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A Large-Scale Exploration of Factors Affecting Hand Hygiene Compliance Using Linear Predictive Models
Conference proceeding   Open access

A Large-Scale Exploration of Factors Affecting Hand Hygiene Compliance Using Linear Predictive Models

Michael T Lash, Jason Slater, Philip M Polgreen and Alberto M Segre
2017 IEEE International Conference on Healthcare Informatics (ICHI), pp.66-73
08/2017
DOI: 10.1109/ICHI.2017.12
url
https://arxiv.org/pdf/1705.03540View
Open Access

Abstract

This large-scale study, consisting of 24.5 million hand hygiene opportunities spanning 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 residents in predicting daily hand hygiene compliance. 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.
Event detection Hand hygiene Linear regression Medical services Meteorology Observers Picture archiving and communication systems Public healthcare Radiation detectors Supervised learning Urban areas

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