Priority setting and resource allocation in the US health system: is there a place for hard caps?
Bibliographic record
Abstract
PURPOSE: The United States devotes a larger share of its GDP to health care and spends more on health care per capita than any other country. The sheer size of the total spending on health care, at approximately $3.5 trillion in 2017, puts significant pressure on all payers and crowds out other forms of public and private spending. DESIGN/METHODOLOGY/APPROACH: In this brief commentary the authors suggest that, as part of the effort to deal with this pressure, the United States should look at borrowing a cost containment strategy from other countries: the use of hard caps on spending growth. The authors draw on our their experience of working with decision-makers over the last 20 years on the topic of priority setting to put forward some ideas on whether there is potential for application of trade-offs in the United States. FINDINGS: As hard caps force choices to be made, a necessary condition for successful implementation of this policy is the presence of an effective priority-setting framework to ensure that the right choices are made in operationalizing spending limitations. Work on this topic elsewhere can provide some insight into the use of a criteria-based framework for priority setting that purports transparency in decision-making to achieve value-based decisions. ORIGINALITY/VALUE: Other countries still have much work to do, but there is a substantial track record of using formal priority-setting approaches that could potentially inform practice in the United States. We suggest that there are key segments of the US healthcare system where the adoption of formal priority-setting frameworks to guide trade-off decisions is feasible. Piloting such activity in these contexts is the next natural step in this line of inquiry.
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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 | 0.121 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".