Exploration of an Effective Method for the Step-by-Step Presentation of Case Information to Guide Grade 4 Medical Students to Develop Clinical Reasoning Skills

Simin Huang, Jing Yang, Feifei Wang, Jun Guo, Shengming Liu

Abstract


Clinical reasoning ability is an important competence for a clinician to have. Undergraduate study is a crucial period to strengthen medical students' clinical reasoning skills. The aim of this study was to explore an effective method for guiding students to improve clinical reasoning skills via a step-by-step presentation of case information. The study was conducted among grade 2015 clinical medicine major students who were studying internal medicine. On the basis of the theoretical study and practical training, a method for the step-by-step presentation of case information was designed and implemented to strengthen students’ clinical reasoning skills. Each case was divided into four modules. Module one focused on inquiry, module two focused on physical examination, module three focused on laboratory tests and module four focused on diagnosis and treatment. Four modules were sent to students in turn as homework. The teacher corrected their answers and feedback was individually given. A questionnaire was conducted at the end of semester to assess the effect. The questionnaire revealed that students were satisfied with this training mode. They thought the mode was helpful for improving clinical reasoning ability and consolidating the basic skills such as history taking and physical examination. In conclusion, this effective method provides a training pattern for developing clinical reasoning skills of medical students. Through the process of analysing clinical cases, students are guided to become familiar with the procedures of solving clinical problems from gathering medical information to establishing diagnosis and treatment plans. It helps students to establish a scientific clinical reasoning mode.

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DOI: https://doi.org/10.5430/jct.v10n1p36

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Copyright (c) 2021 Simin Huang, Jing Yang, Feifei Wang, Jun Guo, Shengming Liu

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