Hospital value based purchasing scores highly associated with prior year score and organizational characteristics
Bibliographic record
Abstract
Objective: Medicare’s Hospital Value Based Purchasing program (HVBP) rewards hospitals which achieve a higher than mean Total Performance Score (TPS). This article investigates the relationship between hospital characteristics and prior year Total Performance Score (TPS) on current year TPS under Medicare’s Hospital Value Based Purchasing (HVBP) program.Methods: Regression analyses are used to investigate the relationship between prior year TPS and organizational characteristics on current year TPS.Results: Regression analyses show that certain geographic locations, smaller bed size, and lower disproportionate share hospital percentage (DSHPCT) lead to a significantly higher TPS in both FY 2015 and FY 2016. Teaching status is associated with higher scores in FY 2015 and lower scores in FY 2016. Furthermore, prior year TPS is a significant predictor of current year TPS.Conclusions: Results suggest HVBP performance is dependent upon organizational characteristics which may have little to do with quality or cost of care. Furthermore, the findings demonstrate that prior year HVBP performance is the strongest predictor of future performance which may impede low performing hospitals from achieving success in future years, despite significant gains in improving cost and quality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".