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Record W2090845377 · doi:10.1377/hlthaff.2011.1219

Insights From Transformations Under Way At Four Brookings-Dartmouth Accountable Care Organization Pilot Sites

2012· article· en· W2090845377 on OpenAlexaff
Bridget K. Larson, Aricca D. Van Citters, Sara A. Kreindler, Kathleen L. Carluzzo, Josette N. Gbemudu, Frances M. Wu, Eugene C. Nelson, Stephen M. Shortell, Elliott S. Fisher

Bibliographic record

VenueHealth Affairs · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsWinnipeg Regional Health Authority
Fundersnot available
KeywordsIncentivePaymentBusinessQuality (philosophy)Adaptation (eye)Public relationsHealth careOrganizational structurePolitical sciencePsychologyManagementEconomicsFinanceEconomic growth

Abstract

fetched live from OpenAlex

This cross-site comparison of the early experience of four provider organizations participating in the Brookings-Dartmouth Accountable Care Organization Collaborative identifies factors that sites perceived as enablers of successful ACO formation and performance. The four pilots varied in size, with between 7,000 and 50,000 attributed patients and 90 to 2,700 participating physicians. The sites had varying degrees of experience with performance-based payments; however, all formed collaborative new relationships with payers and created shared savings agreements linked to performance on quality measures. Each organization devoted major efforts to physician engagement. Policy makers now need to consider how to support and provide incentives for the successful formation of multipayer ACOs, and how to align private-sector and CMS performance measures. Linking providers to learning networks where payers and providers can address common technical issues could help. These sites' transitions to the new payment model constitutes an ongoing journey that will require continual adaptation in the structure of contracts and organizational attributes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.066
GPT teacher head0.261
Teacher spread0.195 · 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.

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

Citations72
Published2012
Admission routes1
Has abstractyes

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