Identifying Barriers to Implementing an Assisted Home Hemodialysis Program in Canada
Bibliographic record
Abstract
Background: Policy changes such as the Advancing American Kidney Health Initiative and the impact of the COVID-19 pandemic will accelerate the trend for more home dialysis. Expanding the pool of patients eligible for HHD will require health care practitioner assisted models to be developed and deployed. We hypothesize that many barriers to delivering assisted HHD (aHHD) exist and implementation of a successful program would require meaningful input from frontline home dialysis nurses. Our primary objective of this study is to survey these key stakeholders to identify these barriers. Methods: We conducted a semi-structured focus group of leaders within our large Canadian home dialysis program to anticipate key aspects of implementing aHHD, including gauging local demand, identifying eligible patients, and recognizing essential operational components. From this, we constructed questionnaires for frontline nursing staff within HHD, peritoneal dialysis (PD) and assisted PD (aPD) programs. We performed a qualitative analysis to identify common themes and implementation barriers. Results: Twenty-six responses from three sites were received. 20/21 PD nurses reported existing aPD programs expanded the eligible pool of PD patients. 5/5 HHD nurses felt an aHHD program would keep more patients on the modality and prevent technique failure. Only 2/5 felt aHHD should be offered as a transition to HHD. While 18/21 PD nurses reported they could easily identify patients for aPD, only 2/5 HHD nurses agreed. Patients with sensory deficits, functional impairments, and limited support networks were felt to benefit most from aHHD. Lack of confidence and phobias were not agreed upon. Behavioral and safety issues, clinical instability, and inability to manage emergencies may be barriers to aHHD. Machine set-up, take-down, and establishing access were thought to be essential services. PD nurses felt clinical assessments should be routine. Few nurses felt complete assistance was necessary. Conclusions: Our findings suggest there is a strong local demand for aHHD provided there is a clear criterion for enrollment and operational plans are well established. Frontline nurses have identified several important barriers to implementation which we will acknowledge and address when deploying our assisted home program over the upcoming year.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".