New activity-based funding model for Australian private sector overnight rehabilitation cases: the rehabilitation Australian National Sub-Acute and Non-Acute Patient (AN-SNAP) model
Bibliographic record
Abstract
Traditional overnight rehabilitation payment models in the private sector are not based on a rigorous classification system and vary greatly between contracts with no consideration of patient complexity. The payment rates are not based on relative cost and the length-of-stay (LOS) point at which a reduced rate applies (step downs) varies markedly. The rehabilitation Australian National Sub-Acute and Non-Acute Patient (AN-SNAP) model (RAM), which has been in place for over 2 years in some private hospitals, bases payment on a rigorous classification system, relative cost and industry LOS. RAM is in the process of being rolled out more widely. This paper compares and contrasts RAM with traditional overnight rehabilitation payment models. It considers the advantages of RAM for hospitals and Australian Health Service Alliance. It also considers payment model changes in the context of maintaining industry consistency with Electronic Claims Lodgement and Information Processing System Environment (ECLIPSE) and health reform generally. What is known about this topic? The Australian Health Service Alliance is unaware of any recent studies comparing and contrasting current Australian private sector rehabilitation models with AN-SNAP-based models. What does this paper add? This paper outlines the advantages of an AN-SNAP payment model with regard to paying for services in relation to relative cost and avoiding perverse incentives in relation to rehabilitation patient admission and LOS. What are the implications for practitioners? Basing private sector rehabilitation payment models on AN-SNAP can address deficiencies of traditional payment models.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".