Assessing Past, Present, and Future Interactions with Virtual Patients

Assessing Past, Present, and Future Interactions with Virtual Patients

Richard E. Ferdig, Katherine Schottke, Diego Rivera-Gutierrez, Benjamin Lok
DOI: 10.4018/jgcms.2012070102
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Virtual patients have proven to be an effective educational tool for learning and applying clinical examination skills. Interactive virtual patient scenarios provide opportunities for medical students to practice and improve verbal and nonverbal communication through the use of performance feedback. This feedback helps students to understand the ways in which they are perceived by their patients which otherwise could not be analyzed by health professionals. Evidence supports that interactive VPs fill a niche in medical education and testing for scenarios that cannot be practiced outside the virtual environment or with standardized patients. Not only are virtual patients effective in medical curriculum, as evidenced by various studies, they are applicable in understanding the ways in which learning occurs and can be implemented into a number of educational settings. In this article, the authors summarize seven years of findings on the use of virtual patients. They also describe current efforts at implementing virtual patients in community scenarios. The paper concludes with avenues for future directions with virtual human patients.
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Brief Description Of The Technology

Prior to describing the results, we begin with a description of the technology used in our virtual patient work. We define virtual patients as embodied conversational agents – computer-generated virtual characters that are controlled by a computer algorithm with whom users can conduct a conversation. This conversation is modeled as a knowledge-base of possible answers the virtual patient can give to input from the user. Input from the user can be in the form of questions or gestures.

Users may type, speak or choose from a list their questions to the virtual patient. These questions are processed by the system using a keyword-based algorithm. This algorithm chooses the best answer from the knowledge-base of the virtual patient. The virtual patient then speaks the answer using prerecorded audio.

Users may also interact with the virtual patient using gestures to perform physical examinations on the virtual patient. These user gestures can be recognized by the system using: infrared cameras tracking retro-reflective markers, a Nintendo Wiimote, a Microsoft Kinect, metaphors using keyboard and mouse, or customized sensors that resemble body parts of the virtual patient.

Depending on the requirements of each training scenario, virtual patients can be displayed as life-size on a wall using a projector or on a TV, or in smaller sizes using desktop monitors or laptop displays (see Figure 1).

Figure 1.

Images of ways in which to interact with virtual patients


Review Of Existing Findings

Historical perspectives on technological advancements are not always useful as newer technologies often replace prior conditions, outcomes, problems, and solutions. However, this review documents important findings that drive current and future planning; they set the stage for others interested in a longitudinal perspective not just on the project, but also how to build on our successes, shortcomings, and learnings.

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