Can economic incentives increase the use of home dialysis?
Bibliographic record
Abstract
There are advantages to home dialysis for patients, and kidney care programs, but use remains low in most countries. Health-care policy-makers have many levers to increase use of home dialysis, one of them being economic incentives. These include how health-care funding is provided to kidney care programs and dialysis facilities; how physicians are remunerated for care of home dialysis patients; and financial incentives-or removal of disincentives-for home dialysis patients. This report is based on a comprehensive literature review summarizing the impact of economic incentives for home dialysis and a workshop that brought together an international group of policy-makers, health economists and home dialysis experts to discuss how economic incentives (or removal of economic disincentives) might be used to increase the use of home dialysis. The results of the literature review and the consensus of workshop participants were that financial incentives to dialysis facilities for home dialysis (for instance, through activity-based funding), particularly in for-profit systems, could lead to a small increase in use of home dialysis. The evidence was less clear on the impact of economic incentives for nephrologists, and participants felt this was less important than a nephrologist workforce in support of home dialysis. Workshop participants felt that patient-borne costs experienced by home dialysis patients were unjust and inequitable, though participants noted that there was no evidence that decreasing patient-borne costs would increase use of home dialysis, even among low-income patients. The use of financial incentives for home dialysis-whether directed at dialysis facilities, nephrologists or patients-is only one part of a high-performing system that seeks to increase use of home dialysis.
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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 | 0.024 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.001 |
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".