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An IoT-based CBL methodology to create real-world clinical cases for medical education

Citation

Ali, M and Lee, S and Kang, BH, An IoT-based CBL methodology to create real-world clinical cases for medical education, Proceedings from the 8th International Conference on ICT Convergence, 18-20 October, Jeju Island, Korea, pp. 1038-1041. ISBN 9781509040322 (2017) [Refereed Conference Paper]

Copyright Statement

Copyright 2017 IEEE

Official URL: http://dx.doi.org/10.1109/ICTC.2017.8190847

Abstract

Medical education is the practice of being a medical practitioner, which varies considerably across the world. It is an active research area and has evolved tremendously in recent decades. The learning activities are commonly explored using patient cases. Among multiple medical education methodologies, Case-Based Learning (CBL) is considered as an effective methodology for small-group of medical students. Normally in CBL, the medical experts give the legacy fixed medical cases to students during their class for group discussions and their learning. In this practice, due to lack of beforehand practice or knowledge about that particular case, students hesitate to participate due to lack of confidence. Internet of Things (IoTs) is one of the well-known emerging technology and this hesitation can be dealt with creating real-world clinical cases using IoTs data and user-friendly environment for case-based learning. In this paper, an IoT-based CBL methodology is introduced, which records real-world patient data using IoTs, analyzes the imperative signsí data, creates the real-world clinical case for students practicing, and finally provides feedback to medical students. For case study purposes, our developed CBL tool is simulated on patientís data to realize the proposed methodology.

Item Details

Item Type:Refereed Conference Paper
Keywords:internet of things, case-based learning, clinical case, medical education
Research Division:Information and Computing Sciences
Research Group:Library and Information Studies
Research Field:Health Informatics
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Information Processing Services (incl. Data Entry and Capture)
UTAS Author:Ali, M ( Maqbool Ali)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:123137
Year Published:2017
Deposited By:Information and Communication Technology
Deposited On:2017-12-18
Last Modified:2018-06-15
Downloads:0

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