21 Million Opportunities: a 19 Facility Investigation of Factors Affecting Hand-Hygiene Compliance via Linear Predictive Models

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.

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Notes

  1. 1.

    Practically speaking, these sensors can be fit to any sort of patient entrance/exit area, as depicted in Fig. 2.

  2. 2.

    Note that both the LASSO [24] and Elastic Net [25] would have also made appropriate supporting methods.

  3. 3.

    We calculated the VIF (variance inflation factor) values of our proposed features to determine whether multi-collinearity exists in our data. We found that VIF values > 5, indicating multi-collinearity, were found only among binarized facility indicator variables, but not among any of our defined features. This result is not unexpected as many co-occurring “0”s are to be expected among facility indicator variables.

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Acknowledgements

The authors would like to thank GOJO Industries, Inc. for access to the hand-hygiene data.

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Correspondence to Michael T. Lash.

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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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Lash, M.T., Slater, J., Polgreen, P.M. et al. 21 Million Opportunities: a 19 Facility Investigation of Factors Affecting Hand-Hygiene Compliance via Linear Predictive Models. J Healthc Inform Res 3, 393–413 (2019). https://doi.org/10.1007/s41666-019-00048-1

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Keywords

  • Hand hygiene
  • Predictive analytics
  • Linear regression
  • Marginal effects modeling
  • Feature ranking