Enhancing Surgical Performance Outcomes Through Process‐driven Care: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Recent evidence has demonstrated the variability in quality of postoperative care, as measured by rates of failure to rescue (FTR). The identification of structure- and process-related factors affecting the quality of postoperative care is the first step towards understanding and improving outcomes. The aim of this review is to review current evidence for structure and process factors affecting postoperative care. METHODS: A systematic review was conducted. Studies were selected that examined structure or process variables affecting FTR rates and postoperative outcomes. Quality analysis with Jadad and Newcastle-Ottawa scales was conducted and poor-quality studies were excluded. RESULTS: Thirty-seven studies were included in final analysis. Of these, 23 were related to enhanced recovery protocols in seven surgical specialties. Twenty-one of these 23 studies reported decreases in length of stay. Six studies also reported decreases in morbidity. No studies reported increases in stay duration or morbidity. Of the 16 studies that examined other structural and process factors, the strongest evidence was for the association between nursing ratios and FTR rates. The effects of hospital size, resources, and subspecialist care processes were less clear. CONCLUSION: Process-led care represents a clear, evidence-based approach that can be integrated on a local scale, without necessitating major structural or organisational change, to improve outcomes and may also be cost effective. To foster success, process improvement must be driven on a local level and backed up by appropriate understanding, education, and multidisciplinary involvement.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".