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Record W2096174828 · doi:10.1377/hlthaff.2012.0544

A Framework For Evaluating The Formation, Implementation, And Performance Of Accountable Care Organizations

2012· article· en· W2096174828 on OpenAlex
Elliott S. Fisher, Stephen M. Shortell, Sara A. Kreindler, Aricca D. Van Citters, Bridget K. Larson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHealth Affairs · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsWinnipeg Regional Health Authority
Fundersnot available
KeywordsBusinessContext (archaeology)PaymentProcess managementKey (lock)Health careTracking (education)Set (abstract data type)Knowledge managementComputer scienceFinanceComputer securityPsychology

Abstract

fetched live from OpenAlex

The implementation of accountable care organizations (ACOs), a new health care payment and delivery model designed to improve care and lower costs, is proceeding rapidly. We build on our experience tracking early ACOs to identify the major factors-such as contract characteristics; structure, capabilities, and activities; and local context-that would be likely to influence ACO formation, implementation, and performance. We then propose how an ACO evaluation program could be structured to guide policy makers and payers in improving the design of ACO contracts, while providing insights for providers on approaches to care transformation that are most likely to be successful in different contexts. We also propose key activities to support evaluation of ACOs in the near term, including tracking their formation, developing a set of performance measures across all ACOs and payers, aggregating those performance data, conducting qualitative and quantitative research, and coordinating different evaluation activities.

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.

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)0.001
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: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.093
GPT teacher head0.387
Teacher spread0.295 · 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