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Teaching Accuracy through Repeated Gamified Echography Training (TARGET): Assessment of an Ultrasound Skill Simulator in Teaching Ultrasound-Guided Needle Placement, a Prospective Observational Study.

Marissa McGaffeyAlex Zur LindenWilliam SearsGabrielle MonteithDeep K KhosaShauna L Blois
Published in: Journal of veterinary medical education (2023)
The increasing use of ultrasound in veterinary private practice and demand for skilled operators upon graduation has placed an increased burden on the ever-dwindling number of academic radiologists. Simulation-based medical education can help prepare for and consequently reduce this burden, allowing for the acquisition of clinical skills through deliberate practice in a safe, controlled, and low-stakes environment. Ultrasound-guided fine needle placement is the foundation for more advanced interventions such as ultrasound-guided fine needle aspirates and centeses. A reusable novel ultrasound skill simulator consisting of metal targets wired to a circuit and suspended in ballistics gel was created to teach ultrasound-guided fine needle placement. Forty-seven second-year veterinary students watched an instructional video and performed two ultrasound-guided fine needle placement skill tests on the simulator with a period of practice between. Significant improvement in time to task completion ( p = .0021) was noted after the period of practice. The majority of student feedback was positive with 89% (42/47) indicating they would use the simulator again to practice and that it should be incorporated into the curriculum, 74% (35/47) indicating their basic skills, knowledge, and confidence using ultrasound improved using the simulator, and 55% (26/47) indicating they could now teach this skill to a peer. The authors suggest further development of this model for ease of manufacture and increased variation in difficulty, and veterinary curriculum incorporation for basic ultrasound-guided fine needle placement training.
Keyphrases
  • ultrasound guided
  • medical education
  • virtual reality
  • medical students
  • healthcare
  • fine needle aspiration
  • primary care
  • air pollution
  • quality improvement
  • risk factors
  • computed tomography
  • deep learning