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 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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".