Effect of a health system payment and quality improvement programme for tonsillectomy in Ontario, Canada: an interrupted time series analysis
Bibliographic record
Abstract
BACKGROUND: Tonsillectomy is among the most common and cumulatively expensive surgical procedures in children, with known variations in quality of care. However, evidence on health system interventions to improve quality of care is limited. The Quality-Based Procedures (QBP) programme in Ontario, Canada, introduced fixed episode hospital payment per tonsillectomy and disseminated a perioperative care pathway. We determined the association of this payment and quality improvement programme with tonsillectomy quality of care. METHODS: Interrupted time series analysis of children undergoing elective tonsillectomy at community and children's hospitals in Ontario in the QBP period (1 April 2014 to 31 December 2018) and the pre-QBP period (1 January 2009 to 31 January 2014) using health administrative data. We compared the age-standardised and sex-standardised rates for all-cause tonsillectomy-related revisits within 30 days, opioid prescription fills within 30 days and index tonsillectomy inpatient admission. RESULTS: 111 411 children underwent tonsillectomy: 51 967 in the QBP period and 59 444 in the pre-QBP period (annual median number of hospitals, 86 (range 77-93)). Following QBP programme implementation, revisit rates decreased for all-cause tonsillectomy-related revisits (0.48 to -0.18 revisits per 1000 tonsillectomies per month; difference -0.66 revisits per 1000 tonsillectomies per month (95% CI -0.97 to -0.34); p<0.0001). Codeine prescription fill rate continued to decrease but at a slower rate (-4.81 to -0.11 prescriptions per 1000 tonsillectomies per month; difference 4.69 (95% CI 3.60 to 5.79) prescriptions per 1000 tonsillectomies per month; p<0.0001). The index tonsillectomy inpatient admission rate decreased (1.12 to 0.23 admissions per 1000 tonsillectomies per month; difference -0.89 (95% CI -1.33 to -0.44) admissions per 1000 tonsillectomies per month; p<0.0001). CONCLUSIONS: The payment and quality improvement programme was associated with several improvements in quality of care. These findings may inform jurisdictions planning health system interventions to improve quality of care for tonsillectomy and other paediatric procedures.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".