Inclusion of Nursing Homes and Long-term Residents in Medicare ACOs
Bibliographic record
Abstract
BACKGROUND: Long-term nursing home residents have complex needs that often require services from acute care settings. The accountable care organization (ACO) model provides an opportunity to improve care by creating payment incentives for more coordinated, higher quality care. OBJECTIVES: To assess the extent of nursing home participation in ACOs, and the characteristics of residents and their nursing homes connected to ACOs. RESEARCH DESIGN: This was a cross-sectional study. SUBJECTS: Medicare nursing home residents identified from 2014 Minimum Data Set assessments. Residents were attributed to ACOs based on Medicare methods. MEASURES: Individuals' demographics, clinical characteristics, health care utilization, and nursing home characteristics. RESULTS: Among 660,780 nursing home residents, a quarter of them were attributed to ACOs. ACO residents had only small differences from non-ACO residents: age 85 years and older (47.1% vs. 45.3%), % black (10.5% vs. 12.7%), % dual eligible (74.3% vs. 75.8%), and emergency department visits (55.1 vs. 57.3 per 100). Of the 14,868 nursing homes with study residents, few were ACO providers (N=222, 1.6% of total residents) yet many had at least one ACO resident (N=8077, 76.4% of total residents); one-fifth had at least 20 (N=2839, 33.4% of total residents). ACO-provider homes were more likely than other homes to have a 5-star rating, be hospital-based and have Medicare as the primary payer. CONCLUSIONS: With a quarter of long-term nursing home residents attributed to an ACO, and one-fifth of nursing homes caring for a large number of ACO residents, outcomes and spending in this setting are important for ACOs to consider when designing patient care strategies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".