Virtual Patient Encounters
To support communication and information-gathering skills that can’t be learned through multiple-choice questions alone, Aquifer has introduced AI-enabled Virtual Patient Encounters (VPEs) across core clinical cases.
In a VPE, learners conduct a dynamic interview with a simulated patient, deciding how to open the visit, what questions to ask, and how to adapt based on patient responses. The experience mirrors real practice in a safe, low-stakes environment that allows learners to practice and refine their approach.
After each encounter, students receive immediate, personalized feedback mapped to established clinical performance criteria, including history-taking, question focus, and diagnostic reasoning. Student feedback shows high engagement and strong perceived value for building confidence and readiness for real patient encounters.
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Summary Statement Feedback
Summary statements offer a clear window into learners’ clinical reasoning, revealing how they synthesize information, prioritize problems, and frame a differential diagnosis. Yet in busy clinical settings, learners often receive little timely or specific feedback on this essential skill.
Within Aquifer’s virtual patient cases, students write a brief summary statement that is immediately analyzed by AI acting as a digital clinical preceptor. In seconds, learners receive structured, actionable feedback—guidance that would typically take an educator 15–20 minutes per case.
This feedback is driven by expert-validated rubrics, comparison to exemplar summaries, and a preceptor-style focus on what matters most clinically. Since launching in 2023, the tool has delivered over a million feedback interactions, with students and educators valuing its clarity, consistency, and alignment with real clinical expectations.
