Radiology podcasting as a model for asynchronous remote learning in the COVID-19 era
Document TypeJournal Article
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AbstractRATIONALE AND OBJECTIVES: We aimed to create an open access online radiology podcast to educate listeners at any time, from anywhere. To meet learner needs and improve the likelihood of successful implementation and utilization, we assessed radiology trainee attitudes and experiences of podcasts. MATERIALS AND METHODS: We developed an educational podcast, From the Viewbox, focused on evergreen themes and practical approaches to radiology. Content categories included Diagnostic Approach, Specific Imaging Diagnoses, Noninterpretive Skills, and Special Topics. We released and promoted episodes on multiple digital platforms. Radiology trainees were surveyed and data were analyzed to assess listener preferences and usage trends. RESULTS: Only 19% of our trainees had previously listened to a radiology podcast, yet 81% expressed interest in listening routinely. After initial release, 86% of trainees listened to the podcast and 62% listened routinely. Episodes gained the most plays immediately following release but retained and continued to attract more listeners. The most popular episode discussing COVID-19 diagnosis and imaging, emphasized the importance of selecting high yield content to match listener needs. Most trainees felt the podcast had "very high" or "high" value in educational value, accessibility, and time efficiency. CONCLUSIONS: From the Viewbox offers efficient and accessible audio-only learning modules that can be used independently or effectively paired with traditional resources to decrease barriers in radiology education and enhance learner productivity. Podcasting is an underutilized asynchronous remote learning tool that can help overcome current challenges of social distancing, and more importantly address the diverse preferences and needs of our learners.
Shiang T, Cerniglia C, Lin H, Lo HS. Radiology podcasting as a model for asynchronous remote learning in the COVID-19 era. Clin Imaging. 2020 Nov 26;71:147-154. doi: 10.1016/j.clinimag.2020.10.045. Epub ahead of print. PMID: 33259980. Link to article on publisher's site