Voicing Individual Concerns for Engagement in Hemodialysis (VOICE-HD): A Mixed Method, Randomized Pilot Trial of Digital Health in Dialysis Care Delivery
Bibliographic record
Abstract
BACKGROUND: People receiving in-center hemodialysis (HD) have prioritized the need for more individualized health information and better communication with nephrologists. The most common setting for patient-nephrologist interactions is during the HD treatment, which is a time pressured setting that lacks privacy. OBJECTIVE: To facilitate effective communication in the hemodialysis (HD) unit, we evaluated the usability of a web application (web app) from both the patient and physician perspective. The main aim of the web app was to support patients in prioritizing their dialysis concerns outside of the clinical HD encounter. DESIGN: Mixed method, parallel arm, multi-site, pilot randomized controlled trial. SETTING: Two outpatient Canadian HD centers. PARTICIPANTS: Adult patients receiving in-center HD and their attending nephrologists. METHODS: Patients were randomized to either a web application or an active control (paper form) for logging concerns to be addressed at weekly encounters with the nephrologist over 8 weeks. Topics included: HD treatment, symptoms, modality, and medications. The primary outcome was usability, defined as effectiveness (engagement with the tool, frequency of submitted concerns, whether the concern was satisfactorily addressed) and satisfaction with the tool using a priori thresholds and explored in interviews with patients and nephrologists. RESULTS: 77 patients (30 women, median age 61, interquartile range [53,67], median 2 years [1,4] on dialysis) and 19 nephrologists (4 women, median age 46 [36,65]) were enrolled. Patient use of a digital device at baseline was low (20%). Engagement with the tool was 70% (web app) and 100% (paper) with a lower proportion of patients in the web app group submitting at least one concern over 8 weeks compared to the paper form group: 56.7% vs 87.9%. Weekly concerns were satisfactorily addressed in both groups and ≥70% of patients would continue to use the tools. For patients, both tools promoted preparation and participation in the encounter; however, only the web app facilitated greater privacy in relaying concerns. For most nephrologists, the tools were disruptive to their workflow and were perceived as unnecessary given existing processes and familiarity with patients. For future versions of the app, patients suggested more features to facilitate self-management and nephrologists suggested integration with health databases and multidisciplinary teams. LIMITATIONS: Tertiary setting may limit generalizability. CONCLUSIONS: ClinicalTrials.gov NCT03605875.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".