Hospital Medicare Spending per Beneficiary: A Longitudinal Study
Bibliographic record
Abstract
GOAL: While studies have examined quality and health outcomes related to the Centers for Medicare & Medicaid Services' (CMS's) Hospital Value-Based Purchasing (HVBP) Program, a significant gap exists in the literature regarding the relationship between pay-for-performance initiatives and hospital financial performance in the program's Efficiency and Cost Reduction domain. This study examined the association between hospitals' cost inefficiency and participation in the HVBP Program by estimating the probability and magnitude of improvement or achievement in the program's Efficiency and Cost Reduction domain. METHODS: The 2014-2019 Efficiency and Cost Reduction domain data were obtained from CMS and merged with the American Hospital Association's Annual Survey Database. We conducted a zero-inflated negative binomial regression to account for the excessive number of zeros in the data. PRINCIPAL FINDINGS: The negative binomial component of the model assessed the magnitude of the impact on the Efficiency and Cost Reduction improvement from each covariate, while the zero-inflated component assessed the odds of being in the "certain-zero" group, meaning no chance to improve or achieve. Hospital ownership, location, size, safety-net status, percentage of Medicare patients, and the number of registered nurses per bed were statistically significant. Additionally, the Herfindahl-Hirschman Index and teaching status significantly influenced efficiency performance. PRACTICAL APPLICATIONS: Changes in hospital performance in this domain exist and have evolved. Hospitals might be at a disadvantage with this performance measure because of their inherent organizational structure. The HVBP Program may not provide clear enough direction or actionable incentive to address the needs of stakeholders influenced primarily by measures of Medicare spending per beneficiary. This study's findings hold practical value for policymakers, healthcare administrators, and researchers. Policymakers can use this information to tailor future pay-for-performance programs and effectively allocate resources. Healthcare administrators can identify areas for improvement and benchmark their performance against similar institutions. Researchers can explore the program's long-term sustainability and investigate cost drivers within different hospital groups. By understanding the link between hospital characteristics and cost reduction, all stakeholders can contribute to a more efficient healthcare system.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".