21 Million Opportunities: a 19 Facility Investigation of Factors Affecting Hand-Hygiene Compliance via Linear Predictive Models
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This large-scale study, consisting of 21.3 million hand-hygiene opportunities from 19 distinct facilities in 10 different states, uses linear predictive models to expose factors that may affect hand-hygiene compliance. We examine the use of features such as temperature, relative humidity, influenza severity, day/night shift, federal holidays, and the presence of new medical residents in predicting daily hand-hygiene compliance; the investigation is undertaken using both a “global” model to glean general trends and facility-specific models to elicit facility-specific insights. The results suggest that colder temperatures and federal holidays have an adverse effect on hand-hygiene compliance rates, and that individual cultures and attitudes regarding hand hygiene exist among facilities.
KeywordsHand hygiene Predictive analytics Linear regression Marginal effects modeling Feature ranking
The authors would like to thank GOJO Industries, Inc. for access to the hand-hygiene data.
Compliance with Ethical Standards
Conflict of Interest
Philip M. Polgreen has received research funding from Company GOJO Industries, Inc. Author Jason Slater is an employee of GOJO Industries, Inc.
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