Bibliographic record
Abstract
In recent years, the U.S. health care industry has experienced a rapid growth of managed care, formation of networks, and an integration of hospitals. This paper provides new insights about the quality consequences of this dynamic in U.S. hospital markets. I empirically investigate the impact of managed care and hospital competition on quality using in-hospital complications as quality measures. I use random and fixed effects, and instrumental variable fixed effect models using hospital panel data from up to 16 States in the 1992-1997 period. The paper has two important findings: First, higher managed care penetration increases the quality, when inappropriate utilization, wound infections and adverse/iatrogenic complications are used as quality indicators. For other complication categories, coefficient estimates are statistically insignificant. These findings do not support the straightforward view that increases in managed care penetration are associated with decreases in quality. Second, both higher hospital market share and market concentration are associated with lower quality of care. Hospital mergers have undesirable quality consequences. Appropriate antitrust policies towards mergers should consider not only price and cost but also quality impacts.
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".