The microbiology data represents the microorganisms recovered during the study period at the University of Utah hospital from samples collected from patients, environmental surfaces, and healthcare personnel (HCP) hands using premoistened sponges. Patient samples were collected daily from the axilla, groin, and perianal areas or stool. Environmental samples were collected daily from room surfaces and unit common areas (such as bed rails, overbed tables, door handles, computer keyboards, and other high-touch areas). HCP hands were periodically sampled upon HCP exit from a patient room after engaging in health care activities. Samples were collected from the 20-bed University of Utah Hospital Cardiovascular ICU (CVICU) over a 54 day period. The information from these datasets can be used to understand how different organisms appear and move throughout a hospital ward over a period of time.
This dataset contains room occupancy during the study period at University of Utah hospital. Admission, Discharge, and Transfer (ADT) data is captured in participating hospitals to characterize room occupancy and non-occupancy in wards. These data are pulled from multiple sources collected during the study by study staff as well as harvested EHR data. Data were adjudicated and compiled into one comprehensive file. Data manipulation included redaction of dates, replaced with study days 1-n, as well as transformation from long format to wide for ease of use.
The objective of using the wireless sensors was to improve understanding of the heterogeneity of healthcare worker (HCW) contact with patients and the physical environment in patients’ rooms. The framework and design were based on contact networks with a) nodes defined by HCW’s, rooms, and items in the room and b) edges defined by HCW’s in the room, near the bed, and touching items. Nodes had characteristics of HCW role and room number. Edges had characteristics of day, start time, and duration. Thus, patterns and heterogeneity could be understood within contexts of time, space, roles, and patient characteristics. At the University of Utah Hospital Cardiovascular ICU (CVICU), a 20-bed unit, we collected data for 54 days. HCW contact with patients was measured using wireless sensors to capture time spent in patient rooms as well as time spent near the patient bed. HCW contact with the physical environment was measured using wireless sensors on the following items in patient rooms: door, sink, toilet, over-bed table, keyboard, vital signs monitor touchscreen, and cart. HCW’s clipped a sensor to their clothing or lanyard. This dataset contains cleaned sensor pings of RFD reads between healthcare worker worn sensors and environmental sensors placed in facility using methods described in the "Data Cleaning Steps" section.
The objective of using the wireless sensors was to improve understanding of the heterogeneity of healthcare worker (HCW) contact with patients and the physical environment in patients’ rooms. The framework and design were based on contact networks with a) nodes defined by HCW’s, rooms, and items in the room and b) edges defined by HCW’s in the room, near the bed, and touching items. Nodes had characteristics of HCW role and room number. Edges had characteristics of day, start time, and duration. Thus, patterns and heterogeneity could be understood within contexts of time, space, roles, and patient characteristics. At the University of Utah Hospital Cardiovascular ICU (CVICU), a 20-bed unit, we collected data for 54 days. HCW contact with patients was measured using wireless sensors to capture time spent in patient rooms as well as time spent near the patient bed. HCW contact with the physical environment was measured using wireless sensors on the following items in patient rooms: door, sink, toilet, over-bed table, keyboard, vital signs monitor touchscreen, and cart. HCW’s clipped a sensor to their clothing or lanyard.