EVALUATING CMS PAYMENT REFORM INITIATIVE TO REDUCE AVOIDABLE HOSPITALIZATIONS AMONG NURSING FACILITY RESIDENTS
Bibliographic record
Abstract
Nursing home residents are characterized by frailty, multiple chronic illnesses, and high levels of physical and cognitive impairment. More than one-quarter of long-stay nursing home residents are hospitalized each year. These hospitalizations are costly, and many are considered potentially avoidable. Unnecessary hospitalizations cause disruption to residents, risk of complications, and possibility of reduced functioning on return to the nursing home. Reducing avoidable hospitalizations of nursing home residents is an important quality-improvement initiative that may also reduce cost of care. This symposium will include an overview of the Centers for Medicare & Medicaid Services (CMS) Initiative to Reduce Avoidable Hospitalizations among Nursing Facility Residents—Payment Reform (henceforth, the Initiative), and will describe the evaluation design and early findings. The Initiative, begun in 2016, tests a new Medicare Part B payment model that pays participating nursing facilities and practitioners for providing higher-level care on site to eligible long-stay residents instead of transferring them to hospitals. These payments are for care of residents with six qualifying conditions whose changing symptoms could possibly trigger a hospital transfer. Four presentations will focus on: (1) Description of the Initiative and individual state models, (2) An overview of evaluation methods, describing the approach to comparison group selection and quantitative impact analysis; (3) Early primary data (telephone interview) results from the perspective of participating nursing facilities; and (4) Preliminary primary data results concerning participating practitioners (physicians and physician extenders).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.067 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".