Improving Shared Decision Making Between Patients and Clinicians: Design and Development of a Virtual Patient Simulation Tool
Bibliographic record
Abstract
BACKGROUND: Shared decision making (SDM) involves the formation of a collaborative partnership between the patient and clinician combining both of their expertise in order to benefit decision making. In order for clinicians to be able to carry out this skilled task, they require practice. Virtual reality, in the form of a virtual patient, could offer a potential method of facilitating this. OBJECTIVE: The objective of this study was to create a virtual patient that simulated a primary care consultation, affording the opportunity to practice SDM. A second aim was to involve patients in the design of a virtual patient simulation and report the process of the design. METHODS: We employed a multistep design process drawing on patient and expert involvement. RESULTS: A virtual patient, following a narrative style, was built, which allows a user to practice and receive feedback; both clinical and communication skills are required for the simulation. The patient group provided multiple insights, which the academic team had overlooked. They pertained mostly to issues concerning the patient experience. CONCLUSIONS: It is possible to design a virtual patient that allows a learner to practice their ability to conduct SDM. Patient input into the design of virtual patient simulations can be a worthwhile activity.
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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 | 0.002 |
| 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".