Hege Inga, Kononowicz Andrzej A, Berman Norman B, Lenzer Benedikt, Kiesewetter Jan
LMU Munich, Institute for Medical Educaiton, Munich, Germany.
Jagiellonian University Medical College, Department of Bioinformatics and Telemedicine, Krakow, Poland.
GMS J Med Educ. 2018 Feb 15;35(1):Doc12. doi: 10.3205/zma001159. eCollection 2018.
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. 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. 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. 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.
临床推理是学生在其教育过程中必须掌握的一项复杂技能。对于教育工作者而言，向学生解释他们的推理过程很困难，因为这在一定程度上是一个自动且无意识的过程。虚拟患者（VPs）被用于支持医疗保健教育中临床推理技能的培养。然而，到目前为止，尚不清楚虚拟患者的哪些特征或设置能最佳地促进临床推理。因此，我们的目标是以定性方法确定临床推理过程的关键概念，并就如何增强每个概念以推进利用虚拟患者进行临床推理学习得出结论。我们选择扎根理论方法来确定学习临床推理的关键类别和概念，并构建一个框架。在整个过程中，与跨学科专家小组讨论了新出现的编码。在第二步中，我们将该框架应用于虚拟患者。基于这些数据，我们确定核心类别为“学习临床推理的多因素性质”。该类别体现在以下五个主要类别中：心理学理论、以患者为中心、背景、以学习者为中心以及教学/评估。每个类别包含四到六个相关概念。通过我们的方法，我们能够详细阐述临床推理的关键类别和概念如何应用于虚拟患者。这包括诸如允许学习者访问大量具有不同复杂程度和反馈的虚拟患者，或强调双重处理、错误和不确定性等方面。
