Achieving and Sustaining Zero
Bibliographic record
Abstract
BACKGROUND: Surgical site infections (SSI) increase morbidity and mortality, hospital costs, length of stay, readmissions, and risk of litigation and may impact a facility's reputation. METHODS: Through implementation of a Six Sigma, interdisciplinary team process and the Contextual Model for change engaged all stakeholders. A total of 44 perioperative processes were evaluated, with 15 processes ultimately altered. Revisions involved identifying inconsistent implementation of procedures and standardizing processes, as well as utilizing new suture techniques and products including disposable electrocardiogram leads and pacing wires, antibiotic-coated sutures, and silver-impregnated midsternal dressings. RESULTS: In isolated coronary artery bypass grafting with donor-site procedures, an incidence of 3.74 per 100 procedures was reduced to 0.7 and ultimately to 0. No patients who underwent coronary artery bypass grafting developed a deep sternal wound infection in over 30 months and 590 procedures, resulting in an estimated cost savings of more than $600 000, from May 2012 through December 2014. CONCLUSIONS: A significant reduction in deep sternal wound infections was achieved by working at all levels of the organization through a multidisciplinary approach to create sustained change. Using real-time observations for current practices, areas for improvement were identified. By engaging frontline staff in the process, ownership of the outcomes and adherence to practice change were promoted. The result was a dramatic, rapid, and sustainable improvement in the prevention of deep sternal wound infection.
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 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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".