Starting Dialysis on Time, At Home on the Right Therapy (START): Cost analysis of an initiative to increase the use of peritoneal dialysis
Bibliographic record
Abstract
Home-based peritoneal dialysis (PD) is less resource-intensive than in-center hemodialysis. When provided with a choice, many patients prefer home-based therapies. The Starting Dialysis on Time, At Home on the Right Therapy (START) project was a quality improvement initiative aiming to increase PD use in Alberta, Canada. The START project provided site-specific audit and feedback reports on the processes of care for PD and increased the use of PD. In this current study, we conducted a retrospective cost analysis of the START project. We used the perspective of a publicly funded healthcare system to compare the costs before and after the START intervention. We used a decision analytic model stratifying the patient cohort by age (under and over 65 years) and estimated the impact of the START intervention on the overall cost of care at 1, 3, 5, and 10 years. Sensitivity analyses were performed. We found cost savings of $CAD 1.2 million, $CAD 1.9 million, $CAD 2.4 million, and $CAD 2.7 million for the START intervention at 1, 3, 5, and 10 years, respectively. Results were robust to a variety of sensitivity and scenario analyses. Even modest increases in PD utilization led to cost savings. We found that the implementation of a quality improvement initiative to increase PD resulted in substantial cost savings over time.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".