Not The Ghost in The Machine: Transforming Patient Data into E-Learning Cases Within A Case-Based Blended Learning Framework For Medical Education

https://doi.org/10.1016/j.sbspro.2015.04.106Get rights and content
Under a Creative Commons license
open access

Abstract

Emergent challenges in medical education support an implementation of trans-disciplinary case-based learning. We propose a case-based blended learning framework, synergistically combining textbook, e-learning cases and a simulated patient course in medical education. A novel server system architecture allows patient data to be automatically transferred from the Vienna General Hospital to the Medical University of Vienna, and then imported into an e-learning template, in which online MCQ questions can be generated. We present one example of a transferred, imported and completed anxiety disorder e-learning case, currently implemented in the pre-clinical psychiatry course. This framework facilitates the creation of up to date teaching content, while addressing difficulties in transforming declarative knowledge into procedural-knowledge and -skill, (background knowledge into clinical reasoning) especially in domains which profit from a structured learning approach in creating a close link in understanding between functional morphology and clinical presentation.

Keywords

Blended learning
Case based learning
Simulated Patients
Clinical reasoning
E-learning

Cited by (0)

Peer-review under responsibility of Academic World Education and Research Center.