Innovation in the U.S. Health Care System’s Organization and Delivery
Bibliographic record
Abstract
Over the past decades, the U.S. has attempted a wide array of innovations in the areas of health care organization and delivery. Canadian policy makers may be interested in some of the successes and failures in the United States health care system. This briefing summarizes recent trends in six areas: fiscal federalism, expanding benefits, payment reform, virtual and digital health, supply chain reforms and healthcare workforce. The federal government allocates money to the states based on per capita income in that state to support state health care programs such as the Medicaid program and Children’s Health Insurance Program. Additional fiscal transfers are also used to incentivize states to provide other services. Benefit expansion currently focuses on expanding Medicare to include hearing care, broadening the benefits covered by Medicare Advantage (managed care plans) plans, and using Medicaid waiver programs to expand eligibility and benefits for low-income individuals. Alternative Payment Models and expanding Medicare Advantage are transforming the system from fee-for-service toward a value-based system. Accelerated use of digital and virtual care is being promoted by waiving restrictions on coverage of telehealth services for acute and chronic conditions and primary care. Shortages of health care inputs, especially pharmaceuticals, were a chronic problem exacerbated by COVID-19. In response, onshoring of pharmaceutical production and expanding drug shortage surveillance and transparency in the drug supply chain is starting. Finally, the federal government has established research centers to track the number of primary care doctors and improve the distribution of physicians in the most disadvantaged areas.
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 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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".