Search

Objectives: Concept maps are a learning tool that fosters clinical reasoning skills in healthcare education. They can be developed by students in combination with virtual patients to create a visual representation of the clinical reasoning process while solving a case. However, in order to optimize feedback, there is a need to better understand the role of connections between concepts in student-generated maps. Therefore, in this study we investigated whether the quality of these connections is indicative of diagnostic accuracy., Methods: We analyzed 40 concept maps created by fifth-year medical students in the context of four virtual patients with commonly encountered diagnoses. Half of the maps were created by students who made a correct diagnosis on the first attempt; the other half were created by students who made an error in their first diagnosis. The connections in the maps were rated by two reviewers using a relational scoring system. Analysis of covariance was employed to examine the difference in mean connection scores among groups while controlling for the number of connections., Results: There were no differences between the groups in the number of concepts or connections in the maps; however, maps made by students who made a correct first diagnosis had higher scores for the quality of connections than those created by students who made an incorrect first diagnosis (12.13 vs 9.09; p=0.03). We also observed students' general reluctance to use connections in their concept maps., Conclusion: Our results suggest that the quality, not the quantity, of connections in concept maps is indicative of their diagnostic accuracy., Competing Interests: The authors declare that they have no competing interests., (Copyright © 2023 Kononowicz et al.)

The COVID-19 pandemic posed new global challenges for teaching. We met these challenges as an international collaboration by adapting a collection of virtual patients for clinical reasoning training to this novel context., Competing Interests: Martin Adler is CEO of the Instruct gGmbH which develops the virtual patient system CASUS. The authors declare that they have no competing interests., (Copyright © 2020 Hege et al.)

Background: Clinical reasoning is a complex skill students have to acquire during their education. For educators it is difficult to explain their reasoning to students, because it is partly an automatic and unconscious process. Virtual Patients (VPs) are used to support the acquisition of clinical reasoning skills in healthcare education. However, until now it remains unclear which features or settings of VPs optimally foster clinical reasoning. Therefore, our aims were to identify key concepts of the clinical reasoning process in a qualitative approach and draw conclusions on how each concept can be enhanced to advance the learning of clinical reasoning with virtual patients. Methods: We chose a grounded theory approach to identify key categories and concepts of learning clinical reasoning and develop a framework. Throughout this process, the emerging codes were discussed with a panel of interdisciplinary experts. In a second step we applied the framework to virtual patients. Results: Based on the data we identified the core category as the "multifactorial nature of learning clinical reasoning". This category is reflected in the following five main categories: Psychological Theories, Patient-centeredness, Context, Learner-centeredness, and Teaching/Assessment. Each category encompasses between four and six related concepts. Conclusions: With our approach we were able to elaborate how key categories and concepts of clinical reasoning can be applied to virtual patients. This includes aspects such as allowing learners to access a large number of VPs with adaptable levels of complexity and feedback or emphasizing dual processing, errors, and uncertainty.