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Original Research: EDUCATION, TEACHING, AND QUALITY IMPROVEMENT |

Simulation Training Improves Diagnostic Performance on a Real Patient With Similar Clinical Findings

Kristin Fraser, MD; Bruce Wright, MD; Louis Girard, MD; Janet Tworek, MSc; Mike Paget, BFA; Lisa Welikovich, MD; Kevin McLaughlin, PhD
Author and Funding Information

From the Department of Medicine (Drs Fraser, Girard, Welikovich, and McLaughlin), Department of Family Medicine (Dr Wright), and Office of Undergraduate Medical Education (Drs Wright and McLaughlin; Ms Tworek; and Mr Paget), University of Calgary, Calgary, AB, Canada.

Correspondence to: Kevin McLaughlin, PhD, Office of Undergraduate Medical Education, University of Calgary, Health Sciences Centre, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada; e-mail: kmclaugh@ucalgary.ca


Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (http://www.chestpubs.org/site/misc/reprints.xhtml).

Funding/Support: Funding for this study was provided by the Office of Undergraduate Medical Education at the University of Calgary.


© 2011 American College of Chest Physicians


Chest. 2011;139(2):376-381. doi:10.1378/chest.10-1107
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Background:  Training on a cardiopulmonary simulator improves subsequent diagnostic performance on the same simulator. But data are lacking on transfer of learning. The objective of this study was to determine whether training on a cardiorespiratory simulator improves diagnostic performance on a real patient.

Methods:  We randomly allocated first-year medical students at the University of Calgary to simulator training in one of three clinical scenarios of acute-onset chest pain: pulmonary embolism with right ventricular strain but no murmur, symptomatic aortic stenosis, or myocardial ischemia causing mitral regurgitation. Simulation sessions ran for 20 min, after which participants had a standardized debriefing session and reviewed the physical findings. Immediately following the training sessions, students assessed the auscultatory findings of a real patient with mitral regurgitation. Our outcome measures were accuracy of identifying abnormal auscultatory findings and diagnosing the underlying cardiac abnormality (mitral regurgitation).

Results:  Eighty-six students participated in the study. Students trained on mitral regurgitation were more likely to identify and diagnose these findings on a real patient with mitral regurgitation than those who had trained on aortic stenosis or a scenario with no cardiac murmur. The accuracy (SD) of identifying clinical features of mitral regurgitation for these three groups was 74.0 (36.4) vs 56.2 (34.3) vs 36.8 (33.1), respectively (P = .0005), and for diagnosing mitral regurgitation, the accuracy was 68.0 (45.4) vs 51.6 (50.0) vs 29.9 (40.7), respectively (P = .01).

Conclusions:  Simulator training on mitral regurgitation increases the likelihood of diagnosing this abnormality on a real patient

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