Background: Contamination of healthcare workers and patient environments likely play a role in the spread of antibiotic-resistant organisms. The mechanisms that contribute to the distribution of organisms within and between patient rooms are not well understood, but they may include movement patterns and patient interactions of healthcare workers. We used an innovative technology for tracking healthcare worker movement and patient interactions in ICUs. Methods: The Kinect system, a device developed by Microsoft, was used to detect the location of a person’s hands and head over time, each represented with 3-dimensional coordinates. The Kinects were deployed in 2 intensive care units (ICUs), at 2 different hospitals, and they collected data from 5 rooms in a high-acuity 20-bed cardiovascular ICU (unit 1) and 3 rooms in a 10-bed medical-surgical ICU (unit 2). The length of the Kinect deployment varied by room (range, 15–48 days). The Kinect data were processed to included date, time, and location of head and hands for all individuals. Based on the coordinates of the bed, we defined events indicating bed touch, distance 30 cm (1 foot) from the bed, and distance 1 m (3 feet) from the bed. The processed Kinect data were then used to generate heat maps showing density of person locations within a room and summarizing bed touches and time spent in different locations within the room. Results: The Kinect systems captured In total, 2,090 hours of room occupancy by at least 1 person within ~1 m of the bed (Table 1). Approximately half of the time spent within ~1 m from the bed was at the bedside (within ~30 cm). The estimated number of bed touches per hour when within ~1 m was 13–23. Patients spent more time on one side of the bed, which varied by room and facility (Fig. 1A, 1B). Additionally, we observed temporal variation in intensity measured by person time in the room (Fig. 1C, 1D). Conclusions: High occupancy tends to be on the far side (away from the door) of the patient bed where the computers are, and the bed touch rate is relatively high. These results can be used to help us understand the potential for room contamination, which can contribute to both transmission and infection, and they highlight critical times and locations in the room, with a potential for focused deep cleaning.

Funding: None

Disclosures: None