Digital Transformation in Musculoskeletal Ultrasound: Acceptability of Blended Learning

Andreas Michael Weimer, Rainer Berthold, Christian Schamberger, Thomas Vieth, Gerd Balser, Svenja Berthold, Stephan Stein, Lukas Müller, Daniel Merkel, Florian Recker, Gerhard Schmidmaier, Maximilian Rink, Julian Künzel, Roman Kloeckner, Johannes Weimer*

*Corresponding author for this work

Abstract

Background: ultrasound diagnostics have a broad spectrum of applications, including among diseases of the musculoskeletal system. Accordingly, it is important for the users to have a well-founded and up-to-date education in this dynamic examination method. The right balance between online and in-class teaching still needs to be explored in this context. Certifying institutions are currently testing digitally transformed teaching concepts to provide more evidence. Methods: this study compared two musculoskeletal ultrasound blended learning models. Model A was more traditional, with a focus on in-person teaching, while Model B was more digitally oriented with compulsory webinar. Both used e-learning for preparation. Participants completed evaluations using a seven-point Likert scale, later converted to a 0–1 scale. Digital teaching media (e-learning) were used for preparation in both courses. Results: the analysis included n = 41 evaluations for Model A and n = 30 for Model B. Model B received a better overall assessment (median: 0.73 vs. 0.69, p = 0.05). Model B also excelled in “course preparation” (p = 0.02), “webinar quality” (p = 0.04), and “course concept” (p = 0.04). The “gain of competence” (p = 0.82), “learning materials” (p = 0.30), and “tutor quality” (p = 0.28) showed no significant differences. Conclusion: participants favorably assessed blended learning in ultrasound teaching. Certifying institutions should consider accrediting models that combine digital methods (e.g., internet lectures/webinars) and materials (e.g., e-learning) with hands-on ultrasound training. Further research is needed to validate these subjective findings for a stronger evidential basis.

Original languageEnglish
Article number3272
JournalDiagnostics
Volume13
Issue number20
ISSN2075-4418
DOIs
Publication statusPublished - 10.2023

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