Digital encounter decision aids linked to clinical practice guidelines: results from user testing SHARE-IT decision aids in primary care
Bibliographic record
Abstract
BACKGROUND: Encounter decision aids (EDAs) are tools that can support shared decision making (SDM), up to the clinical encounter. However, adoption of these tools has been limited, as they are hard to produce, to keep up-to-date, and are not available for many decisions. The MAGIC Evidence Ecosystem Foundation has created a new generation of decision aids that are generically produced along digitally structured guidelines and evidence summaries, in an electronic authoring and publication platform (MAGICapp). We explored general practitioners' (GPs) and patients' experiences with five selected decision aids linked to BMJ Rapid Recommendations in primary care. METHODS: We applied a qualitative user testing design to evaluate user experiences for both GPs and patients. We translated five EDAs relevant to primary care, and observed the clinical encounters of 11 GPs when they used the EDA with their patients. We conducted a semi-structured interview with each patient after the consultation and a think-aloud interview with each GPs after multiple consultations. We used the Qualitative Analysis Guide (QUAGOL) for data analysis. RESULTS: Direct observations and user testing analysis of 31 clinical encounters showed an overall positive experience. The EDAs created better involvement in decision making and resulted in meaningful insights for patients and clinicians. The design and its interactive, multilayered structure made the tool enjoyable and well-organized. Difficult terminology, scales and numbers hindered understanding of certain information, which was sometimes perceived as too specialized or even intimidating. GPs thought the EDA was not suitable for every patient. They perceived a learning curve was required and the need for time investment was a concern. The EDAs were considered trustworthy as they were provided by a credible source. CONCLUSIONS: This study showed that EDAs can be useful tools in primary care by supporting actual shared decision making and enhancing patient involvement. The graphical approach and clear representation help patients better understand their options. To overcome barriers such as health literacy and GPs attitudes, effort is still needed to make the EDAs as accessible, intuitive and inclusive as possible through use of plain language, uniform design, rapid access and training. TRIAL REGISTRATION: The study protocol was approved by the The Research Ethics Committee UZ/KU Leuven (Belgium) on 31-10-2019 with reference number MP011977.
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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 | 0.007 | 0.265 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".