Quality Improvement in Ambulatory Surgery Centers: A Major National Effort Aimed at Reducing Infections and Other Surgical Complications
Bibliographic record
Abstract
BACKGROUND: Surgical volume has shifted significantly from inpatient to outpatient settings, including free-standing ambulatory surgery centers (ASCs). Approaches to quality improvement (QI) and surveillance used in hospitals are not always appropriate to the ambulatory setting. METHODS: We recruited 665 ASCs in 47 US states to participate in an intervention to improve safe practice through implementation of a surgical safety checklist and infection control practices. Areas for partner contribution included recruitment, project development, content development and delivery, clinical subject matter expertise, data analysis, and facility coaching. RESULTS: Barriers to implementation and data collection were encountered during the project, requiring revisions to the implementation plan. Project activities, such as facility recruitment, data measurement, and implementation strategies were modified to meet ASC-specific needs. Several ASC-specific tools were designed. CONCLUSIONS: The increasing number of patients being cared for in ASCs makes it essential to better understand how to implement quality improvement projects in that environment. Tailoring interventions to the ASC's unique needs is necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".