P.126 Enhancing patient understanding of spinal conditions through advanced imaging platforms
Background: In spite of the shift towards “personalized medicine,” ambulatory medicine lags behind the cutting edge technology employed in non-medical fields to convey information in unique ways to enhance customer interactions. Furthermore, the complex nature of neurosurgical concepts can be diffic...
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Veröffentlicht in: | Canadian journal of neurological sciences 2019-06, Vol.46 (s1), p.S46-S46 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Background: In spite of the shift towards “personalized medicine,” ambulatory medicine lags behind the cutting edge technology employed in non-medical fields to convey information in unique ways to enhance customer interactions. Furthermore, the complex nature of neurosurgical concepts can be difficult to convey within the confines of a short outpatient visit. These factors, coupled with potentially long wait times, can limit a patient’s engagement in the treatment process. We propose that application of advanced video platforms will empower patients to feel that neurosurgical concepts are accessible and understandable and enable the face-to-face time with the physician to begin at a more sophisticated level, ultimately improving patient engagment. Methods: 3D modeling, animation, and video game design were used in conjuction with tablet computers and VR headsets to create a video-driven “choose-your-own-adventure style patient experience” with initial use during waiting times prior to face-to-face interaction with the neurosurgery providers. Results: 3D modeling, animation, and virtual reality were successfully implemented in the Northwestern Medicine neurosurgery clinic with positive impact on patient engagement, including preliminary improvements in multiple patient satisfaction/”Likelihood to Recommend” scores. Conclusions: Advanced imaging platforms, including 3D modeling, animation, and virtual reality show great promise in improving patient engagement, patient retention, and “Likelihood to Recommend” scores. |
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ISSN: | 0317-1671 2057-0155 |
DOI: | 10.1017/cjn.2019.217 |