User Response to the Simulation of a Virtual Patient with Cranial Nerve Injury

Abstract

Technology has rarely attempted to simulate a CN exam. NERVE simulates a life-size virtual patient (VP), using speech recognition with a Nintendo Wiimote® serving as a virtual hand, ophthalmoscope, and eye-chart. This study assesses the introductory reception, ability to identify the CN lesion, and students' preference of NERVE. Our goal is to evaluate the responses from medical students, residents, and clinicians using the Neurological Examination Rehearsal Virtual Environment (NERVE), a cranial nerve (CN) exam simulator. Medical College of Georgia participants from a variety of medical specialties, including 9 clinicians, 7 residents, and 8 MS3 and 4s, 20 MS 2s, and 25 MS 1s performed a CN examination on a VP. There were no statistically significant differences in measures related to the actual performance of the exam, the controller, overall benefit of the experience, use of technology or satisfaction with the technology. Even with technical limitations, overall medical student's reported NERVE having educational value. Residents had the lowest rate of correct CN identification, indicating they could be the group that most benefits from repeat exposure to CN exams. Medical students and clinicians were the best groups at identifying the correct deficit for our simulation. The next step is to assess NERVE's capability to teach students and residents the cranial nerve exam.

Physical-Virtual Agents for Healthcare Simulation

“…Shader Lamps based virtual patients combine a front-or back-projected human-shaped surface with a projector to provide computer-generated visual feedback to observers, thus allowing medical students to conduct ophthalmic exams in an interactive training experience [41]. For instance, NERVE simulates a life-size virtual patient with cranial nerve injury [20,30], giving medical students standardized experiences to interviewing, examining, and diagnosing virtual patients with cranial nerve disorders [17]. When medical students interacted with such virtual patients with cranial nerve injury of different gender and skin, female patients were correctly diagnosed more frequently than their male counterpart [42].…”

Network motif frequency vectors reveal evolving metabolic network organisation

“…Importantly, both the columns of W and the rows of H can be used to reveal important network features. 28,29 Note, that in all of our experiments, the factorisation in (1) was carried out using k = 3, and W, H were chosen so as to minimise the residual 25 ||S À WH|| F .…”