Medicare Opt-Out Trends Among Dermatologists May Reflect Systemic Health Policy: Cross-sectional Analysis
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Provider opt-out of accepting Medicare insurance is a nationally tracked metric by the Centers for Medicare & Medicaid Services (CMS) for all physicians, including dermatologists. Although this usually only consists of a small number of providers, the magnitude of opting out has varied historically, often tracing changes in systemic health care policy. OBJECTIVE: In this paper, we explored dermatologist opt-out data since 2001, as reported by the CMS, to characterize trends and provide evidence that shifts in provider opt-out may represent a potential indicator of the state of health policy and possible needs for reform as it pertains to Medicare. METHODS: The publicly available Opt Out Affidavits data set, available from the CMS, was evaluated for providers in all dermatologic specialties from January 1, 2001, to May 27, 2022. RESULTS: There were a total of 196 dermatology opt-outs in the overall period, with the largest spike being 33 providers in 2016, followed by generally consistent decreases through 2021. In the most recent 12 months of data, the number of new monthly opt-outs from January 2022 to May 2022 was significantly higher than that of the trailing 7 months of 2021 (P=.03). CONCLUSIONS: Despite decreasing numbers of dermatologist opt-outs in the late-2010s, 2022 was marked by a significant increase in opt-outs. The reduced acceptance of Medicare by dermatologists may present risks to care access, so it is important to frequently assess physician opt-out data and changes over time.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it