Variation in tonsillectomy cost and revisit rates: analysis of administrative and billing data from US children’s hospitals
Bibliographic record
Abstract
Background Tonsillectomy is one of the most common and cumulatively expensive surgical procedures in children. We determined if substantial variation in resource use, as measured by standardised costs, exists across hospitals for performing tonsillectomy and if higher resource use is associated with better quality of care, as measured by revisits to hospital. Methods We conducted a retrospective analysis of children undergoing routine outpatient tonsillectomy between 2011 to 2017 across US children's hospitals using an administrative and billing data source. The primary outcome measures were the hospital tonsillectomy standardised cost and the 30-day revisit rate to hospital. We analysed the interhospital variation in standardised cost by determining the number of outlier hospitals in standardised cost and the intraclass correlation coefficient. Results 131 814 children (median age 6 years, IQR: 4,9; female sex 52.5%) underwent tonsillectomy for airway obstruction (62.9%) and infection (23.9%) across 28 hospitals. The median adjusted hospital standardised cost for tonsillectomy was $2392 (IQR: $1827, $2793; range: $1166 to $4222). There was substantial interhospital variation in costs as 11 (40%) hospitals were cost outliers, and the intraclass correlation coefficient was 0.62, suggesting that 62% of the variation in cost was attributable to variation between hospitals. The median hospital revisit rate was 9.5% (IQR: 7.8, 12.1) and higher hospital costs did not correlate with lower revisit rates (rs =0.03, 95% CI −0.36 to 0.41; p=0.87). Conclusions There is substantial variation in hospital resource use and standardised costs for routine outpatient tonsillectomy across US children’s hospitals. Higher resource use is not associated with lower revisit rates. Further study is needed to understand the practices of lower resource use hospitals who deliver high quality of care.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".