Human Factors Testing of the Quanta Dialysis System for Self-Care Hemodialysis in the United States
Bibliographic record
Abstract
Background: Most hemodialysis (HD) treatments are delivered by healthcare professionals within a facility (in-center HD) but HD is increasingly performed at home via self-care. Uptake of home hemodialysis (HHD) in the United States (US) remains low despite potential improvements in quality of life, health outcomes and cost savings compared to in-center HD. This may be because patients fear they are unable to manage complex treatments at home. With this in mind, the Quanta TM Dialysis System (Quanta Dialysis System) was designed to be more user-friendly. Human factors testing demonstrated the usability of the Quanta Dialysis System in the United Kingdom but has yet to be validated in a US cohort. Using a human factors testing process, we sought to assess how two lay user groups, patients with kidney failure and caregivers, use the Quanta Dialysis System within a representative, simulated use environment in the US. Methods: We recruited patients with kidney failure and caregivers from a dialysis center in California between June and November 2022. Adults who were a patient on dialysis, or a caregiver for a patient on dialysis, within the last 4 years (including the present) were eligible. Participants trained on the Quanta Dialysis System for 4-5 sessions with a Qualified Nurse Trainer followed by a competency sign off session. After a 48-hour decay period, those deemed competent proceeded to 2 test sessions to perform tasks independently. During testing, independent human factors moderators scored patient performance on 111 critical tasks required to effectively set up, operate, and shut down the Quanta Dialysis System. We assessed the number of participants who passed each task. Results: A total of 31 individuals (16 patients, 15 caregivers) participated. The mean age was 49 years and 16 (51.6%) participants were female. Of the 16 patients, 9 (56.3%) were from in-center HD. There were 7,200 tasks were tested across all participants. Of these, 96.4% were completed without difficulty, error or assistance. Conclusions: The Quanta Dialysis System was easy to use after a small number of training sessions and a 48-hour decay period. These results indicate the Quanta Dialysis System has demonstrated a high level of usability in the US and may help improve HHD penetration by using a state of the art, compact, easy to use device. Funding: Commercial Support - Quanta Dialysis Technologies
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".