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Early assessment with a virtual reality haptic simulator predicts performance in clinical practice.

Authors :
Al-Saud LM
Mushtaq F
Mann RP
Mirghani I
Balkhoyor A
Harris R
Osnes C
Keeling A
Mon-Williams MA
Manogue M
Source :
BMJ simulation & technology enhanced learning [BMJ Simul Technol Enhanc Learn] 2020 Sep 03; Vol. 6 (5), pp. 274-278. Date of Electronic Publication: 2020 Sep 03 (Print Publication: 2020).
Publication Year :
2020

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.<br />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).<br />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.<br />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.<br />Competing Interests: Competing interests: None declared.<br /> (© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

Details

Language :
English
ISSN :
2056-6697
Volume :
6
Issue :
5
Database :
MEDLINE
Journal :
BMJ simulation & technology enhanced learning
Publication Type :
Academic Journal
Accession number :
35517392
Full Text :
https://doi.org/10.1136/bmjstel-2018-000420