Provider Practice Competition and Adoption of Medicare’s Oncology Care Model
Bibliographic record
Abstract
BACKGROUND: There is a concern that the Oncology Care Model (OCM), a voluntary bundled payment program, may incentivize mergers and acquisitions among physician practices leading to reduced competition and price increases. These concerns are heightened if OCM is preferentially adopted in competitive health care markets because it could result in reduced competition, but little is known about the characteristics of markets where OCM is adopted. OBJECTIVE: To measure the association between regional market competition among medical oncologists with the initial adoption of OCM. RESEARCH DESIGN: The Herfindahl-Hirschman Index (HHI), a measure of competition, was calculated for hospital referral regions (HRRs) using secondary data from the Centers for Medicare and Medicaid Services. The relationship between HHI and OCM adoption was assessed using a 2-part regression model adjusting for the market-level number of practices, physician density, average practice size, sociodemographic characteristics, and medical resources. A count model on all HRRs was also estimated to assess an overall effect. SUBJECTS: A total of 10,788 physicians in 3,537 practices who billed Medicare for oncology services in 2015. RESULTS: OCM was adopted in 114 (37%) of the 306 HRRs. We found that practices in competitive health care markets were more likely to adopt OCM than in noncompetitive markets. Two-part regression analysis indicated a nonlinear relationship between HHI and OCM adoption. Average practice size, number of practices in an HRR, and the hospital bed rate were positively associated with adoption, whereas the rate of full-time equivalent hospital employees to 1000 residents was negatively associated with adoption. CONCLUSIONS: OCM adoption was higher in HRRs with greater competition. Careful monitoring of market-level changes among OCM adopters should be undertaken to ensure that the benefits of the OCM outweigh the negative consequences of possible changes in competition.
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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".