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Quantifying surface contamination in a primary care clinic using 3D gestural human-computer interaction
Kylie H Dowers1, Katherina A Jurewicz1
1School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK, USA.
Background:
Healthcare-associated infections (HAIs) are a risk to patient safety and health. Several initiatives have been studied to reduce surface contamination; however, there is lack of work investigating emerging technologies such as 3D gestural human-computer interaction (HCI).
Aim/Objective:
This study investigates a touchless 3D gestural display and traditional HCI to quantify the differences in surface contamination.
Methods:
A between-subjects experimental study was performed to study the spread of surface contamination in a simulated primary care clinic. Participants (N = 30) wore gloves with a blacklight-sensitive powder that would identify what surfaces were contaminated. The number of surfaces of contamination was collected for all participants as well as overall process times.
Findings/Results:
The findings showed that participants who used gestural technology spread contamination to significantly less surfaces that the traditional HCI group (p < .05). However, the gestural group took a significantly longer time to complete the process than the HCI group (p < .05).
Discussion:
The implementation of 3D gestural HCI has the opportunity to reduce surface contamination and potentially reduce the risk of HAIs. Because the process was found to take longer with the 3D gestural technology, this technology could affect overall healthcare processes. Future work should investigate how the technology should be developed for use in healthcare systems as well as explore other high-risk areas in healthcare.
