The Evaluation of Bundled Care for Patients with Congestive Heart Failure and Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Bundled care, also known as the Integrated Funding Model initiative, was implemented by the Ontario Ministry of Health for patients with chronic obstructive pulmonary disease (COPD) and congestive heart failure (CHF). The intervention focused on integrating acute and home care for up to 60 days post-discharge. This thesis generated evidence with three studies to inform health care administrators’ and policymakers’ decisions to scale, spread, or redesign bundled care for patients with COPD and CHF. The first study applied machine learning (ML) approaches to predict 90-day unplanned readmission or death in patients with CHF and compared their performance with a commonly used regression model called the LACE+ index. Overall, the ML models showed modest model discrimination. The gradient boosting machine model had the best discrimination with an Area Under the Receiver Operating Curve (AUROC) of 0.700, which was marginally better than the logistic regression model (AUROC: 0.695) and moderately better than the LACE+ index (AUROC: 0.648). The second study conducted an effectiveness and cost-effectiveness evaluation of the bundled care program among patients hospitalized for COPD and CHF within a 24-month follow-up. In patients with COPD, bundled care was associated with a decrease in the risk of death and an increase in unplanned readmission, but no difference in the risk of emergency department visits within 24 months. In patients with CHF, there were no differences in these outcomes. Participation in the program was cost-effective for both conditions and resulted in significant cost savings in patients with CHF but not significant in COPD. The third study examined bundled care’s level of intervention fidelity for patients with COPD and CHF, and whether it was associated with unplanned readmission within 12 months of follow-up. This analysis suggests that while in aggregate, the intervention fidelity of bundled care for patients with COPD and CHF was not associated with readmission within a 12-month follow-up, specific intervention components were associated with reduced readmission. As healthcare leaders and administrators seek to improve patient outcomes as they transition from hospital to home, evidence from this thesis can inform decision-making regarding the implementation of bundled care in Ontario.
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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 | 0.053 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".