Supported in principle, challenges prevail: Patients and healthcare professionals' perspectives on the implementation of shared decision-making for the treatment of aortic stenosis
Bibliographic record
Abstract
Abstract Purpose In Canada, treatment decisions for severe aortic stenosis remain primarily driven by referral patterns and physicians’ recommendations, with uneven integration of patients' perspectives. There is a gap in knowledge translation to implement shared decision-making (SDM) for aortic stenosis treatment to improve patient outcomes and experiences, and help shift the culture of care from clinician-driven to patient-centred. We aimed to explore patients and healthcare professionals’ perspectives and factors influencing shared decision-making implementation in three valvular heart programs across Canada. Methods We conducted a qualitative study using an interpretive description approach. We conducted interviews and focus groups with nurse practitioners, advanced practice nurses, cardiac surgeons, cardiac interventionalists, and patients who have faced valve replacement treatment decisions across three academic hospitals in Canada. We undertook a theory-guided approach to data collection, and constant comparative analysis between transcripts. Results We conducted thirteen interviews with nurses, physicians, and patients who all, in principle, report support and engagement towards SDM. Reflective preliminary analysis of the primary interview data suggested four categories which reveal the factors that influence SDM and demonstrate the extent of SDM use in clinical practice in this context. First, established treatment clinical guidelines, in particular age-specific cutoffs, often serve as the foundation for discussions and recommended options. Second, participants noted that at initial appointments, patients are generally well informed about their options, yet gaps in their understanding of the risks, benefits, and potential outcomes of the treatment options persist and require attention. Third, participants acknowledged that an interdisciplinary approach is crucial to genuine SDM, with team members like nurse practitioners contributing to extended patient interaction and fostering a culture of collaborative decision-making. At a system’s level, recent changes in funding models have enhanced healthcare professionals' interest and motivation to engage in SDM. Conclusions While supported in principle, healthcare professionals contend with several realities that guide their treatment recommendations. The involvement of nurses was identified as critical to engaging patients in SDM, capturing their preferences and situational circumstances, and advocating for them at heart team meetings. These findings offer a multi-leveled lens through which new and innovative implementation strategies can be proposed to overcome identified barriers, which may be useful for heart valve programs wishing to deliberately build SDM-supportive teams and infrastructure that value patient-centred decision-making.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".