| Literature DB >> 35415431 |
Michael T Lash1, Jason Slater2, Philip M Polgreen3, Alberto M Segre4.
Abstract
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. © Springer Nature Switzerland AG 2019.Entities:
Keywords: Feature ranking; Hand hygiene; Linear regression; Marginal effects modeling; Predictive analytics
Year: 2019 PMID: 35415431 PMCID: PMC8982796 DOI: 10.1007/s41666-019-00048-1
Source DB: PubMed Journal: J Healthc Inform Res ISSN: 2509-498X