Insights From Transformations Under Way At Four Brookings-Dartmouth Accountable Care Organization Pilot Sites
Bibliographic record
Abstract
This cross-site comparison of the early experience of four provider organizations participating in the Brookings-Dartmouth Accountable Care Organization Collaborative identifies factors that sites perceived as enablers of successful ACO formation and performance. The four pilots varied in size, with between 7,000 and 50,000 attributed patients and 90 to 2,700 participating physicians. The sites had varying degrees of experience with performance-based payments; however, all formed collaborative new relationships with payers and created shared savings agreements linked to performance on quality measures. Each organization devoted major efforts to physician engagement. Policy makers now need to consider how to support and provide incentives for the successful formation of multipayer ACOs, and how to align private-sector and CMS performance measures. Linking providers to learning networks where payers and providers can address common technical issues could help. These sites' transitions to the new payment model constitutes an ongoing journey that will require continual adaptation in the structure of contracts and organizational attributes.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".