Nursing Home Compare Star Rankings and the Variation in Potentially Preventable Emergency Department Visits and Hospital Admissions
Bibliographic record
Abstract
Measurement of the quality of US health care increasingly emphasizes clinical outcomes over clinical processes. Nursing Home Compare Star Ratings are provided by Medicare to help select better nursing home care. The authors determined the rates and types of 2 important clinical outcomes-potentially preventable hospital admissions and potentially preventable emergency department (ED) visits-for a subset of 439,011 long-term nursing homes residents residing in 12,883 nursing homes throughout the United States over a 2-year period (2010-2011) and compared them with the Star Rating system. This study found that (1) the likelihood of potentially preventable events increases with increasing burden of chronic illness, (2) the principle reasons for hospital admissions and ED visits (eg, septicemia, pneumonia, confusion, gastroenteritis) are not part of existing nursing home quality measures, (3) the rate of potentially preventable admissions and ED visits for nursing homes residents varies greatly both across and within states, with 5 states having in excess of 20% more than the national average for both, and (4) the Nursing Home Compare Stars measure has limited correlation with rates of these potentially preventable events. Nursing Home Compare Star rankings could benefit by incorporating outcomes measures such as preventable hospitalizations and ED visits, and by comparing nursing home performance on results drawn from across states rather than within them. Such reform could better help users find nursing homes of higher quality and stimulate homes to improve quality in ways that benefit residents.
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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 | 0.011 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".