Medical Education in the Metaverse

Purpose: Advancements in technology present opportunities for innovation and the reimagination of traditional medical education pedagogy. Virtual reality (VR) allows learners to interact with highly visual inputs that simulate realistic digital representations of the real world and clinical learning environment. While VR pilot programs have been tried in various medical educational domains, the impact of VR-based learning embedded into core clinical clerkships for medical students is unknown.1,2 Approach: A virtual patient experience was embedded into 4 core clerkships—surgery (abdominal pain), OB/GYN (preeclampsia/eclampsia), emergency medicine (chest pain), and family medicine (diabetes). Cases were designed to supplement and practice for the objective structured clinical examination (OSCE). Students were asked to obtain a clinical history, perform a physical exam, and order appropriate diagnostic and therapeutic interventions. Virtual patients responded to student questions and statements using artificial intelligence-based software. Cases were created locally by Perelman School of Medicine faculty and hosted on the PCS Spark software platform. Students received computer-generated feedback on their performance after case completion. Case evaluations consisted of 2 self-efficacy ratings (“more confident to approach [clinical scenario] in the real world,” “more prepared to manage [clinical scenario] in the real world”) using 5-point Likert scale (strongly disagree to strongly agree), 1 question assessing technical problems (5-point Likert scale with 1 = all the time and 5 = not at all), and an open-ended item. Free-text responses were categorized into strengths, weaknesses, opportunities, and threats to identify lessons learned and best practices. Usage and performance metrics were extracted from the third-party software database, and data analysis was performed using Microsoft Excel. Outcomes: Over the course of the clerkship year, a total of 491 virtual case experiences were completed over 196.4 hours, taking an average of 24 minutes per case, with students asking an average of 47 questions per case. A total of 313 self-efficacy ratings were collected. Average ratings for all cases combined increased over the course of the year in both self-efficacy domains, from 2.41 in block 1 to 3.27 in block 4 for “more confident in approach,” and from 2.42 to 3.24 for “more prepared to manage.” Similarly, distraction from technical problems also improved, with the average rating increasing from 2.33 to 3.13 from block 1 to block 4. We found a relationship between degree of technical problems and self-efficacy ratings—for students with minimal technical problems (4–5 on the Likert scale), the average self-efficacy ratings for approach and management were 3.69 and 3.65, respectively, compared to 2.32 and 2.39 for those experiencing distraction from technical problem half the time or more. Analysis of number of questions asked and score (corresponding to completion of a list of critical tasks for the case) did not demonstrate significant performance ratings differences by race or gender identity. Major themes identified via free-text comments included the value of additional practice opportunities, challenges presented by technical issues, and the need for variable case complexity and refined scoring rubrics. Significance: VR-based cases are a feasible, cost-effective, and scalable way to increase exposure to clinical encounters in the absence of faculty supervision. This may allow educators to better standardize and increase student exposure to clinical cases on core clerkship rotations. Future study is needed to understand the impact of virtual cases on student performance, clinical decision making, and knowledge acquisition

Educational technology · Instructional simulation · Likert scale · Mathematics education · Medical education · Metaverse · Psychological intervention · Virtual patient · Virtual reality · Computer Science · Innovations in Medical Education · Medicine · Nursing · Psychology · Simulation-Based Education in Healthcare · Artificial Intelligence