The Revolution in Health Care Antitrust: New Methods and Provocative Implications
Bibliographic record
Abstract
CONTEXT: In recent years, federal courts have permitted hospital consolidations and other potentially anticompetitive actions by accepting hospitals' claims that they compete in expansive geographic markets. Recent events, including two actions by the U.S. Federal Trade Commission, suggest that antitrust is undergoing a sea change, thanks in part to new methods for defining geographic markets. This article reviews the recent history of hospital antitrust, describes the methods used to define markets, and illustrates the new methods by considering two consolidations recently proposed by a New York regulatory agency. METHODS: The new methods for defining geographic markets rely on estimates from conditional choice models using patient-level hospitalization data. These estimates are the raw material for computations of price effects derived from a theoretical model of hospital pricing in a managed care environment. FINDINGS: Applying these methods to two proposed consolidations in New York shows that one of the mergers would likely raise prices by a substantial amount without the promise of offsetting efficiencies but that the other would not have this effect. CONCLUSIONS: New methods for geographic market definition may fundamentally alter how courts will evaluate antitrust challenges. Although additional research is necessary to refine the predictions of these new methods, consolidating hospitals, as well as any other hospitals engaging in potentially anticompetitive conduct, can no longer anticipate a friendly reception in the courtroom.
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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 | 0.072 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.047 |
| Scholarly communication | 0.011 | 0.024 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.016 |
| 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".