Applying a Risk-Adjustment Framework to Primary Care: Can We Improve on Existing Measures?
Bibliographic record
Abstract
Outcome-based performance measurement and prospective payment are common features of the current managed care environment. Increasingly, primary care clinicians and health care organizations are being asked to assume financial risk for enrolled patients based on negotiated capitation rates. Therefore, the need for methods to account for differences in risk among patients enrolled in primary care organizations has become critical. Although current risk-adjustment measures represent significant advances in the measurement of morbidity in primary care populations, they may not adequately capture all the dimensions of patient risk relevant to primary care. We propose a risk-adjustment framework for primary care that incorporates clinical features related to patients' health status and nonclinical factors related to patients' health behaviors, psychosocial factors, and social environment. Without this broad perspective, clinicians with more unhealthy and more challenging populations are at risk of being inadequately compensated and inequitably compared with peers. The risk-adjustment framework should also be of use to health care organizations that have been mandated to deliver high-quality primary care but are lacking the necessary tools.
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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 | 0.135 | 0.281 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".