Quality Measurement and Accountability for Community-Based Serious Illness Care: Synthesis Report of Convening Findings and Conclusions
Bibliographic record
Abstract
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 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 | 0.177 | 0.298 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.016 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
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".