089 Levers and limitations of artificial intelligence (AI) to support the assessment and implementation of shared decision making (SDM): perspectives of key stakeholders
Bibliographic record
Abstract
Introduction Artificial intelligence (AI) is a promising avenue to advance the assessment and implementation of SDM. We explored stakeholder perspectives on the potential of AI to facilitate the implementation of SDM, focusing specifically on the assessment of SDM in healthcare professionals’ practice. Methods Our environmental scan combined a web-based survey and targeted semi-structured interviews with key stakeholders. Participants included AI or SDM researchers and patient partners from our networks and from post-2015 studies using observer-rated instruments. The survey explored participants’ research experience, perceptions of levers and barriers for AI to support SDM assessment and implementation, and challenges in developing an AI system for consultation assessment. We then conducted semi-structured videoconference interviews with a subset of 6–10 participants who were purposively selected. Data analysis included descriptive statistics for quantitative results and thematic qualitative analyses for qualitative results, by two researchers based on the Consolidated Framework for Implementation Research. Results (preliminary) To date, we have recruited 22 participants from seven countries - seven AI experts and 14 SDM experts, five of whom were practicing clinicians, and 10 owned observer-rated consultation datasets in different clinical settings. Levers in AI included developing decision aids or personalized information for patients to facilitate decision-making, and facilitating training in SDM through assessment in real or simulated consultations. Barriers cited included ethical concerns about privacy and confidentiality, reliability and transparency of AI systems, and reduced understanding of the complexity of human interaction. Barriers to implementation included cost, time, social acceptance of AI and data quality issues for AI training. Discussion Most participants considered the use of AI for SDM assessment feasible. Half of the dataset owners indicated that they would make data available for AI if ethical and administrative concerns were addressed. Conclusion(s) These findings will help inform future research agendas for AI-assisted SDM assessment and implementation.
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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 | 0.067 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".