What Works in Implementing Patient Decision Aids in Routine Clinical Settings? A Rapid Realist Review and Update from the International Patient Decision Aid Standards Collaboration
Bibliographic record
Abstract
BACKGROUND: Decades of effectiveness research has established the benefits of using patient decision aids (PtDAs), yet broad clinical implementation has not yet occurred. Evidence to date is mainly derived from highly controlled settings; if clinicians and health care organizations are expected to embed PtDAs as a means to support person-centered care, we need to better understand what this might look like outside of a research setting. AIM: This review was conducted in response to the IPDAS Collaboration's evidence update process, which informs their published standards for PtDA quality and effectiveness. The aim was to develop context-specific program theories that explain why and how PtDAs are successfully implemented in routine healthcare settings. METHODS: Rapid realist review methodology was used to identify articles that could contribute to theory development. We engaged key experts and stakeholders to identify key sources; this was supplemented by electronic database (Medline and CINAHL), gray literature, and forward/backward search strategies. Initial theories were refined to develop realist context-mechanism-outcome configurations, and these were mapped to the Consolidated Framework for Implementation Research. RESULTS: We developed 8 refined theories, using data from 23 implementation studies (29 articles), to describe the mechanisms by which PtDAs become successfully implemented into routine clinical settings. Recommended implementation strategies derived from the program theory include 1) co-production of PtDA content and processes (or local adaptation), 2) training the entire team, 3) preparing and prompting patients to engage, 4) senior-level buy-in, and 5) measuring to improve. CONCLUSIONS: We recommend key strategies that organizations and individuals intending to embed PtDAs routinely can use as a practical guide. Further work is needed to understand the importance of context in the success of different implementation studies.
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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 | 0.178 | 0.325 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.029 | 0.022 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".