An analysis of bundled care funding for total hip and knee arthroplasty in Ontario, Canada: a population-based retrospective cohort study
Bibliographic record
Abstract
Background: Rising health care expenditures and dissatisfaction with traditional models of reimbursement have driven an interest in alternative payment model (APM) initiatives. Bundled funding, an APM, was implemented province-wide for elective total hip arthroplasty (THA) and total knee arthroplasty (TKA) in Ontario in 2019. In this study, we explored whether procedure volume, quality of care, and cost were affected by the program’s introduction. Methods: In this retrospective cohort study, we developed pre- and postimplementation patient cohorts with aggregate data collected from the Canadian Institute for Health Information (CIHI) and Canadian Joint Replacement Registry. We assessed quality via length of stay, 30-day readmissions, emergency department visits, and revision surgeries. We assessed costs using methodology and data provided by CIHI. We performed statistical analysis by comparing patient cohorts via χ2 and Student t tests. Results: After the introduction of the bundle, case volume increased, length of stay decreased, and more patients were discharged directly home following surgery (p ≤ 0.001). Patients with THA were less likely to be readmitted or visit the emergency department in the postbundled cohort (p ≤ 0.009). Despite a reduced length of stay, the cost of THA and TKA increased, with $106 more being spent per patient (p ≤ 0.001). Conclusion: The introduction of bundled funding for THA and TKA in Ontario was associated with preserved quality of care despite shorter lengths of stay in hospital and reduced use of inpatient rehabilitation. Although cost containment is often a goal of bundled funding, Ontario’s model saw a rise in inpatient surgical costs. A shift to outpatient arthroplasty could yield significant cost savings under the current bundle design.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".