Evidence map›Paper›PMID 36925661›Full record

ArticleComputer methods and programs in biomedicine update2023

Development of a mobile tele-education system to assist remote otolaryngology learning during COVID-19 pandemic.

Te-Yung Fang, Su-Yi Hsu, Jun-Ming Su, Pa-Chun Wang

Abstract read
In one paragraph

Article in Computer methods and programs in biomedicine update, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Te-Yung FangDepartment of Otolaryngology Head Neck Surgery, Cathay General Hospital, Taipei, Taiwan.
Su-Yi HsuDepartment of Otolaryngology Head Neck Surgery, Cathay General Hospital, Taipei, Taiwan.
Jun-Ming SuDepartment of Information and Learning Technology, National University of Tainan, Tainan, Taiwan.
Pa-Chun WangDepartment of Otolaryngology Head Neck Surgery, Cathay General Hospital, Taipei, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Developing clinical thinking competence (CTC) is crucial for physicians, but effective methods for cultivation and evaluation are a significant challenge. Classroom teaching and paper-and-pencil tests are insufficient, and clinical field learning is difficult to implement, especially during the COVID-19 pandemic. Simulation learning is a useful alternative, but existing methods, e.g., OSCE, 3D AR/VR, and SimMan, have limitations in terms of time, space, and cost. Objective: This study aims to present the design and development of an Otolaryngology Mobile Tele-education System (OMTS) to facilitate CTC learning, and to evaluate the system's usability with senior otolaryngology experts. Methods: The OMTS system utilizes the convenience of mobile learning and the touch function of mobile devices to assist users (medical students or post-graduate physicians) in learning CTC remotely. Clinical knowledge and system functions in the OMTS system are defined by senior experts based on required CTC learning cases. Through simulated clinical case scenarios, users can engage in interactive clinical inquiry, practice required physical and laboratory examinations, make treatment decisions based on simulated responses, and understand and correct learning problems through a diagnostic report for effective learning. Usability testing of the OMTS system was evaluated by three senior otolaryngology experts using measurements of content validity, system usability, and mental workload during their available time and location. Results: Statistical results of experts' evaluation showed that the OMTS system has good content validity, marginal-to-acceptable system usability, and moderate mental workload. Experts agreed that the system was efficient, professional, and usable for learning, although the practicality of the clinical inquiry and hands-on practice functions could be improved further. Conclusions: Based on the OMTS system, users can efficiently hands-on practice and learn clinical cases in otolaryngology, and understand and correct their problems according to the diagnostic report. Therefore, the OMTS system can be expected to facilitate CTC learning according to experts' evaluation.

Indexed as

Clinical thinking competenceCOVID-19CTC, Clinical Thinking CompetenceCVI, Content Validity IndexENT, Ear, Nose, and ThroatHands-on practiceMobile learningOSCE, Objective Structured Clinical ExaminationOtolaryngologySUS, System Usability ScaleTAM, Technology Acceptance Model

Identifiers

PMID36925661
PMCPMC9995394

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.