Examining the Utility of 30‐day Readmission Rates and Hospital Profiling in the Veterans Health Administration
Bibliographic record
Abstract
BACKGROUND: The Veterans Health Administration (VA) reports hospital-specific 30-day risk-standardized readmission rates (RSRRs) using CMS-derived models. OBJECTIVE: The aim of this study was to examine and describe the interfacility variability of 30-day RSRRs for acute myocardial infarction (AMI), heart failure (HF), and pneumonia as a means to assess its utility for VA quality improvement and hospital comparison. RESEARCH DESIGN: A retrospective analysis of VA and Medicare claims data using one-year (2012) and three-year (2010-2012) data given their use for quality improvement or for hospital comparison, respectively. SUBJECTS: This study included 3,571 patients hospitalized for AMI at 56 hospitals, 10,609 patients hospitalized for HF at 102 hospitals, and 10,191 patients hospitalized for pneumonia at 106 hospitals. MEASURES: Hospital-specific 30-day RSRRs for AMI, HF, and pneumonia hospitalizations were calculated using hierarchical generalized linear models. RESULTS: Of 164 qualifying VA hospitals, 56 (34%), 102 (62%), and 106 (64%) qualified for analysis based on CMS criteria for AMI, HF, and pneumonia cohorts, respectively. Using 2012 data, we found that two hospitals (2%) had CHF RSRRs worse than the national average (+95% CI), whereas no hospital demonstrated worse-than-average risk-stratified readmission Rate (RSRR; +95% CI) for AMI or pneumonia. After increasing the number of facility admissions by combining three years of data, we found that four (range: 3.5%-5.3%) hospitals had RSRRs worse than the national average (+95% CI) for all three conditions. CONCLUSIONS: The Centers for Medicare and Medicaid Services-derived 30-day readmission measure may not be a useful measure to distinguish VA interfacility performance or drive quality improvement given the low facility-level volume of such readmissions.
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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 | 0.002 | 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 | 0.000 |
| 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".