Are Patient Decision Aids Used in Clinical Practice after Rigorous Evaluation? A Survey of Trial Authors
Bibliographic record
Abstract
<b>Background.</b> Patient decision aids (PtDAs) are effective interventions to support patient involvement in health care decisions, but there is little use in practice. Our study aimed to determine subsequent PtDA use in clinical practice following published randomized controlled trials. <b>Design.</b> A descriptive study using an email-embedded questionnaire survey targeting authors of 133 trials included in Cochrane Reviews of PtDAs (106 authors). We classified PtDA level of use as a) implementation, defined as integrating within care processes; b) dissemination to target users with planned strategies; and c) diffusion, defined as passive, unplanned spread. We conducted content analysis to identify barriers and enablers guided by the Ottawa Model of Research Use. <b>Results.</b> Ninety-eight authors responded (92.5%) on 108 trialed PtDAs. Reported levels of use were implementation (<i>n</i> = 29; 28%), dissemination to target user(s) (<i>n</i> = 9; 9%), and diffusion (<i>n</i> = 7; 7%); 57 (55%) reported no uptake, and 1 had no response (1%). Barriers to use in clinical practice were identified at the level of researchers (e.g., lack of posttrial plan), PtDAs (e.g., outdated, delivery mechanism), clinicians (e.g., disagreed with PtDA use), and practice environment (e.g., infrastructure support; funding). Enablers were online delivery, organizational endorsement (e.g., professional organization, charity, government), and design for and integration into the care process. <b>Limitations.</b> Self-report bias and potential for recall bias. <b>Conclusions.</b> Only 44% of PtDA trial authors indicated some level of subsequent use following their trial. The most commonly reported barriers were lack of funding, outdated PtDAs, and clinician disagreement with PtDA use. To improve subsequent use, researchers should codesign PtDAs with end users to ensure fit with clinical practice and develop an implementation plan. National systems (e.g., platforms, endorsement, funding) can enable use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.078 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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; both teacher heads agree on what is shown here.
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".