期刊
JOURNAL OF HEALTHCARE INFORMATICS RESEARCH
卷 3, 期 4, 页码 393-413出版社
SPRINGERNATURE
DOI: 10.1007/s41666-019-00048-1
关键词
Hand hygiene; Predictive analytics; Linear regression; Marginal effects modeling; Feature ranking
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.
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