Improving Patient Education Using Augmented Reality for Spine Fractures: Feasibility Study and Review of Literature
Year: 2024
Type: article
Abstract: Purpose: The purpose of this prospective feasibility study was to investigate the use of an augmented reality (AR) 3-dimensional (3D) spine fracture model, in an acute inpatient setting on patient education. Methods: Adult patients (18-89 years) with traumatic spine fractures were included (June 2021-July 2022). Standard spine computed tomography imaging obtained during trauma evaluation was used to create 3D spine models through an open-source application and converted to an AR software application. A 3D-spine model was presented to patients by a resident while discussing spinal anatomy, injury, treatment options, and possible complications. Post consultation, patients completed Likert-scale surveys related to the consultation discussion, with scores subsequently tabulated. Additionally, a review of the literature was performed using the search terms [(virtual reality) AND (patient education)] and [(augmented reality) AND (patient education)] up to May 2023. Results: Eight patients (50% female), mean age of 57 years (range, 28-85 years), were included. Injuries comprised four cervical, three thoracic, and one lumbar fracture. Treatment(s) included four operative interventions, three braces, and one observation. Median patient Likert-scale score was 36 (range, 33.75-36; maximum = 36). Highest patient scores were for how helpful the 3D spine model was for learning about specific spine anatomy, injury, treatment plan, and possible complications. Seven articles were identified that investigated patient education using 3D mediums. All studies were in the outpatient setting. Conclusion: AR-based simulation has the potential to improve patient understanding of spinal anatomy, injury, treatment plan, and potential complications over a wide range of traumatic spinal fractures and management plans in the inpatient setting.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4394937287