Effects of hospital funding reform on wait times for hip fracture surgery: a population-based interrupted time-series analysis
Bibliographic record
Abstract
Abstract Background Health care funding reforms are being used worldwide to improve system performance but may invoke unintended consequences. We assessed the effects of introducing a targeted hospital funding model, based on fixed price and volume, for hip fractures. We hypothesized the policy change was associated with reduction in wait times for hip fracture surgery, increase in wait times for non-hip fracture surgery, and increase in the incidence of after-hours hip fracture surgery. Methods This was a population-based, interrupted time series analysis of 49,097 surgeries for hip fractures, 10,474 for ankle fractures, 1,594 for tibial plateau fractures, and 40,898 for appendectomy at all hospitals in Ontario, Canada between April 2012 and March 2017. We used segmented regression analysis of interrupted monthly time series data to evaluate the impact of funding reform enacted April 1, 2014 on wait time for hip fracture repair (from hospital presentation to surgery) and after-hours provision of surgery (occurring between 1700 and 0700 h). To assess potential adverse consequences of the reform, we also evaluated two control procedures, ankle and tibial plateau fracture surgery. Appendectomy served as a non-orthopedic tracer for assessment of secular trends. Results The difference (95 % confidence interval) between the actual mean wait time and the predicted rate had the policy change not occurred was − 0.46 h (-3.94 h, 3.03 h) for hip fractures, 1.46 h (-3.58 h, 6.50 h) for ankle fractures, -3.22 h (-39.39 h, 32.95 h) for tibial plateau fractures, and 0.33 h (-0.57 h, 1.24 h) for appendectomy (Figure 1; Table 3). The difference (95 % confidence interval) between the actual and predicted percentage of surgeries performed after-hours − 0.90 % (-3.91 %, 2.11 %) for hip fractures, -3.54 % (-11.25 %, 4.16 %) for ankle fractures, 7.09 % (-7.97 %, 22.14 %) for tibial plateau fractures, and 1.07 % (-2.45 %, 4.59 %) for appendectomy. Conclusions We found no significant effects of a targeted hospital funding model based on fixed price and volume on wait times or the provision of after-hours surgery. Other approaches for improving hip fracture wait times may be worth pursuing instead of funding reform.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".