Low-mortality death in hospitals after vertical integration with Health Maintenance Organizations
Bibliographic record
Abstract
Objective: Hospital mergers and acquisitions continue to rise due to government and market pressures to join Accountable Care Organizations in order to lower costs. Our study examines the changes in a quality of care measure when hospitals were acquired by Health Maintenance Organizations (HMOs) to analyze the effect that vertical integration has on coordination of care.Methods: Using California patient discharge data from 2000-2011, our analysis used differences-in-differences and logistic models to test for a change in a quality of care measure before and after hospitals merged with an HMO. We utilized Patient Safety Indicator #2, death rate in low-mortality diagnosis related groups, to measure quality of care.Results: Hospitals experienced decreases in low-mortality death rates after being acquired by an HMO. This group of hospitals had increasing measures of the quality indicator prior to the merge as well, suggesting selection of well-performing hospitals by HMOs. Hospitals acquired by HMOs also faced increased Type-2 diabetes rates post-merge.Conclusions: Our results suggest that hospitals merging with a vertically integrated health care system may lead to increases in quality of care. It appears that this could be due to either HMOs providing more coordinated care, or that HMOs acquire hospitals already trending towards better care.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".