Authors:
Peng Zhang
1
;
Marcelino Rodriguez-Cancio
2
;
Douglas Schmidt
1
;
Jules White
1
and
Tom Dennis
3
Affiliations:
1
Vanderbilt University, United States
;
2
University of Rennes 1, France
;
3
ZH Solutions, United States
Keyword(s):
Machine Learning, Deep Learning, Data Mining, Hospital and Healthcare, Hand Hygiene Compliance, Re-admissions.
Abstract:
Hospital Acquired Infections (HAIs) are a global concern as they impose significant economic consequences
on the healthcare systems. In the U.S. alone, HAIs have cost hospitals an estimated $9.8 billion a year. An
effective measure to reduce the spread of HAIs is for Health Care Workers (HCWs) to comply with recommended
hand hygiene (HH) guidelines. Unfortunately, HH guideline compliance is currently poor, forcing
hospitals to implement controls. The current standard for monitoring compliance is overt direct observation
of hand sanitation of HCWs by trained observers, which can be time-consuming, costly, biased, and sporadic.
Our research describes a hand hygiene compliance monitoring app, Hygiene Police (HyPo), that can be deployed
as a service to alleviate the manual effort, reduce errors, and improve existing compliance monitoring
practice. HyPo exploits machine learning analyses of handwashing compliance data from a 30-bed intensive
care unit to predict future compliance characte
ristics. Based on the results, HyPo then provides HWCs with
timely feedback and augments the current monitoring approach to improve compliance.
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