The New Politics of US Health Care Prices: Institutional Reconfiguration and the Emergence of All-Payer Claims Databases
Bibliographic record
Abstract
Prices are a significant driver of health care cost in the United States. Existing research on the politics of health system reform has emphasized the limited nature of policy entrepreneurs' efforts at solving the problem of rising prices through direct regulation at the state level. Yet this literature fails to account for how change agents in the states gradually reconfigured the politics of prices, forging new, transparency-based policy instruments called all-payer claims databases (APCDs), which are designed to empower consumers, purchasers, and states to make informed market and policy choices. Drawing on pragmatist institutional theory, this article shows how APCDs emerged as the dominant model for reforming health care prices. While APCD advocates faced significant institutional barriers to policy change, we show how they reconfigured existing ideas, tactical repertoires, and legal-technical infrastructures to develop a politically and technologically robust reform. Our analysis has important implications for theories of how change agents overcome structural barriers to health reform.
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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 | 0.024 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".