Medicare Opt-Out Trends Among Dermatologists May Reflect Systemic Health Policy: Cross-sectional Analysis
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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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".