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Record W2530133993 · doi:10.1215/03616878-3702746

The New Politics of US Health Care Prices: Institutional Reconfiguration and the Emergence of All-Payer Claims Databases

2016· article· en· W2530133993 on OpenAlexafffund
Philip Rocco, Andrew S. Kelly, Daniel Béland, Michael Kinane

Bibliographic record

VenueJournal of Health Politics Policy and Law · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Saskatchewan
FundersCanada Research Chairs
KeywordsTransparency (behavior)PoliticsControl reconfigurationHealth carePublic economicsPragmatismState (computer science)Health policyPublic administrationBusinessEconomicsPolitical scienceLaw and economicsEconomic growthLaw

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.902
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.088
GPT teacher head0.353
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2016
Admission routes2
Has abstractyes

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