Augmentation of telemedicine post-operative follow-up after oculofacial plastic surgery with a self-guided patient tool

Orbit. 2022 Aug 3:1-11. doi: 10.1080/01676830.2022.2104325. Online ahead of print.

ABSTRACT

PURPOSE: This study evaluates a web-based tool designed to augment telemedicine post-operative visits after periocular surgery.

METHODS: Adult, English-speaking patients undergoing periocular surgery with telemedicine follow-up were studied prospectively in this interventional case series. Participants submitted visual acuity measurements and photographs via a web-based tool prior to routine telemedicine post-operative visits. An after-visit survey assessed patient perceptions. Surgeons rated photographs and live video for quality and blurriness; external raters also evaluated photographs. Images were analyzed for facial centration, resolution, and algorithmically detected blur. Complications were recorded and graded for severity and relation to telemedicine.

RESULTS: Seventy-nine patients were recruited. Surgeons requested an in-person assessment for six patients (7.6%) due to inadequate evaluation by telemedicine. Surgeons rated patient-provided photographs to be of higher quality than live video at the time of the post-operative visit (p < 0.001). Image blur and resolution had moderate and weak correlation with photograph quality, respectively. A photograph blur detection algorithm demonstrated sensitivity of 85.5% and specificity of 75.1%. One patient experienced a wound dehiscence with a possible relationship to inadequate evaluation during telemedicine follow-up. Patients rated the telemedicine experience and their comfort with the structure of the visit highly.

CONCLUSIONS: Augmented telemedicine follow-up after oculofacial plastic surgery is associated with high patient satisfaction, rare conversion to clinic evaluation, and few related post-operative complications. Automated detection of image resolution and blur may play a role in screening photographs for subsequent iterations of the web-based tool.

PMID:35920583 | DOI:10.1080/01676830.2022.2104325