Human-Centred Design & Development of a Shared Decision Aid for Patients with Chronic Kidney Disease Facing Treatment for Coronary Heart Disease
Bibliographic record
Abstract
Background: Coronary artery disease (CAD) commonly accompanies chronic kidney disease (CKD) and carries unique management considerations for people with CKD. Shared decision-making (SDM) is a collaborative approach in which patients and physicians make decisions together based on a shared understanding of the health condition, treatment options and attributes, patient values and preferences, and risk tolerance. Our objective was to support SDM by creating a decision aid for patients with CKD and physicians addressing invasive vs conservative CAD treatment options, which included personalized risk estimates for treatment option attributes, and identification of patient values and preferences. Methods: decision aid. A concurrent mixed-methods study involved patients and physicians evaluating content, features, implementation contexts, and guided design. Survey data analysis used descriptive statistics, and interview transcripts were analyzed using deductive content analysis. Results: Thirty-two patients (47% aged < 65 years; 47% women) and 18 physicians (72% aged < 50 years; 22% women) evaluated successive decision-aid iterations, providing design and implementation perspectives. Most received decision-aid content positively, and the design was refined over 3 development iterations. Overarching development-informing themes were as follows: (i) facilitating patient-physician interactions and knowledge-sharing to enable SDM; (ii) responding to contextual end-user needs for decision-making; and (iii) supporting flexible workflow use and integration. The decision aid is available at: https://myheartandckd.ca. Conclusions: Human-centred design processes effectively guided creation of a decision aid for patients with CKD and physicians making shared CAD treatment decisions. Findings will inform future clinical implementation strategies.
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 | 0.049 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".