Literature DB >> 25010240

Competency in electrocardiogram interpretation among graduating medical students.

Robert S Jablonover1, Erin Lundberg, Yilong Zhang, Alex Stagnaro-Green.   

Abstract

BACKGROUND: The ability to accurately interpret electrocardiogram (ECG) abnormalities is a core competency for graduating medical students (GMS). Incorrect interpretation of ECG findings can result in adverse patient outcomes. To our knowledge, there has been no published study evaluating the level of competency in ECG interpretation in GMS. PURPOSES: To evaluate the ability of graduating medical students to interpret abnormal and critical ECGs and to correlate student performance with self-reported confidence and adequacy of ECG training.
METHODS: A list of 22 ECGs which GMS are expected to identify was developed. Classic examples of each ECG were identified and verified by two board-certified cardiologists. The 22 ECGs along with 11 questions related to confidence and degree of ECG training were administered to (a) 168 4th-year George Washington University School of Medicine (GWUSOM) students, (b) 63 incoming housestaff to GWUSOM, and (c) 22 graduating internal medicine housestaff.
RESULTS: Given the lack of statistical differences, GW medical students and incoming housestaff were combined into a single group (GMS, n=231). Mean number of correct answers on the 22 ECG examination for GMS was 8.2 (SE=0.529) and 13.9 (SE=1.312) for graduating residents (p<.0001). On the 6 life-threatening ECGs, GMS scored lower than graduating residents (3.4 SE=0.191 vs. 4.6 SE=0.541; p<.0002). Mean score in the GMS group was associated with increasing levels of reported confidence and degree of ECG experience.
CONCLUSIONS: A 22-item ECG examination was developed, piloted, and demonstrated to have construct validity. GMS had a limited level of competency in ECG interpretation which was correlated with reported self-confidence and degree of ECG exposure in Years 3-4.

Entities:  

Keywords:  competency; electrocardiogram (ECG); interpretation; medical students

Mesh:

Year:  2014        PMID: 25010240     DOI: 10.1080/10401334.2014.918882

Source DB:  PubMed          Journal:  Teach Learn Med        ISSN: 1040-1334            Impact factor:   2.414


  18 in total

1.  Mastering Electrocardiogram Interpretation Skills Through a Perceptual and Adaptive Learning Module.

Authors:  Sally Krasne; Carl D Stevens; Philip J Kellman; James T Niemann
Journal:  AEM Educ Train       Date:  2020-05-05

2.  Is computer-assisted instruction more effective than other educational methods in achieving ECG competence among medical students and residents? Protocol for a systematic review and meta-analysis.

Authors:  Charle André Viljoen; Rob Scott Millar; Mark E Engel; Mary Shelton; Vanessa Burch
Journal:  BMJ Open       Date:  2017-12-26       Impact factor: 2.692

3.  Electrocardiography Interpretation Competency of Medical Interns: Experience from Two Ethiopian Medical Schools.

Authors:  Melaku Getachew; Temesgen Beyene; Sofia Kebede
Journal:  Emerg Med Int       Date:  2020-05-11       Impact factor: 1.112

4.  A qualitative study on the development and rectification of advanced medical students' misconceptions about the physiological electrocardiogram (ECG).

Authors:  Mathias Trauschke
Journal:  GMS J Med Educ       Date:  2019-11-15

5.  Competency in ECG Interpretation Among Medical Students.

Authors:  Grzegorz Kopeć; Wojciech Magoń; Mateusz Hołda; Piotr Podolec
Journal:  Med Sci Monit       Date:  2015-11-06

6.  Computer model for the cardiovascular system: development of an e-learning tool for teaching of medical students.

Authors:  David Roy Warriner; Martin Bayley; Yubing Shi; Patricia Victoria Lawford; Andrew Narracott; John Fenner
Journal:  BMC Med Educ       Date:  2017-11-21       Impact factor: 2.463

7.  A pragmatic randomised controlled trial of SAFMEDS to produce fluency in interpretation of electrocardiograms.

Authors:  Louise Rabbitt; Dara Byrne; Paul O'Connor; Miroslawa Gorecka; Alan Jacobsen; Sinéad Lydon
Journal:  BMC Med Educ       Date:  2020-03-31       Impact factor: 2.463

8.  Is computer-assisted instruction more effective than other educational methods in achieving ECG competence amongst medical students and residents? A systematic review and meta-analysis.

Authors:  Charle André Viljoen; Rob Scott Millar; Mark E Engel; Mary Shelton; Vanessa Burch
Journal:  BMJ Open       Date:  2019-11-18       Impact factor: 2.692

9.  Quantifying the medical student learning curve for ECG rhythm strip interpretation using deliberate practice.

Authors:  Jason Waechter; David Reading; Chel Hee Lee; Mathieu Walker
Journal:  GMS J Med Educ       Date:  2019-08-15

10.  An evaluation study on gamified online learning experiences and its acceptance among medical students.

Authors:  May Honey Ohn; Khin-Maung Ohn
Journal:  Ci Ji Yi Xue Za Zhi       Date:  2019-06-06
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