Design and implementation of a decision aid for juvenile idiopathic arthritis medication choices
Bibliographic record
Abstract
BACKGROUND: Randomized trials have demonstrated the efficacy of patient decision aids to facilitate shared decision making in clinical situations with multiple medically reasonable options for treatment. However, little is known about how best to implement these tools into routine clinical practice. In addition, reliable implementation of decision aids has been elusive and spread within pediatrics has been slow. We sought to develop and reliably implement a decision aid for treatment of children with juvenile idiopathic arthritis. METHODS: To design our decision aid, we partnered with patient, parent, and clinician stakeholders from the Pediatric Rheumatology Care and Outcomes Improvement Network. Six sites volunteered to use quality improvement methods to implement the decision aid. Four of these sites collected parent surveys following visits to assess outcomes. Parents reported on clinician use of the decision aid and the amount of shared decision making and uncertainty they experienced. We used chi-square tests to compare eligible visits with and without use of the decision aid on the experience of shared decision making and uncertainty. RESULTS: After 18 rounds of testing and revision, stakeholders approved the decision aid design for regular use. Qualitative feedback from end-users was positive. During the implementation project, the decision aid was used in 35% of visits where starting or switching medication was discussed. Clinicians used the decision aid as intended in 68% of these visits. The vast majority of parents reported high levels of shared decision making following visits with (64/76 = 84%) and without (80/95 = 84%) use of the decision aid (p = 1). Similarly, the vast majority of parents reported no uncertainty following visits with (74/76 = 97%) and without (91/95 = 96%) use of the decision aid (p = 0.58). CONCLUSIONS: Although user acceptability of the decision aid was high, reliable implementation in routine clinical care proved challenging. Our parsimonious approach to outcome assessment failed to detect a difference between visits with and without use of our aid. Innovative approaches are needed to facilitate use of decision aids and the assessment of outcomes.
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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 | 0.066 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".