Reducing postoperative pain in children undergoing strabismus surgery: From bundle implementation to clinical decision support tools
Bibliographic record
Abstract
BACKGROUND: Postoperative pain is a significant cause of morbidity in pediatric anesthesia, which can result in delayed discharge and unplanned hospital admission. Children undergoing strabismus surgery are known to be a particularly high-risk group for postoperative pain. AIM: The aim of this project was to reduce the incidence of moderate to severe postoperative pain by 25% over a period of 12 months in children undergoing strabismus surgery. METHODS: This was a multidisciplinary quality improvement project using the Institute for Healthcare Improvement model for improvement and iterative Plan-Do-Study-Act cycles. Baseline data from one hundred patients were collected retrospectively from patient records. Subsequently, iterative interventions introduced comprised: a perioperative bundle (comprising preoperative acetaminophen, intraoperative dexamethasone and ketorolac, a long-acting opioid, and two anti-emetics), email reminders, dissemination of results at departmental rounds, and an intraoperative clinical decision aide. Postoperative pain data were collected as an outcome measure, and length of stay in PACU was monitored as a balancing measure. Statistical process control charts were constructed to monitor bundle compliance and incidence of postoperative pain in the postanesthesia care unit. RESULTS: Postoperative pain and bundle compliance data were collected for 1127 children in total. Baseline mean monthly incidence of moderate to severe postoperative pain was 47.3%. By the conclusion of this project, the incidence of postoperative pain decreased to 21%. Concurrently, mean bundle compliance increased to 78.7%. Mean length of PACU stay for baseline audit patients was 72.5 min compared with 70 min for patients after the introduction of the strabismus macro (November 2018-April 2019, n 91) (mean difference, 2.5; 95% CI, -3.86 to 8.86; P = .439). CONCLUSION: Through the implementation and adoption of an evidence-based bundle of care, we successfully decreased the incidence of moderate to severe postoperative pain for children undergoing strabismus repair. We demonstrated that combining nudge theory with QI methodology can be an effective means of delivering positive results in quality improvement projects.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".