Medical students should be able to actively apply clinical reasoning skills to further their interpretative, diagnostic, and treatment skills in a non-obtrusive and scalable way. Case-Based Learning (CBL) approach has been receiving attention in medical education as it is a student-centered teaching methodology that exposes students to real-world scenarios that need to be solved using their reasoning skills and existing theoretical knowledge. In this paper, we propose an interactive CBL System, called iCBLS, which supports the development of collaborative clinical reasoning skills for medical students in an online environment. The iCBLS consists of three modules: (i) system administration (SA), (ii) clinical case creation (CCC) with an innovative semi-automatic approach, and (iii) case formulation (CF) through intervention of medical students' and teachers' knowledge. Two evaluations under the umbrella of the context/input/process/product (CIPP) model have been performed with a Glycemia study. The first focused on the system satisfaction, evaluated by 54 students. The latter aimed to evaluate the system effectiveness, simulated by 155 students. The results show a high success rate of 70% for students' interaction, 76.4% for group learning, 72.8% for solo learning, and 74.6% for improved clinical skills.
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http://dx.doi.org/10.1016/j.ijmedinf.2017.11.004 | DOI Listing |
BMC Med Educ
January 2025
HAN University of Applied Sciences, Academy Allied Health Sciences, Nijmegen, The Netherlands.
Background: Educational innovation in health professional education is needed to keep up with rapidly changing healthcare systems and societal needs. This study evaluates the implementation of PACE, an innovative curriculum designed by the physiotherapy department of the HAN University of Applied Sciences in The Netherlands. The PACE concept features an integrated approach to learning and assessment based on pre-set learning outcomes, personalized learning goals, flexible learning routes, and programmatic assessment.
View Article and Find Full Text PDFAdv Physiol Educ
January 2025
Department of Biological Sciences, San Jose State University, San Jose, CA 95192, USA.
Generative large language models (LLMs) like ChatGPT can quickly produce informative essays on various topics. However, the information generated cannot be fully trusted as artificial intelligence (AI) can make factual mistakes. This poses challenges for using such tools in college classrooms.
View Article and Find Full Text PDFJMIR Med Educ
January 2025
Department of Orthopedics, First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Background: Teaching severe pelvic trauma poses a significant challenge in orthopedic surgery education due to the necessity of both clinical reasoning and procedural operational skills for mastery. Traditional methods of instruction, including theoretical teaching and mannequin practice, face limitations due to the complexity, the unpredictability of treatment scenarios, the scarcity of typical cases, and the abstract nature of traditional teaching, all of which impede students' knowledge acquisition.
Objective: This study aims to introduce a novel experimental teaching methodology for severe pelvic trauma, integrating virtual reality (VR) technology as a potent adjunct to existing teaching practices.
Introduction: Professional identity is one of the frequently used and least clearly defined terms in healthcare education. Its simplest definition includes feeling, thinking, and acting like a representative of a given professional group. Many other aspects of professional identity exist.
View Article and Find Full Text PDFTrends Hear
January 2025
Department of Otolaryngology - Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.
When listening to speech under adverse conditions, listeners compensate using neurocognitive resources. A clinically relevant form of adverse listening is listening through a cochlear implant (CI), which provides a spectrally degraded signal. CI listening is often simulated through noise-vocoding.
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