Association between the Value-Based Purchasing pay for performance program and patient mortality in US hospitals: observational study
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of the Hospital Value-Based Purchasing (HVBP) program-the US pay for performance program introduced by Medicare to incentivize higher quality care-on 30 day mortality for three incentivized conditions: acute myocardial infarction, heart failure, and pneumonia. DESIGN: Observational study. SETTING: 4267 acute care hospitals in the United States: 2919 participated in the HVBP program and 1348 were ineligible and used as controls (44 in general hospitals in Maryland and 1304 critical access hospitals across the United States). PARTICIPANTS: 2 430 618 patients admitted to US hospitals from 2008 through 2013. MAIN OUTCOME MEASURES: 30 day risk adjusted mortality for acute myocardial infarction, heart failure, and pneumonia using a patient level linear spline analysis to examine the association between the introduction of the HVBP program and 30 day mortality. Non-incentivized, medical conditions were the comparators. A secondary outcome measure was to determine whether the introduction of the HVBP program was particularly beneficial for a subgroup of hospital-poor performers at baseline-that may benefit the most. RESULTS: Mortality rates of incentivized conditions in hospitals participating in the HVBP program declined at -0.13% for each quarter during the preintervention period and -0.03% point difference for each quarter during the post-intervention period. For non-HVBP hospitals, mortality rates declined at -0.14% point difference for each quarter during the preintervention period and -0.01% point difference for each quarter during the post-intervention period. The difference in the mortality trends between the two groups was small and non-significant (difference in difference in trends -0.03% point difference for each quarter, 95% confidence interval -0.08% to 0.13% point difference, P=0.35). In no subgroups of hospitals was HVBP associated with better outcomes, including poor performers at baseline. CONCLUSIONS: Evidence that HVBP has led to lower mortality rates is lacking. Nations considering similar pay for performance programs may want to consider alternative models to achieve improved patient outcomes.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".