Interactive Health Technology Tool for Kidney Living Donor Assessment to Standardize the Informed Consent Process: Usability and Qualitative Content Analysis
Bibliographic record
Abstract
BACKGROUND: Kidney living donation carries risks, yet standardized information provision regarding nephrectomy risks and psychological impacts for candidates remains lacking. OBJECTIVE: This study assesses the benefit of interactive health technology in improving the informed consent process for kidney living donation. METHODS: The Kidney Hub institutional open portal offers comprehensive information on kidney disease and donation. Individuals willing to start the kidney living donation process at Helsinki University Hospital (January 2019-January 2022) were invited to use the patient-tailored digital care path (Living Donor Digital Care Path) included in the Kidney Hub. This platform provides detailed donation process information and facilitates communication between health care professionals and patients. eHealth literacy was evaluated via the eHealth Literacy Scale (eHEALS), usability with the System Usability Scale (SUS), and system utility through Likert-scale surveys with scores of 1-5. Qualitative content analysis addressed an open-ended question. RESULTS: The Kidney Hub portal received over 8000 monthly visits, including to its sections on donation benefits (n=1629 views) and impact on donors' lives (n=4850 views). Of 127 living kidney donation candidates, 7 did not use Living Donor Digital Care Path. Users' ages ranged from 20 to 79 years, and they exchanged over 3500 messages. A total of 74 living donor candidates participated in the survey. Female candidates more commonly searched the internet about kidney donation (n=79 female candidates vs n=48 male candidates; P=.04). The mean eHEALS score correlated with internet use for health decisions (r=0.45; P<.001) and its importance (r=0.40; P=.01). Participants found that the Living Donor Digital Care Path was technically satisfactory (mean SUS score 4.4, SD 0.54) and useful but not pivotal in donation decision-making. Concerns focused on postsurgery coping for donors and recipients. CONCLUSIONS: Telemedicine effectively educates living kidney donor candidates on the donation process. The Living Donor Digital Care Path serves as a valuable eHealth tool, aiding clinicians in standardizing steps toward informed consent. TRIAL REGISTRATION: ClinicalTrials.gov NCT04791670; https://clinicaltrials.gov/study/NCT04791670. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-051166.
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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 | 0.041 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".