This shows a full multivariant decision. It illustrates:
This example illustrates how to use a shared decision-making approach to identify patient preferences for various treatment options.
This decision example navigates a challenging misconception.
Patient re-evaluation after initial treatment. This brief interaction sets up next treatment decision.
One virtual physician is at the level of the learner while the second one is an MS expert. After each decision, the expert provides coaching to both the virtual physician and the participant.
This creates a 3-way conversation that immerses the learner in virtual practice with coaching.
This scene sets up a difficult shared decision-making situation around adherence.
Example of discovering the optimal treatment and coaching on how to manage the patient with that option.
This type of decision allows participants to see how various approaches play out to recognize an optimal approach. It’s a good fit for role playing and experiencing “what great looks like” compared to alternatives. Here, various approaches to shared decision-making are contemplated.
This one is a bit long but shows a participant’s experience.
This example shows how we can change up a clinical situation to improve pattern recognition on how to individualize treatment for each patient.
It also illustrates coaching on each decision, personalizing the feedback based on learner’s choices.
Discovering patient preferences in advance of a patient management decision.
In this example, a newly approved option was not a good fit for patient, but the coaching enabled the learner to understand where to use it (later in the simulation).
This shows an example a complex presentation that sets up clinical management decisions.
Participant is asked to decide what an expert sleep specialist would do in the situation. The other virtual physician is at the level of the participant, and asks questions to draw out the clinical nuances.
Example of a follow up visit for a patient with HIV. Their new symptoms and lab results will require an adjustment in the treatment strategy.
This illustrates how AliveSim can simulate patient management over time.
Review of sleep study results with child and parent before deciding on next steps in clinical management.
Example of expert clinical feedback on each decision in managing a patient with relapsed/refractory chronic lymphocytic leukemia.
Syandus: Transform your challenging situations into interactive scenarios where learners and virtual characters engage in structured practice to make decisions the way top performers do. Our AI-enhanced platform (AliveSim) creates natural conversations and decision points through game technology and proven learning design, consistently delivering measurable performance improvements for leading organizations. Trusted by healthcare and enterprise customers, and accessible anywhere.
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