Bundled payments for care improvement initiative – insights from the test pilots of payment reform
Bibliographic record
Abstract
Background: The Medicare Bundled Payments for Care Improvement (BPCI) pilot program aims to reward high-value providers by setting a global payment target for particular episodes of care. The representativeness of BPCI participants will influence the ability of this pilot to inform policy decisions.Methods: We linked the Medicare lists of participants in the risk-bearing portion of BPCI Model 2, encompassing acute and post-acute care, to the American Hospital Association resource file and the 2013 Hospital Value-Based Purchasing quality performance data. We classified episode-initiating hospitals by the number of bundles in which they were participating into “narrow”, “medium” and “comprehensive”. The analysis described the characteristics of hospitals in each of these categories.Results: The 105 hospitals with linkable data were predominantly large, urban, non-profit, teaching hospitals. These hospitals were quite similar to the general population in terms of disproportionate share, Medicare, and Medicaid percentages. Most participants selected a narrow number of bundles, with the majority selecting a single bundle around joint replacement. There were only minor differences in quality between Model 2 participants and non-participants.Conclusions: Informing the decision about whether to scale the BPCI program nationally will require evaluation of the pilot’s performance by participants’ characteristics to understand in what conditions and for which providers the program is most effective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".