Proposed Policy Changes to Cancer Care and Oncologic Drug Reimbursement: Exploring the Rationale and Anticipating the Consequences
Bibliographic record
Abstract
Although innovation in cancer treatment has improved cure rates and survival, the costs have escalated beyond societies' ability to pay. Cancer care costs in the United States are expected to rise to 245 billion by 2030. In this health policy session, we focused on three cost-containment strategies, and their potential impact on cancer care delivery. Site Neutrality: Curbing the Cost of Cancer Care, but at What Risk? , explores legislation that would introduce payment parity across differing sites of care: elimination of facility fees in hospital owned practices and shift of billing for oncology infusions away from hospital owned practices. Unintended consequences could reduce access to care for vulnerable populations, shift costs to patients, and reduce safety. Post-Pandemic Digital Health Reimbursement: Impact on Access to Care , explores how the emergency waivers, licensure flexibilities, and parity reimbursement necessitated by COVID-19 shepherded innovation that outpaced the regulatory framework needed for long-term sustainability. Today, telehealth services, hospital at home, remote patient monitoring, and decentralized clinical trial enrollment face reintroduction of regulatory and payment barriers. Will we live to see efficacy-based pricing for cancer drugs? Learning from international models , explores how lack of drug price negotiation has led to higher prices in the United States compared with Canada and Europe. Comparative clinical effectiveness pricing systems compare the value of each drug to standard treatments. Comparative cost-effectiveness programs look at the incremental cost per additional unit of health gained above the standard of care. The United States could learn from these approaches used in comparable countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".