Enhancing IV Needle Insertion Training With a Bimanual Haptic VR Simulator: Development, Usability, and Learning Impact
Training healthcare professionals in intravenous (IV) needle insertion is a critical component of medical education, traditionally relying on manikin-based simulations and real-life practice. However, the advent of Haptic Virtual Reality (HVR) technologies offers a transformative approach to this training, enhancing safety and potential efficiency. This study explores the development of an IV needle insertion simulator using two different haptic devices integrated into a VR system on the Unity platform and assesses its impact on learning through a 3-week experiment. The simulator is designed to create a realistic and immersive training environment by incorporating detailed anatomical models, physics-based hand interactions, and realtime haptic feedback. The virtual environment replicates a clinical setting, featuring a patient arm model and an IV needle. The haptic feedback is programmed to offer realistic feelings of needle insertion and hand grasping, improving the user's accuracy. Learning impact and usability testing with 41 students indicate a promising improvement in skill acquisition and confidence. Specifically, participants showed a 55% increase in success rates and a significant boost in confidence. This highfidelity HVR simulator represents a significant step forward in medical training technologies, offering a scalable and repeatable training tool adaptable to various educational needs and skill levels.
Authors
- Jin Seok Woo (ORCID: https://orcid.org/0000-0001-8921-2832)
- Robert J. Clements (ORCID: https://orcid.org/0000-0002-9349-1990)
- John Dunlosky (ORCID: https://orcid.org/0000-0002-7367-7958)
- Allan James (ORCID: https://orcid.org/0000-0002-9375-6223)
- Jeremy Jarzembak
- Kwangtaek Kim (ORCID: https://orcid.org/0000-0002-6830-4655)
- J. Biggs
- Jin‐Woo Kim (ORCID: https://orcid.org/0000-0002-1672-5730)
Institutions
- Kent State University (US)
Publication Details
- Journal
- IEEE Transactions on Learning Technologies
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tlt.2025.3592579
- Citations
- 1
- Primary Topic
- Engineering Technology and Methodologies
- Type
- article
- Field-Weighted Citation Impact
- 1.24