Education Enhancements and Text Messaging Support After Adenotonsillectomy to Decrease Emergency Department Visits: A Quality Improvement Study
Bibliographic record
Abstract
Purpose The purpose of this quality improvement (QI) project was to decrease preventable emergency department (ED) visits within 30 days post tonsillectomy and adenotonsillectomy surgery at a large, pediatric urban tertiary care hospital by 10% by December 31, 2023. Design & Methods Pediatric otolaryngology (ENT) nurses initiated a QI process to improve patient family education to decrease preventable ED visits. Plan–Do–Study–Act cycles tested improvements through utilization of visual aids during education, incorporating videos, and creating Tonsil Texts. Emergency Department visits were tracked on the department dashboard and surveys were collected on Day 14 texts. Annual change in the return to ED percentages between 2022 and 2023 was calculated with a two-proportion z-test. Surveys were reviewed for overall experience with Tonsil Texts. Results In 2023, 11.11% returned to the ED within 30 days, compared to 13.78% in 2022, reflecting a statistically significant improvement. This reduction is associated with an estimated cost savings of $78,100 for the organization. In 2023 ( n = 238), 96.6% of patient families’ found Tonsil Texts helped manage their child's pain, 97.5% found the information useful, 93.7% felt delivered at the appropriate times, and 95.4% indicated it helped them know who to contact for concerns. Families reported messaging improved their coping and confidence to care for their child. In 2024, ED visits remained significantly lower (10.24%), indicating sustainability with our interventions. Practice Implications Multifaceted improvements to in-clinic education, followed by postop Tonsil Texts, supported the information needs of patient families to reduce preventable ED visits.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".