Beyond pros and cons – developing a patient decision aid to cultivate dialog to build relationships: insights from a qualitative study and decision aid development
Bibliographic record
Abstract
BACKGROUND: An individualized approach using shared decision-making (SDM) and goal setting is a person-centred strategy that may facilitate prioritization of treatment options. SDM has not been adopted extensively in clinical practice. An interprofessional approach to SDM with tools to facilitate patient participation may overcome barriers to SDM use. The aim was to explore decision-making experiences of health professionals and people with diabetes (PwD), then develop an intervention to facilitate interprofessional shared decision-making (IP-SDM) and goal-setting. METHODS: This was a multi-phased study. 1) Feasibility: Using a descriptive qualitative study, individual interviews with primary care physicians, nurses, dietitians, pharmacists, and PwD were conducted. The interviews explored their experiences with SDM and priority-setting, including facilitators and barriers, relevance of a decision aid for priority-setting, and integration of SDM and a decision aid into practice. 2) Development: An evidence-based SDM toolkit was developed, consisting of an online decision aid, MyDiabetesPlan, and implementation tools. MyDiabetesPlan was reviewed by content experts for accuracy and comprehensiveness. Usability assessment was done with 3) heuristic evaluation and 4) user testing, followed by 5) refinement. RESULTS: Seven PwD and 10 clinicians participated in the interviews. From interviews with PwD, we identified that: (1) approaches to decision-making were diverse and dynamic; (2) a trusting relationship with the clinician and dialog were critical precursors to SDM; and, (3) goal-setting was a dynamic process. From clinicians, we found: (1) complementary (holistic and disease specific) approaches to the complex patient were used; (2) patient-provider agendas for goal-setting were often conflicting; (3) a flexible approach to decision-making was needed; and, (4) conflict could be resolved through SDM. Following usability assessment, we redesigned MyDiabetesPlan to consist of data collection and recommendation stages. Findings were used to finalize a multi-component toolkit and implementation strategy, consisting of MyDiabetesPlan, instructional card and videos, and orientation meetings with participating patients and clinicians. CONCLUSIONS: A decision aid can provide information, facilitate clinician-patient dialog and strengthen the therapeutic relationship. Implementation of the decision aid can fit into a model of team care that respects and exemplifies professional identity, and can facilitate intra-team communication. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT02379078. Date of Registration: 11 February 2015.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".