Al-Saud, LM, Mushtaq, F orcid.org/0000-0001-7881-1127, Mann, RP orcid.org/0000-0003-0701-1274 et al. (7 more authors) (2020) Early assessment with a virtual reality haptic simulator predicts performance in clinical practice. BMJ Simulation & Technology Enhanced Learning, 6. pp. 274-278. ISSN 2056-6697
Abstract
Background: Prediction of clinical training aptitude in medicine and dentistry is largely driven by measures of a student’s intellectual capabilities. The measurement of sensorimotor ability has lagged behind, despite being a key constraint for safe and efficient practice in procedure-based medical specialties. Virtual reality (VR) haptic simulators, systems able to provide objective measures of sensorimotor performance, are beginning to establish their utility in facilitating sensorimotor skill acquisition, and it is possible that they may also inform the prediction of clinical performance.
Methods: A retrospective cohort study examined the relationship between student performance on a haptic VR simulator in the second year of undergraduate dental study with subsequent clinic performance involving patients 2 years later. The predictive ability was tested against a phantom-head crown test (a traditional preclinical dental assessment, in the third year of study).
Results: VR scores averaged across the year explained 14% of variance in clinic performance, while the traditional test explained 5%. Students who scored highly on this averaged measure were ~10 times more likely to be high performers in the clinical crown test. Exploratory analysis indicated that single-trial VR scores did not correlate with real-world performance, but the relationship was statistically significant and strongest in the first half of the year and weakened over time.
Conclusions: The data demonstrate the potential of a VR haptic simulator to predict clinical performance and open up the possibility of taking a data-driven approach to identifying individuals who could benefit from support in the early stages of training.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ. This is an author produced version of a paper published in BMJ Simulation & Technology Enhanced Learning. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Dentistry (Leeds) > Restorative Dentistry (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Psychology (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/R031193/1 Alan Turing Institute Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Nov 2019 15:08 |
Last Modified: | 15 Dec 2020 12:01 |
Status: | Published |
Publisher: | BMJ Journals |
Identification Number: | 10.1136/bmjstel-2018-000420 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:153012 |