The Relationship between Health Plan Performance Measures and Physician Network Overlap: Implications for Measuring Plan Quality
Bibliographic record
Abstract
OBJECTIVE: To examine the extent to which health plan quality measures capture physician practice patterns rather than plan characteristics. DATA SOURCE: We gathered and merged secondary data from the following four sources: a private firm that collected information on individual physicians and their health plan affiliations, The National Committee for Quality Assurance, InterStudy, and the Dartmouth Atlas. STUDY DESIGN: We constructed two measures of physician network overlap for all health plans in our sample and linked them to selected measures of plan performance. Two linear regression models were estimated to assess the relationship between the measures of physician network overlap and the plan performance measures. PRINCIPAL FINDINGS: The results indicate that in the presence of a higher degree of provider network overlap, plan performance measures tend to converge to a lower level of quality. CONCLUSIONS: Standard health plan performance measures reflect physician practice patterns rather than plans' effort to improve quality. This implies that more provider-oriented measurement, such as would be possible with accountable care organizations or medical homes, may facilitate patient decision making and provide further incentives to improve performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".