessary to determine if these effects persist over time.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".