User Involvement in the Design and Development of Patient Decision Aids and Other Personal Health Tools: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: When designing and developing patient decision aids, guidelines recommend involving patients and stakeholders. There are myriad ways to do this. We aimed to describe how such involvement occurs by synthesizing reports of patient decision aid design and development within a user-centered design framework and to provide context by synthesizing reports of user-centered design applied to other personal health tools. METHODS: We included articles describing at least one development step of 1) a patient decision aid, 2) user- or human-centered design of another personal health tool, or 3) evaluation of these. We organized data within a user-centered design framework comprising 3 elements in iterative cycles: understanding users, developing/refining prototype, and observing users. RESULTS: We included 607 articles describing 325 patient decision aid projects and 65 other personal health tool projects. Fifty percent of patient decision aid projects reported involving users in at least 1 step for understanding users, 35% in at least 1 step for developing/refining the prototype, and 84% in at least 1 step for observing users' interaction with the prototype. In comparison, other personal health tool projects reported 91%, 49%, and 92%, respectively. A total of 74% of patient decision aid projects and 92% of other personal health tool projects reported iterative processes, both with a median of 3 iterative cycles. Preliminary evaluations such as usability or feasibility testing were reported in 66% of patient decision aid projects and 89% of other personal health tool projects. CONCLUSIONS: By synthesizing design and development practices, we offer evidence-based portraits of user involvement. Those wishing to further align patient decision aid design and development with user-centered design methods could involve users earlier, design and develop iteratively, and report processes in greater detail.
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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 | 0.048 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".