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Feasibility of a real-time hand hygiene notification machine learning system in outpatient clinics

  • Author Footnotes
    † Joint first authors.
    R. Geilleit
    Footnotes
    † Joint first authors.
    Affiliations
    Medical Innovation and Care Transformation, KK Women's and Children's Hospital, SingHealth Duke – NUS Academic Medical Centre, Singapore

    Radboud Institute for Health Sciences, Scientific Centre for Quality of Healthcare (IQ Healthcare), Radboud University Medical Centre, Nijmegen, The Netherlands
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  • Author Footnotes
    † Joint first authors.
    Z.Q. Hen
    Footnotes
    † Joint first authors.
    Affiliations
    Medical Innovation and Care Transformation, KK Women's and Children's Hospital, SingHealth Duke – NUS Academic Medical Centre, Singapore
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  • C.Y. Chong
    Affiliations
    Infectious Diseases, Department of Paediatrics, KK Women's and Children's Hospital, Singapore

    Paediatrics Academic Medical Program, Duke–NUS Medical School, Singapore

    Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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  • A.P. Loh
    Affiliations
    Department of Biomedical Engineering, National University Singapore, Singapore
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  • N.L. Pang
    Affiliations
    Quality, Safety and Risk Management, KK Women's and Children's Hospital, Singapore
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  • G.M. Peterson
    Affiliations
    Health Services Innovation, School of Medicine, University of Tasmania, Australia
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  • K.C. Ng
    Affiliations
    Medical Innovation and Care Transformation, KK Women's and Children's Hospital, SingHealth Duke – NUS Academic Medical Centre, Singapore
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  • A. Huis
    Affiliations
    Radboud Institute for Health Sciences, Scientific Centre for Quality of Healthcare (IQ Healthcare), Radboud University Medical Centre, Nijmegen, The Netherlands
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  • D.F. de Korne
    Correspondence
    Corresponding author. Address: Medical Innovation and Care Transformation, KK Women's and Children's Hospital, 100 Bukit Timah Road, 229899, Singapore. Tel.: +65 63552550.
    Affiliations
    Medical Innovation and Care Transformation, KK Women's and Children's Hospital, SingHealth Duke – NUS Academic Medical Centre, Singapore

    Erasmus School of Health Policy and Management, Erasmus University Rotterdam, The Netherlands

    Health Services and Systems Research, Duke–NUS Medical School, Singapore
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  • Author Footnotes
    † Joint first authors.
Published:April 09, 2018DOI:https://doi.org/10.1016/j.jhin.2018.04.004

      Summary

      Background

      Various technologies have been developed to improve hand hygiene (HH) compliance in inpatient settings; however, little is known about the feasibility of machine learning technology for this purpose in outpatient clinics.

      Aim

      To assess the effectiveness, user experiences, and costs of implementing a real-time HH notification machine learning system in outpatient clinics.

      Methods

      In our mixed methods study, a multi-disciplinary team co-created an infrared guided sensor system to automatically notify clinicians to perform HH just before first patient contact. Notification technology effects were measured by comparing HH compliance at baseline (without notifications) with real-time auditory notifications that continued till HH was performed (intervention I) or notifications lasting 15 s (intervention II). User experiences were collected during daily briefings and semi-structured interviews. Costs of implementation of the system were calculated and compared to the current observational auditing programme.

      Findings

      Average baseline HH performance before first patient contact was 53.8%. With real-time auditory notifications that continued till HH was performed, overall HH performance increased to 100% (P < 0.001). With auditory notifications of a maximum duration of 15 s, HH performance was 80.4% (P < 0.001). Users emphasized the relevance of real-time notification and contributed to technical feasibility improvements that were implemented in the prototype. Annual running costs for the machine learning system were estimated to be 46% lower than the observational auditing programme.

      Conclusion

      Machine learning technology that enables real-time HH notification provides a promising cost-effective approach to both improving and monitoring HH, and deserves further development in outpatient settings.

      Keywords

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