21 Million Opportunities: a 19 Facility Investigation of Factors Affecting Hand-Hygiene Compliance via Linear Predictive Models
- 35 Downloads
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.
KeywordsHand hygiene Predictive analytics Linear regression Marginal effects modeling Feature ranking
The authors would like to thank GOJO Industries, Inc. for access to the hand-hygiene data.
Compliance with Ethical Standards
Conflict of Interest
Philip M. Polgreen has received research funding from Company GOJO Industries, Inc. Author Jason Slater is an employee of GOJO Industries, Inc.
- 1.Klevens R, Edwards J, Richards C, Horan T (2007) Estimating health care-associated infections and deaths in US hospitals. Public Health 122:160–166Google Scholar
- 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):1175–1184CrossRefGoogle Scholar
- 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):133–141CrossRefGoogle Scholar
- 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):611–622CrossRefGoogle Scholar
- 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
- 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):1294–1297CrossRefGoogle Scholar
- 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.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 CrossRefGoogle Scholar
- 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.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.Draper NR, Smith H, Pownell E (1966) Applied regression analysis, vol 3. Wiley, New YorkGoogle Scholar
- 22.Quinlan JR (1992) Learning with continuous classes. In: 5th Australian Joint Conference on Artificial Intelligence, vol 92, pp 343–348Google Scholar
- 23.Johansson FD, Shalit U, Sontag D (2016) Learning representations for counterfactual inference. In: 33rd International Conference on Machine Learning (ICML)Google Scholar
- 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 296–304Google Scholar
- 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 1184–1193Google Scholar