Evaluation of virtual patient cases for teaching diagnostic and management skills in internal medicine: a mixed methods study

Abstract Objective The virtual patient (VP) is a computer program that simulates real-life clinical scenarios and allows learners to make diagnostic and therapeutic decisions in a safe environment. Although many VP cases are available, few focus on junior trainees as their target audience. In additi...

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Bibliographic Details
Main Authors: Samira Jeimy, Jenny Yujing Wang, Lisa Richardson
Format: Article
Language:English
Published: BMC 2018-06-01
Series:BMC Research Notes
Subjects:
Online Access:http://link.springer.com/article/10.1186/s13104-018-3463-x
Description
Summary:Abstract Objective The virtual patient (VP) is a computer program that simulates real-life clinical scenarios and allows learners to make diagnostic and therapeutic decisions in a safe environment. Although many VP cases are available, few focus on junior trainees as their target audience. In addition, there is wide variability in trainees’ clinical rotation experiences, based on local practice and referral patterns, duty hour restrictions, and competing educational requirements. In order to standardize clinical exposure and improve trainees’ knowledge and perceived preparedness to manage core internal medicine cases, we developed a pool of VP cases to simulate common internal medicine presentations. We used quantitative and qualitative analyses to evaluate the effectiveness of one of our VP cases among medical trainees at University of Toronto. We also evaluated the role of VP cases in integrated teaching of non-medical expert competencies. Results Despite modest effects on knowledge acquisition, a majority of participants enjoyed using VP cases as a resource to help them prepare for and reinforce clinical experiences. Cognitive interactivity and repetitive practice were particularly appreciated by study participants. Trainees perceived VP cases as a useful resource as their learning can be customized to their actions within the case, resulting in unique learning trajectories.
ISSN:1756-0500