Uncompensated Care Decreased At Hospitals In Medicaid Expansion States But Not At Hospitals In Nonexpansion States
Bibliographic record
Abstract
One pillar of the Affordable Care Act (ACA) was its expected impact on the growing burden of uncompensated care costs for the uninsured at hospitals. However, little is known about how this burden changed as a result of the ACA's enactment. We examine how the Affordable Care Act (ACA)'s coverage expansions affected uncompensated care costs at a large, diverse sample of hospitals. We estimate that in states that expanded Medicaid under the ACA, uncompensated care costs decreased from 4.1 percentage points to 3.1 percentage points of operating costs. The reductions in Medicaid expansion states were larger at hospitals that had higher pre-ACA uncompensated care burdens and in markets where we predicted larger gains in coverage through expanded eligibility for Medicaid. Our estimates suggest that uncompensated care costs would have decreased from 5.7 percentage points to 4.0 percentage points of operating costs in nonexpansion states if they had expanded Medicaid. Thus, while the ACA decreased the variation in uncompensated care costs across hospitals within Medicaid expansion states, the difference between expansion and nonexpansion states increased substantially. Policy makers and researchers should consider how the shifting uncompensated care burden affects other hospital decisions as well as the distribution of supplemental public funding to hospitals.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".