Innovation in the United States Health-Care System’s Organization and Delivery
Bibliographic record
Abstract
The COVID-19 pandemic has had a galvanizing effect on policy-makers in the U.S. who are trying and testing innovations in delivering government-insured health care that Canada would be wise to take note of and learn from. As in Canada, the federal government in the U.S. is limited in how directly it can influence health-care delivery, even within programs such as Medicare and Medicaid that receive federal funding. However, it has used its significant funding power to offer states incentives to promote federal priorities. As a result, dozens of states have expanded eligibility and benefits in line with federal priorities in exchange for additional funding. The U.S. government has also encouraged states to experiment with innovative delivery or financing approaches within Medicaid by offering temporary, extendable waivers that exempt novel concepts from meeting all Medicaid requirements. This has allowed states to try new programs that target the expansion of benefits coverage. One waiver-based program, for example, allows elderly individuals to employ friends and relatives as home caregivers as an alternative to institutionalization. Digital-health initiatives have also been accelerated in the U.S. during the pandemic, including loosening restrictions to allow telehealth visits and “hospital-at-home” programs, which allow acute- care and post-acute care patients to convalesce in their own residences through remote monitoring and drug delivery. Recent payment reforms in the U.S., meanwhile, include allowing health-care providers to voluntarily band together to share medical and financial responsibility for providing quality, co-ordinated care with lower costs, resulting in notable savings. Episode-based, bundled payments for complete courses of therapy for certain conditions have lowered some costs with equal or better quality of care. Other promising payment-reform initiatives involve managed care programs with private sector participation in care and insurance offering bundles of benefits not provided by traditional government coverage. The health-care challenges experienced during the pandemic have also prompted the U.S. government to take a more active role in promoting more resilient supply chains for drugs and medical equipment, to reduce dependence on offshore manufacturers and introduce more transparency into the drug supply chain. It is also providing funds for training grants and student-loan repayment relief for health-care workers, to help ensure there are enough professionals being trained for the health-care workforce now and into the future. These efforts demonstrate how health policy-makers in the U.S., in light of the COVID-19 pandemic, are driven by innovation in navigating the trade-offs between delivering quality care, expanding care, controlling costs, and allowing for regional autonomy. These considerations are not unlike those faced by Canadian policy-makers, who can learn from American innovations to help Canada emerge from the pandemic with a stronger health-care system.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".