Learning to diagnose a virtual patient: An investigation of cognitive errors in medical problem solving

Abstract

Although cognitive errors (i.e., premature closure, faulty data gathering, and faulty knowledge) are the main reasons for making diagnostic mistakes, the mechanisms by which they occur are difficult to isolate in clinical settings. Computer-based learning environments (CBLE) offer the opportunity to train medical students to avoid cognitive errors by tracking the onset of these errors. The purpose of this study is to explore cognitive errors in a CBLE called Bio- World. A logistic regression was fitted to learner behaviors that characterize premature closure in order to predict diagnostic performance. An ANOVA was used to assess if participants who were highly confident in their wrong diagnosis engaged in more faulty data gathering via confirmation bias. Findings suggest that diagnostic mistakes can be predicted from faulty knowledge and faulty data gathering and indicate poor metacognitive awareness. This study supports the notion that to improve diagnostic performance medical education programs should promote metacognitive skills.