Literature DB >> 23506909

Pulmonary embolism teaching file: a simple pilot study for rapidly increasing pulmonary embolism recognition among new residents using interactive cross-sectional imaging.

Jamie Williams1, Takashi Shawn Sato, Bruno Policeni.   

Abstract

RATIONALE AND
OBJECTIVES: Chest radiographs can be demanding, making this an area of focus during most first-year resident chest rotations. This often comes at a cost of cross-sectional imaging, and new residents are often not initially comfortable with reading chest computed tomographic angiograms (CTAs) for pulmonary embolisms (PEs). We created a teaching file of CTAs to improve the detection of PEs.
MATERIALS AND METHODS: For initial testing, we used videos of 25 cases, which played for 90 seconds (to allow multiple passes) to residents with and without call experience. The presence and location of PEs and the readers' confidence scores were recorded. After initial testing, first-year residents without call experience were given 20 separate known positive CTA videos to scroll through on their own. The goal of this was to allow for individual review and development of individual search strategies. A second testing was done with all levels of residents with the same initial 25 cases, re-randomized to evaluate for improvement.
RESULTS: Initially, first-year residents without call experience identified an average of 14.7 of 18 examinations positive for PEs (versus 15.8 for more senior residents; P < .04). After reviewing the 20 known positive cases, the first-year residents improved, averaging 16.6 (versus 14.7 earlier; P < .01).
CONCLUSIONS: We created a fast, simple way to expose novice residents to CTA examinations and increase their accuracy in identifying PEs. After using a teaching file, the ability to recognize PEs improved significantly, and scores were no longer significantly different from those of residents with call experience.
Copyright © 2013 AUR. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Teaching file; education; pulmonary embolisms

Mesh:

Year:  2013        PMID: 23506909     DOI: 10.1016/j.acra.2012.12.020

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  2 in total

1.  Artificial Intelligence-Powered Clinical Decision Support and Simulation Platform for Radiology Trainee Education.

Authors:  Chintan Shah; Karapet Davtyan; Ilya Nasrallah; R Nick Bryan; Suyash Mohan
Journal:  J Digit Imaging       Date:  2022-10-24       Impact factor: 4.903

2.  What We Do and Do Not Know about Teaching Medical Image Interpretation.

Authors:  Ellen M Kok; Koos van Geel; Jeroen J G van Merriënboer; Simon G F Robben
Journal:  Front Psychol       Date:  2017-03-03
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.