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Record W7033175474

Quality Measurement and Accountability for Community-Based Serious Illness Care: Synthesis Report of Convening Findings and Conclusions

2017· report· en· W7033175474 on OpenAlexaboutno aff

Bibliographic record

VenueIssue Lab (Candid) · 2017
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityIncentiveQuality (philosophy)Health carePaymentQuality managementFoundation (evidence)Set (abstract data type)
DOInot available

Abstract

fetched live from OpenAlex

The movement of U.S. health care to value-based payment presents a critical opportunity to improve accountability for the quality of serious illness care, while constraining the growth of spending. The changing incentives in the health care system are driving innovation in the delivery of serious illness care in traditional Medicare, Medicare Advantage and commercial plans. Implementation of an accountability system for serious illness care is vital for ensuring that cost containment efforts do not result in undertreatment or worse quality of care for the seriously ill.In May 2017, the Gordon and Betty Moore Foundation convened 45 serious illness care experts and stakeholders - such as physicians, researchers, patient advocates, policy experts - in Banff, Alberta, Canada, to identify a path forward for building an accountability system for high-quality, community-based serious illness care programs. The group reached consensus on a definition of the serious illness population, the necessary components of an accountability system and guiding principles for quality measurement. In addition, convening participants identified a starter set of quality measures, future pathways for implementation of an accountability system and needed future research.

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)0.021
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.110
GPT teacher head0.385
Teacher spread0.275 · 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 designNot applicable
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

Citations0
Published2017
Admission routes1
Has abstractyes

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