Discussions of a Preliminary Hand Hygiene Compliance Monitoring Application-as-a-service

Peng Zhang, Marcelino Rodriguez-Cancio, Douglas Schmidt, Jules White, Tom Dennis


Hospital Acquired Infections (HAIs) are a global concern as they impose significant economic consequences on the healthcare systems. In the U.S. alone, HAIs have cost hospitals an estimated $9.8 billion a year. An effective measure to reduce the spread of HAIs is for Health Care Workers (HCWs) to comply with recommended hand hygiene (HH) guidelines. Unfortunately, HH guideline compliance is currently poor, forcing hospitals to implement controls. The current standard for monitoring compliance is overt direct observation of hand sanitation of HCWs by trained observers, which can be time-consuming, costly, biased, and sporadic. Our research describes a hand hygiene compliance monitoring app, Hygiene Police (HyPo), that can be deployed as a service to alleviate the manual effort, reduce errors, and improve existing compliance monitoring practice. HyPo exploits machine learning analyses of handwashing compliance data from a 30-bed intensive care unit to predict future compliance characteristics. Based on the results, HyPo then provides HWCs with timely feedback and augments the current monitoring approach to improve compliance.


  1. Armellino, D., Trivedi, M., Law, I., Singh, N., Schilling, M. E., Hussain, E., and Farber, B. (2013). Replicating changes in hand hygiene in a surgical intensive care unit with remote video auditing and feedback. American Journal of Infection Control, 41(10):925-927.
  2. Boyce, J. M., Cooper, T., and Dolan, M. J. (2009). Evaluation of an Electronic Device for Real-Time Measurement of Alcohol-Based Hand Rub Use. Infection Control & Hospital Epidemiology, 30(11):1090-1095.
  3. Davis, C. R. (2010). Infection-free surgery: how to improve hand-hygiene compliance and eradicate methicillinresistant Staphylococcus aureus from surgical wards. Annals of the Royal College of Surgeons of England, 92(4):316-319.
  4. Eckmanns, T., Bessert, J., Behnke, M., Gastmeier, P., and RĂ¼den, H. (2006). Compliance with antiseptic hand rub use in intensive care units the hawthorne effect. Infection Control, 27(09):931-934.
  5. Edmond, M. B., Goodell, A., Zuelzer, W., Sanogo, K., Elam, K., and Bearman, G. (2010). Successful use of alcohol sensor technology to monitor and report hand hygiene compliance. The Journal of Hospital Infection, 76(4):364-365.
  6. EFORE, B. (2009). Hand hygiene technical reference manual.
  7. Ellingson, K., Haas, J. P., Aiello, A. E., Kusek, L., Maragakis, L. L., Olmsted, R. N., Perencevich, E., Polgreen, P. M., Schweizer, M. L., Trexler, P., VanAmringe, M., and Yokoe, D. S. (2014). Strategies to Prevent Healthcare-Associated Infections through Hand Hygiene. Infection Control and Hospital Epidemiology, 35(8):937-960.
  8. Ellison, R. T., Barysauskas, C. M., Rundensteiner, E. A., Wang, D., and Barton, B. (2015). A Prospective Controlled Trial of an Electronic Hand Hygiene Reminder System. Open Forum Infectious Diseases, page ofv121.
  9. Fakhry, M., Hanna, G. B., Anderson, O., Holmes, A., and Nathwani, D. (2012). Effectiveness of an audible reminder on hand hygiene adherence. American Journal of Infection Control, 40(4):320-323.
  10. Gardner, W. A. (1984). Learning characteristics of stochastic-gradient-descent algorithms: A general study, analysis, and critique. Signal Processing, 6(2):113-133.
  11. Glorot, X. and Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In Aistats, volume 9, pages 249-256.
  12. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley.
  13. Gould, D. J., Drey, N. S., and Creedon, S. (2011). Routine hand hygiene audit by direct observation: has nemesis arrived? The Journal of Hospital Infection, 77(4):290-293.
  14. Graves, A. (2012). Supervised sequence labelling. In Supervised Sequence Labelling with Recurrent Neural Networks, pages 5-13. Springer.
  15. Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar):1157-1182.
  16. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter, 11(1):10-18.
  17. John, G. H. and Langley, P. (1995). Estimating continuous distributions in bayesian classifiers. In Eleventh Conference on Uncertainty in Artificial Intelligence , pages 338-345, San Mateo. Morgan Kaufmann.
  18. Kohavi, R. and John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence , 97(1-2):273- 324. Special issue on relevance.
  19. Marra, A. R., Guastelli, L. R., Arajo, C. M. P. d., Santos, J. L. S. d., Lamblet, L. C. R., Silva, M., Lima, G. d., Cal, R. G. R., Paes, n. T., Neto, M. C., Barbosa, L., Edmond, M. B., and Santos, O. F. P. d. (2010). Positive Deviance A New Strategy for Improving Hand Hygiene Compliance. Infection Control & Hospital Epidemiology, 31(1):12-20.
  20. Marra, A. R., Sampaio Camargo, T. Z., Magnus, T. P., Blaya, R. P., Dos Santos, G. B., Guastelli, L. R., Rodrigues, R. D., Prado, M., Victor, E. d. S., Bogossian, H., Monte, J. C. M., dos Santos, O. F. P., Oyama, C. K., and Edmond, M. B. (2014). The use of real-time feedback via wireless technology to improve hand hygiene compliance. American Journal of Infection Control, 42(6):608-611.
  21. Morgan, D. J., Pineles, L., Shardell, M., Young, A., Ellingson, K., Jernigan, J. A., Day, H. R., Thom, K. A., Harris, A. D., and Perencevich, E. N. (2012). Automated hand hygiene count devices may better measure compliance than human observation. American Journal of Infection Control, 40(10):955-959.
  22. Platt, J. et al. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines.
  23. Sahud, A. G. and Bhanot, N. (2009). Measuring hand hygiene compliance: a new frontier for improving hand hygiene. Infection Control and Hospital Epidemiology, 30(11):1132.
  24. Schum, D. A. (1994). The evidential foundations of probabilistic reasoning. Northwestern University Press.
  25. Shrestha, S. K., Sunkesula, V. C., Kundrapu, S., Tomas, M. E., Nerandzic, M. M., and Donskey, C. J. (2016). Acquisition of clostridium difficile on hands of healthcare personnel caring for patients with resolved c. difficile infection. Infection Control & Hospital Epidemiology, 37(04):475-477.
  26. Sickbert-Bennett, E. E., DiBiase, L. M., Schade Willis, T. M., Wolak, E. S., Weber, D. J., and Rutala, W. A. (2016). Reducing health careassociated infections by implementing a novel all hands on deck approach for hand hygiene compliance. American Journal of Infection Control, 44(5, Supplement):e13-e16.
  27. Team, D. D. (2016). Deeplearning4j: Open-source distributed deep learning for the jvm. Apache Software Foundation License, 2.
  28. Ward, M. A., Schweizer, M. L., Polgreen, P. M., Gupta, K., Reisinger, H. S., and Perencevich, E. N. (2014). Automated and electronically assisted hand hygiene monitoring systems: A systematic review. American Journal of Infection Control, 42(5):472-478.
  29. WHO (2009). A guide to the implementation of the WHO multimodal hand hygiene improvement strategy.
  30. Zhang, P., White, J., Schmidt, D., and Dennis, T. (2016). A preliminary study of hand hygiene compliance characteristics with machine learning methods. (ISIS-16- 101).

Paper Citation

in Harvard Style

Zhang P., Rodriguez-Cancio M., Schmidt D., White J. and Dennis T. (2017). Discussions of a Preliminary Hand Hygiene Compliance Monitoring Application-as-a-service . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: SmartMedDev, (BIOSTEC 2017) ISBN 978-989-758-213-4, pages 537-544. DOI: 10.5220/0006293705370544

in Bibtex Style

author={Peng Zhang and Marcelino Rodriguez-Cancio and Douglas Schmidt and Jules White and Tom Dennis},
title={Discussions of a Preliminary Hand Hygiene Compliance Monitoring Application-as-a-service},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: SmartMedDev, (BIOSTEC 2017)},

in EndNote Style

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: SmartMedDev, (BIOSTEC 2017)
TI - Discussions of a Preliminary Hand Hygiene Compliance Monitoring Application-as-a-service
SN - 978-989-758-213-4
AU - Zhang P.
AU - Rodriguez-Cancio M.
AU - Schmidt D.
AU - White J.
AU - Dennis T.
PY - 2017
SP - 537
EP - 544
DO - 10.5220/0006293705370544