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Decentralized virtual reality mastoidectomy simulation training: a prospective, mixed-methods study.

Martin Frendø1,2, Ebbe Thingaard3,4, Lars Konge3

  • 1Department of Otorhinolaryngology-Head and Neck Surgery and Audiology, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark. martin.frendoe-soerensen.01@regionh.dk.

European Archives of Oto-Rhino-Laryngology : Official Journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : Affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
|July 28, 2019
PubMed
Summary
This summary is machine-generated.

Decentralized virtual reality (VR) mastoidectomy training is feasible but requires further evaluation to increase training volume. Evidence for training and access are key motivational factors for surgical residents.

Keywords:
Decentralized trainingMastoidectomySimulationSurgical trainingTemporal bone dissectionVirtual reality

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Area of Science:

  • Surgical Education
  • Medical Simulation
  • Otorhinolaryngology

Background:

  • Virtual reality (VR) training enhances surgical skills, particularly mastoidectomy.
  • Distributed practice in VR simulation is effective but faces barriers due to facility centralization.
  • Decentralized training models offer a potential solution to overcome implementation challenges.

Purpose of the Study:

  • To assess the feasibility, utilization, and barriers of decentralized VR mastoidectomy training.
  • To evaluate a freeware, high-fidelity temporal bone simulator for this purpose.

Main Methods:

  • Prospective, mixed-methods study involving 20 otorhinolaryngology residents.
  • Three months of local access to a VR mastoidectomy simulator with learning supports.
  • Data collection via questionnaires and thematic analysis of focus group interviews.

Main Results:

  • Participants trained a total of 48.5 hours, primarily near the trial's end.
  • Most participants utilized 2-4 learning supports.
  • Key themes identified: convenience, training time, ease of use, evidence, and testing.

Conclusions:

  • Decentralized VR mastoidectomy training is feasible but did not achieve high training volume or distributed practice.
  • Motivational factors include perceived evidence for training, access, educational design, and the role of testing.
  • Further evaluation is needed to optimize decentralized simulation training models.