Effects of Skilled Nursing Facility Partnerships on Outcomes Following Total Joint Arthroplasty
Bibliographic record
Abstract
INTRODUCTION: Post-total joint arthroplasty (TJA) discharge to a skilled nursing facility (SNF) is associated with higher costs and more complications than home discharge; however, some patients still require postoperative SNF care. To improve outcomes for patients requiring postoperative SNF care, this article analyzed the effect of SNF-surgeon partnerships on TJA postoperative costs and patient outcomes. METHODS: This was a retrospective study of primary TJA patients who were part of Medicare's Comprehensive Care for Joint Replacement (CJR) pilot program at our urban, academic medical center. We identified all patients discharged to SNF and designated SNFs as "preferred" if they maintained a partnership with our surgical team. SNF costs, total 90-day postoperative costs, average length of stay in SNF, 90-day readmission rates, and readmission diagnoses were recorded. Data were compared using Student t-tests. Readmission rates and the presence of a readmission diagnosis were analyzed using z-scores. RESULTS: Our search identified 189 patients (22.9%) discharged to SNFs, with 128 (67.8%) discharged to preferred and 61 (32.2%) discharged to nonpreferred facilities. Over the 4-year CJR pilot program, SNF costs ($10,981.23 versus $7,343.34; P < 0.005) and overall postdischarge costs ($23,952.52 versus $18,339.26; P = 0.07) were higher for patients discharged to nonpreferred SNFs versus preferred SNFs. Patients discharged to nonpreferred SNFs also had increased length of stay (14.8 versus 10.1 days; P < 0.005) and increased readmission rates (19.7% versus 3.9%; P < 0.005). These differences became more pronounced across the study period. CONCLUSION: For patients undergoing primary TJA, hospital partnership with SNFs can improve CJR performance by cost reduction and overall outcomes for TJA patients.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".