An Asthma Action Plan Created by Physician, Educator and Patient Online Collaboration with Usability and Visual Design Optimization
Bibliographic record
Abstract
BACKGROUND: Asthma action plans (AAPs), which decrease hospitalizations and improve symptom control, are recommended in guidelines, but are seldom delivered to patients. Existing AAPs have been developed by experts, without the inclusion of all stakeholders (such as patients with asthma) and without specifically addressing usability and visual design. OBJECTIVE: Our objective was to develop a more usable AAP by involving all stakeholders and considering design preferences. METHODS: We created a Wiki-based system for multiuser AAP development. Pulmonologists, primary care physicians, asthma educators and patients used the system to collaboratively compile a single AAP by making multiple online selections over 1 week. We combined common elements from 3 AAPs developed in this way into 1, optimized visual design features and tested face validity in focus groups. RESULTS: A total of 41 participants averaged 646 selections/week over a login-time of 28.8 h/week. Of 35 participants, 28 (80%) were satisfied with the final AAP and 32 (91%) perceived that they would be able to use it. The plans created by the 3 groups were very similar, with a unanimous or majority agreement in the handling of 100/110 (91%) AAP options. CONCLUSIONS: Inclusion of multiple stakeholders and focus on design preferences predict enhanced usability and uptake of medical tools. The validity of our AAP is further supported by the similarity between the AAPs created by each group, user engagement and satisfaction with the plan and agreement with existing validity criteria proposed by experts. This AAP can be implemented in care with a concurrent measurement of uptake and health impact.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".