A systematic review of case-mix models for home health care payment: Making sense of variation
Bibliographic record
Abstract
BACKGROUND: Case-mix based payment of health care services offers potential to contain expenditure growth and simultaneously support needs-based care provision. However, limited evidence exists on its application in home health care (HHC). Therefore, this study aimed to synthesize available international literature on existing case-mix models for HHC payment. METHODS: We performed a systematic review of scientific literature, supplemented with grey literature. We searched for literature using six scientific databases, reference lists, expert consultation, and targeted websites. Data on study design, case-mix model attributes, and conclusions were extracted narratively. RESULTS: Of 3303 references found, 22 scientific studies and 27 grey documents met eligibility criteria. Eight case-mix models for HHC were identified, from the US, Canada, New Zealand, Australia, and Germany. Three countries have implemented a case-mix model as part of a HHC payment system. Different combinations of in total 127 unique case-mix predictors are included across models to predict HHC use. Case-mix models also differ in targeted services, operationalization, and outcome measures and predictive power. CONCLUSIONS: Case-mix based payment is not yet widely used within HHC. Multiple varieties were found between HHC case-mix models, and no one best form of a model seems to exist. Even though varieties are partly inevitable due to country-specific contexts, developing a shared vision in case-mix model attributes would be key to achieving efficient, needs-based HHC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".