PHYSICIAN LEADERSHIP IN BUNDLED PAYMENT JOINT REPLACEMENT INITIATIVES
Bibliographic record
Abstract
Introduction Bundled budgeting of payments for joint replacement services has become increasing common in an effort to improve quality while lowering cost. In the US, some Medicare bundled payment programs are voluntary whereas some now are mandatory. Large medical care and medical management organizations have largely been assigned or seized control of management of these programs, leaving the surgeon in a subordinate role. The current abstract describes an experience where surgeons provide leadership and accept responsibility in bundled payment program. Methods We engaged a collective of 16 different private company orthopedic physician groups to apply to become episode initiators under under the Medicare Bundled Payment for Care Improvement (BPCI) models 2 and 3. The application process itself provided historical cost data, enabling each group to independently decide whether or not to proceed with the BPCI. Results Ultimately, 7 of the private orthopedic groups decided to continue with the BPCI initiative. At the first quarter reconciliation, savings ranged from 9% to 17% across the participating groups. Conclusion It is possible and potentially preferable for surgeons to take a primary role in accepting responsibility and leadership in the comprehensive care of joint replacement patients. The surgeons are those who determine the indications for and perform the surgery, accept much of the risk, and typically maintain a career long relationship with the patient. As such, the surgeon is also in the best position to achieve the ultimate goals of improved quality which simultaneously controlling cost. Our experience thus far supports that view that the more leadership surgeons provide in value base care provision, the more our patients and health care system will benefit from optimization of care delivery.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".