Online Liver Imaging Course; Pivoting to Transform Radiology Education During the SARS-CoV-2 Pandemic
Khaled M Elsayes1, Robert M Marks2, Serageldin Kamel3
1Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX, 77030.
Academic Radiology
|October 28, 2020
Summary
The Online Liver Imaging Course (OLIC) successfully transitioned radiology education online during the pandemic, with most participants finding virtual learning beneficial and preferring it over in-person conferences. This indicates virtual radiology education is a sustainable model for the future.
Area of Science:
- Radiology and Medical Education
- Virtual Learning Technologies
- Pandemic Response in Healthcare Education
Background:
- The COVID-19 pandemic significantly disrupted traditional in-person radiology education.
- Maintaining continuity in radiology training and professional development was a critical challenge.
Purpose of the Study:
- To evaluate the implementation and effectiveness of a virtual teaching method for radiology education.
- To assess the role of available technology in sustaining radiology education during the pandemic.
Main Methods:
- Development and delivery of the Online Liver Imaging Course (OLIC) with 28 online lectures over six weeks.
- Real-time and on-demand lecture delivery via videoconferencing.
- Post-course survey distributed to 487 attendees from 37 countries.
Main Results:
- High engagement with 1,434 initial sign-ups and an average of 89 live participants per session.
- Overwhelmingly positive feedback: 97% found the course beneficial, with 57% exceeding expectations.
- 84% expressed willingness to attend future virtual conferences, and 61% preferred virtual to in-person formats.
Conclusions:
- Videoconferencing offers an effective and inexpensive alternative for radiology conferences and education.
- The success of OLIC suggests virtual education is a lasting component of radiology training.
- Educational institutions should support and develop virtual learning models for radiology.
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