Opportunities for Quality Measurement to Improve the Value of Care for Patients With Multiple Chronic Conditions
Bibliographic record
Abstract
Quality measurement efforts have not historically focused on pa-tients with multiple chronic conditions (MCCs), despite them com-prising one quarter of the population and two thirds of health care spending. The Patient Protection and Affordable Care Act (ACA) creates several mechanisms for the Centers for Medicare & Med-icaid Services (CMS) to transform quality measurement into an organized enterprise designed to support clinicians caring for this vulnerable population. This article highlights 3 emerging policy op-portunities for CMS to guide public and private quality measure-ment efforts for patients with MCCs. First, it discusses infusing an MCC framework into measure development to promote patient-centered, as opposed to single-disease–specific, performance mea-surement. Second, it describes the importance of using common performance measures for individual clinicians, hospitals, and com-munities to accelerate meaningful improvement in the prevention and management of chronic conditions across local populations. Finally, the need for longitudinal measurement as a foundation for sustained quality improvement is presented. The ACA’s expansion of insurance access and portability necessitates collaborative align-ment of chronic condition quality measurement efforts between public and private programs to develop a high-value lifelong health system.
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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 | 0.078 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".