A Single-Centre Experience with Assisted Home Hemodialysis in Long-Term Care Facilities: A Cost-Feasibility Study
Bibliographic record
Abstract
Background: For hemodialysis (HD) recipients residing in long-term care (LTC), the COVID-19 pandemic created several barriers to care. To address these challenges, we established a pilot program of fully-assisted HD provided on-site to LTC residents (LTC-HD) by registered nurses (RNs), registered practical nurses (RPNs) and personal support workers (PSWs). Methods: We performed a cost-feasibility analysis from the provider perspective using a bottom-up micro-costing approach based on real costs incurred between March 2020-March 2023. We examined a range of staffing models (in-sourced vs. out-sourced/agency and 1:1 vs higher patient:staff ratios) for providing daily (6/week, 2hrs) and conventional (3/week, 4hrs) HD. Direct costs included labor, medical supplies, and dialysis consumables using standard (Fresenius 4008K) and portable (NxStage) equipment. Indirect costs included equipment maintenance, injectables, travel, and staff replacement costs. We excluded capital, patient-borne, non-dialysis costs, and physician fees. Costs are reported in CAD/year using FY2022/23 prices. Results: During follow-up, 44 patients received LTH-HD at 15 facilities. Bundled rates were $50,076 and $83,467 for conventional and daily HD, respectively. Conventional HD with PSWs (1:1) yielded a net loss of $2,947 vs. net surplus of $3,076 with out- vs. in-sourcing, respectively. Staffing with in- and out-sourced RNs and RPNs yielded net losses with 1:1 staffing but generated surpluses of $17,426 and $20,546 when insourced RPNs and RNs treated provided 2:1 and 3:1 clustered HD. Daily HD with NxStage cost $13,681/yr more vs. standard equipment resulting in net losses in all scenarios. Daily HD yielded a surplus when staffed by in-sourced staff with further savings under clustered models. Conclusion: Fully-assisted LTC-HD is financially feasible under current bundled rates in Ontario, with greater savings in clustered settings with in-sourced staff.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".