Improving the Rate of Same-Day Discharge in Gynecologic Oncology Patients Undergoing Minimally Invasive Surgery—An Enhanced Recovery After Surgery Quality Improvement Initiative
Bibliographic record
Abstract
OBJECTIVES: The objectives of our quality improvement (QI) initiative were (1) to increase the rate of same-day discharge (SDD) in eligible gynecologic oncology (GO) patients to 70% and (2) to evaluate the ease with which QI methods demonstrated in one study could be applied at another center. DESIGN: A pre-/postintervention design was used (50 patients/group). SETTING: SDD in patients undergoing minimally invasive GO surgery is a recent trend aligned with Enhanced Recovery After Surgery (ERAS) principles. SDD in GO is safe and feasible based on several recent studies, including a QI initiative in Edmonton, Alberta, which resulted in SDD rates >70%. PATIENTS: A baseline audit of GO patients at our center (Calgary, Alberta) found the SDD rate to be 14%. Given that Edmonton and our center are within the same province, they have similar patient populations and available resources-suggesting that interventions from the Edmonton QI initiative may be translatable. INTERVENTIONS: Four interventions were designed to address root causes for failed SDD identified after QI diagnostics: (1) SDD as the default discharge plan, including a "Day Surgery" surgical booking; (2 and 3) development and implementation of ERAS SDD preoperative and postoperative order sets; and (4) patient education SDD-specific documents. MEASUREMENTS AND MAIN RESULTS: Rate of SDD was measured together with patient demographics and surgical outcomes. Process and balancing measures were defined and tracked. SDD in GO increased from 14% (7 of 50) to 82% (41 of 50) after the implementation of the above-mentioned interventions (odds ratio [OR], 28; p <.001; 95% confidence interval [CI], 9.54-82.11). Improved SDD was achieved without negatively affecting postoperative rates of emergency department visits: 8% pre- and 4% postintervention within 7 days (OR, 0.48; p = .678; 95% CI, 0.09-2.74) and 12% pre- and 10% postintervention within 30 days (OR, 0.8148; p = 1.001; 95% CI, 0.2317-2.86). CONCLUSION: This ERAS QI initiative resulted in a substantial increase in SDD in GO, without a negative impact on balancing measures. We demonstrate that the "spread" of simple, clearly defined QI interventions across centers (where the patient population is similar) is feasible. This suggests that an ERAS SDD program for GO could be a realistic goal for other centers with similar characteristics.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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 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".