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


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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  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.


  1. 1.

    Klevens R, Edwards J, Richards C, Horan T (2007) Estimating health care-associated infections and deaths in US hospitals. Public Health 122:160–166

    Google Scholar 

  2. 2.

    Roberts R, Scott R, Hota B, Kampe L, Abbasi F, Schabowski S, Ahmad I, Ciavarella G, Cordell R, Solomon S, Hagtvedt R, Weinstein R (2010) Costs attributable to healthcare-acquired infection in hospitalized adults and a comparison of economic methods. Med Care 48(11):1026–1035

    Article  Google Scholar 

  3. 3.

    Roberts R, Hota B, Ahmad I, Scott R, Foster S, Abbasi F, Schabowski S, Kampe L, Ciavarella G, Supino M, Naples J, Cordell R, Levy S, Weinstein R (2009) Hospital and societal costs of antimicrobial-resistant infection in a Chicago teaching hospital: implications for antibiotic stewardship. Clin Infect Dis 49(8):1175–1184

    Article  Google Scholar 

  4. 4.

    Boyce JM, Pittet D (2002) Guidelines for hand hygiene in health-care settings: recommendations of the Healthcare Infection Control Practices Advisory Committee and the HICPAC/SHEA/APIC/IDSA Hand Hygiene Task Force. Infect Control Hosp Epidemiol 23:S3–S41

    Article  Google Scholar 

  5. 5.

    Allegranzi B, Sax H, Bengaly L, Richet H, Minta D, Chraiti M, Sokona F, Gayet-Ageron A, Bonnabry P, Pittet D (2010) World Health Organization “Point G” Project Management Committee. Successful implementation of the World Health Organization hand hygiene improvement strategy in a referral hospital in Mali, Africa. Infect Control Hosp Epidemiol 31(2):133–141

    Article  Google Scholar 

  6. 6.

    Pittet D, Allegranzi B, Boyce J (2009) World Health Organization world alliance for patient safety first global patient safety challenge core group of experts. The World Health Organization guidelines on hand hygiene in health care and their consensus recommendations. Infect Control Hosp Epidemiol 30(7):611–622

    Article  Google Scholar 

  7. 7.

    Hass JP, Larson EL (2007) Measurement of compliance with hand hygiene. J Hosp Infect 66:6–14

    Article  Google Scholar 

  8. 8.

    Boyce JM, Cooper T, Dolan MJ (2009) Evaluation of an electronic device for real-time measurement of alcohol-based hand rub use. Infect Control Hosp Epidemiol 30(11):1090–1095

    Article  Google Scholar 

  9. 9.

    Joint Commission of Accreditation of Healthcare Organizations, “Patient safety goals”. Joint Commission of Accreditation of Healthcare Organizations, Tech. Rep. [Online]. Available: http://www.jcaho.org/accredited+organizations/patient+safety/npsg.htm

  10. 10.

    Fries J, Segre A, Thomas G, Herman T, Ellingson K, Polgreen P (2012) Monitoring hand hygiene via human observers: how should we be sampling?. Infect Control Hosp Epidemiol 33(7):689–695. [PMID: 22669230]

    Article  Google Scholar 

  11. 11.

    Sharma D, Thomas G, Foster E, Iacovelli J, Lea K, Streit J, Polgreen P (2012) The precision of human-generated hand-hygiene observations: a comparison of human observation with an automated monitoring system. Infect Control Hosp Epidemiol 33(12):1259–1261

    Article  Google Scholar 

  12. 12.

    Eckmanns T, Bessert J, Behnke M, Gastmeier HP, Ruden A (2006) Compliance with antiseptic hand rub use in intensive care units: the Hawthorne effect. Infect Control Hosp Epidemiol 27:931–934

    Article  Google Scholar 

  13. 13.

    Monsalve M, Pemmaraju S, Thomas G, Herman T, Segre PAM, Polgreen A (2014) Do peer effects improve hand hygiene adherence among healthcare workers?. Infect Control Hosp Epidemiol 35(10):1277–1285

    Article  Google Scholar 

  14. 14.

    Boscart V, McGilton K, Levchenko A, Hufton G, Holliday P, Fernie G (2008) Acceptability of a wearable hand hygiene device with monitoring capabilities. J Hosp Infect 70(3):216–222

    Article  Google Scholar 

  15. 15.

    Venkatesh A, Lankford M, Rooney D, Blachford T, Watts C, Noskin G (2008) Use of electronic alerts to enhance hand hygiene compliance and decrease transmission of vancomycin-resistant enterococcus in a hematology unit. Am J Infect Control 36(3):199–205

    Article  Google Scholar 

  16. 16.

    Polgreen PM, Hlady CS, Severson M. a., Segre AM, Herman T (2010) Method for automated monitoring of hand hygiene adherence without radio-frequency identification. Infection Control and Hospital Epidemiology : The Official Journal of the Society of Hospital Epidemiologists of America 31(12):1294–1297

    Article  Google Scholar 

  17. 17.

    Lash MT, Slater J, Polgreen PM, Segre AM A large-scale exploration of factors affecting hand hygiene compliance using linear predictive models. In: Healthcare informatics, 2017 IEEE International Conference on (ICHI), 2017, pp 66–73. [Online]. Available: http://ieeexplore.ieee.org/document/8031133/

  18. 18.

    Dai H, Milkman KL, Hofmann DA, Staats BR (2014) The impact of time at work and time off from work on rule compliance: the case of hand hygiene in healthcare. J Appl Psychol 100(3):846–862. [Online]. Available: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2423009

    Article  Google Scholar 

  19. 19.

    Jarrin Tejada C, Bearman G (2015) Hand hygiene compliance monitoring: the state of the art. Current Infectious Disease Reports, vol. 17, no. 4, [Online]. Available: http://link.springer.com/10.1007/s11908-015-0470-0

  20. 20.

    Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell S, Saha S, White G, Zhu Y, Leetmaa A, Reynolds R, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo K, Ropelewski C, Wang J, Jenne R, Joseph D The NCEP/NCAR 40-Year Reanalysis Project. pp 437–471, 1996. [Online]. Available: https://doi.org/10.1175/1520-0477(1996)077>0437:TNYRP<2.0.CO;2

  21. 21.

    Draper NR, Smith H, Pownell E (1966) Applied regression analysis, vol 3. Wiley, New York

    Google Scholar 

  22. 22.

    Quinlan JR (1992) Learning with continuous classes. In: 5th Australian Joint Conference on Artificial Intelligence, vol 92, pp 343–348

  23. 23.

    Johansson FD, Shalit U, Sontag D (2016) Learning representations for counterfactual inference. In: 33rd International Conference on Machine Learning (ICML)

  24. 24.

    Tibshirani R (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), pp. 267–288

    MathSciNet  MATH  Google Scholar 

  25. 25.

    Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B (Stat Methodol) 67(2):301–320

    MathSciNet  Article  Google Scholar 

  26. 26.

    Robnik-Šikonja M, Kononenko I (1997) An adaptation of relief for attribute estimation in regression. In: Machine Learning: Proceedings of the Fourteenth International Conference (ICML97), pp 296–304

  27. 27.

    Kira K, Rendell LA (1992) A practical approach to feature selection. In: Proceedings of the Ninth International Workshop on Machine Learning, pp 249–256

    Chapter  Google Scholar 

  28. 28.

    Williams R, et al. (2012) Using the margins command to estimate and interpret adjusted predictions and marginal effects. The Stata Journal 12(2):308

    Article  Google Scholar 

  29. 29.

    Lash MT, Lin Q, Street NW, Robinson JG, Ohlmann J (2017) Generalized inverse classification. In: Proceedings of the 2017 SIAM International Conference on Data Mining (SDM’17), pp 162–170. [Online]. Available: https://doi.org/10.1137/1.9781611974973.19

    Chapter  Google Scholar 

  30. 30.

    Lash MT, Lin Q, Street WN, Robinson J (2017) A budget constrained inverse classification framework for smooth classifiers. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), pp 1184–1193

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