How Well Is Surgical Improvement Being Conducted? Evaluation of 50 Local Surgery-Related Improvement Efforts
Bibliographic record
Abstract
BACKGROUND: Delivering high-quality care is paramount; however, evaluations show mixed results. Studies assessing improvement efforts in nonsurgical disciplines show suboptimal conduct, yet little is known about how well improvement efforts in surgery are conducted. This study evaluates local surgical improvement efforts to determine whether opportunities exist to improve their conduct. STUDY DESIGN: Fifty consecutive improvement efforts were collected from hospitals participating in 1 of 5 American College of Surgeons Quality Accreditation/Verification Programs. Conduct of these efforts was evaluated using a quality framework (with 39 criteria grouped into 8 components). Descriptive, paired, and 1-way ANOVA analyses were undertaken. RESULTS: The mean percentage of 39 criteria fulfilled for the 50 improvement efforts was 36% (range 0% to 72%). Individual criterion scores ranged from 0% to 82%. The 2 highest scoring criteria were improvement planning and problem documentation; the 2 lowest scoring were value assessments and stakeholder value perspective. The highest scoring framework component addressed End-of-Project Decision-Making (47%); the lowest was Cost Evaluation (3%). Twenty-four percent of 50 improvement efforts reported full achievement of project goals, 32% reported partial achievement, and 44% reported no achievement. Higher scores were associated with projects having full/partial achievement of stated project goals vs projects not achieving project goals (p < 0.05). Higher scores were not associated with hospital characteristics (eg bed size, teaching status) or improvement characteristics (eg improvement strategy). CONCLUSIONS: Evaluation of local surgical improvement efforts shows opportunities for improvement. Better-conducted improvement efforts were associated with more effective improvement. To support better surgical quality of care, improvement efforts need to improve.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".