Interventions as an alternative to penalties in preventable readmissions
Bibliographic record
Abstract
While expenditures in healthcare in the United States are the highest in the world, it is widely known that those resources are not being used efficiently. The government addressed this situation in the Patient Protection and Affordable Care Act, in an attempt to improve quality and affordability of healthcare. In the fiscal year 2013, the Centers for Medicare and Medicaid Services began imposing financial penalties through the Inpatient Prospective Payment System to hospitals that have higher than expected readmission rates for specific diseases. The nature and effects of this new policy have raised several concerns. This article discusses Medicare’s hospital readmissions reduction program and presents an alternate policy based on diseasespecific interventions to reduce preventable readmissions. Our results show that a policy based on implementing disease-specific interventions, instead of penalties, may save 33.43% of hospitals from being under the penalization level in the first year, while at the same time improving the delivery of care.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".