Is a Skilled Nursing Facility's Rehospitalization Rate a Valid Quality Measure?
Bibliographic record
Abstract
OBJECTIVE: To determine whether the observed differences in the risk-adjusted rehospitalization rates across skilled nursing facilities (SNFs) reflect true differences or merely differences in patient severity. SETTINGS: Elderly Medicare beneficiaries newly admitted to an SNF following hospitalization. STUDY DESIGN: We used 2009-2012 Medicare data to calculate SNFs' risk-adjusted rehospitalization rate. We then estimated the effect of these rehospitalization rates on the rehospitalization of incident patients in 2013, using an instrumental variable (IV) method and controlling for patient's demographic and clinical characteristics and residential zip code fixed effects. We used the number of empty beds in a patient's proximate SNFs during hospital discharge to create the IV. PRINCIPAL FINDINGS: The risk-adjusted rehospitalization rate varies widely; about one-quarter of the SNFs have a rehospitalization rate lower than 17 percent, and for one-quarter, it is higher than 23 percent. All the IV models result in a robust finding that an increase in a SNF's rehospitalization rate of 1 percentage point over the period 2009-2012 leads to an increase in a patient's likelihood of rehospitalization by 0.8 percentage points in 2013. CONCLUSIONS: Treatment in SNFs with historically low rehospitalization causally reduces a patient's likelihood of rehospitalization. Observed differences in rehospitalization rates reflect true differences and are not an artifact of selection.
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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 | 0.028 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".