Adaptive Virtual Reality Training To Optimize Military Medical Skills Acquisition And Retention

Abstract

The Department of Defense has pursued the integration of virtual reality simulation into medical training and applications to fulfill the need to train 100,000 military health care personnel annually. Medical personnel transitions, both when entering an operational area and returning to the civilian theater, are characterized by the need to rapidly reacquire skills that are essential but have decayed through disuse or infrequent use. Improved efficiency in reacquiring such skills is critical to avoid the likelihood of mistakes that may result in mortality and morbidity. We focus here on a study testing a theory of how the skills required for minimally invasive surgery for military surgeons are learned and retained. Our adaptive virtual reality surgical training system will incorporate an intelligent mechanism for tracking performance that will recognize skill deficiencies and generate an optimal adaptive training schedule. Our design is modeling skill acquisition based on a skill retention theory. The complexity of appropriate training tasks is adjusted according to the level of retention and/or surgical experience. Based on preliminary work, our system will improve the capability to interactively assess the level of skills learning and decay, optimizes skill relearning across levels of surgical experience, and positively impact skill maintenance. Our system could eventually reduce mortality and morbidity by providing trainees with the reexperience they need to help make a transition between operating theaters. This article reports some data that will support adaptive tutoring of minimally invasive surgery and similar surgical skills.

Publication Date

5-1-2016

Publication Title

Military Medicine

Volume

181

Issue

5

Number of Pages

214-220

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.7205/MILMED-D-15-00164

Socpus ID

84979010946 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84979010946

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