Independent Practice Associations And Physician-Hospital Organizations Can Improve Care Management For Smaller Practices
Bibliographic record
Abstract
Pay-for-performance, public reporting, and accountable care organization programs place pressures on physicians to use health information technology and organized care management processes to improve the care they provide. But physician practices that are not large may lack the resources and size to implement such processes. We used data from a unique national survey of 1,164 practices with fewer than twenty physicians to provide the first information available on the extent to which independent practice associations (IPAs) and physician-hospital organizations (PHOs) might make it possible for these smaller practices to share resources to improve care. Nearly a quarter of the practices participated in an IPA or a PHO that accounted for a significant proportion of their patients. On average, practices participating in these organizations provided nearly three times as many care management processes for patients with chronic conditions as nonparticipating practices did (10.4 versus 3.8). Half of these processes were provided only by IPAs or PHOs. These organizations may provide a way for small and medium-size practices to systematically improve care and participate in accountable care organizations.
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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 | 0.016 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".