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New web-based applications for mechanistic case diagramming
Journal article   Open access   Peer reviewed

New web-based applications for mechanistic case diagramming

Fred R Dee, Thomas H Haugen and Clarence D Kreiter
Medical education online, Vol.19(1), pp.24708-24708
01/01/2014
DOI: 10.3402/meo.v19.24708
PMCID: PMC4110383
PMID: 25059836
url
https://doi.org/10.3402/meo.v19.24708View
Published (Version of record) Open Access

Abstract

The goal of mechanistic case diagraming (MCD) is to provide students with more in-depth understanding of cause and effect relationships and basic mechanistic pathways in medicine. This will enable them to better explain how observed clinical findings develop from preceding pathogenic and pathophysiological events. The pedagogic function of MCD is in relating risk factors, disease entities and morphology, signs and symptoms, and test and procedure findings in a specific case scenario with etiologic pathogenic and pathophysiological sequences within a flow diagram. In this paper, we describe the addition of automation and predetermined lists to further develop the original concept of MCD as described by Engelberg in 1992 and Guerrero in 2001. We demonstrate that with these modifications, MCD is effective and efficient in small group case-based teaching for second-year medical students (ratings of ~3.4 on a 4.0 scale). There was also a significant correlation with other measures of competency, with a 'true' score correlation of 0.54. A traditional calculation of reliability showed promising results (α =0.47) within a low stakes, ungraded environment. Further, we have demonstrated MCD's potential for use in independent learning and TBL. Future studies are needed to evaluate MCD's potential for use in medium stakes assessment or self-paced independent learning and assessment. MCD may be especially relevant in returning students to the application of basic medical science mechanisms in the clinical years.
Clinical-Pathologic Correlation Pathophysiology Mechanisms of Disease Pathogenesis Case Based Learning

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