Is quality important to our patients? The relationship between surgical outcomes and patient satisfaction
Bibliographic record
Abstract
BACKGROUND: With greater transparency in health system reporting and increased reliance on patient-centred outcomes, patient satisfaction has become a priority in delivering quality care. We sought to explore the relationship between patient satisfaction and short-term outcomes in patients undergoing general surgical procedures. METHODS: Satisfaction surveys were distributed to patients following discharge from the general surgery service at an academic hospital between June 2012 and March 2015. Short-term clinical outcomes were obtained from the American College of Surgeons National Surgical Quality Improvement Program database. Patients rated their level of satisfaction on a 5-point Likert scale, and ordered logistic regression model was used to determine predictors of high patient satisfaction. RESULTS: 757 patient satisfaction surveys were completed. The mean age of patients surveyed was 52.2 years; 60.0% of patients were female. The majority of patients underwent a laparoscopic procedure (85.9%) and were admitted as inpatients following surgery (72%). 91.5% of patients rated satisfaction of 4-5, and 95.0% said they would recommend the service. The odds of overall satisfaction were lower in patients who had complications (OR: 0.52, 95% CI 0.31 to 0.87) and 30-day readmission (OR: 0.35, 95% CI 0.17 to 0.70). Having elective surgery was associated with higher odds of satisfaction (OR: 1.62, 95% CI 1.07 to 2.47). CONCLUSIONS: We found a significant association between patient satisfaction and both 30-day readmission and the occurrence of postoperative surgical complications. Given this association, further study is warranted to evaluate patient satisfaction as a healthcare quality indicator.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".