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

Computers and Society 2017-07-11 v2 Machine Learning Applications

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 seem to exist among facilities.

Keywords

Cite

@article{arxiv.1705.03540,
  title  = {A Large-Scale Exploration of Factors Affecting Hand Hygiene Compliance Using Linear Predictive Models},
  author = {Michael T. Lash and Jason Slater and Philip M. Polgreen and Alberto M. Segre},
  journal= {arXiv preprint arXiv:1705.03540},
  year   = {2017}
}

Comments

Accepted to ICHI 2017. 8 pages

R2 v1 2026-06-22T19:42:21.831Z