eCite Digital Repository

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

Citation

Geilleit, R and Hen, ZQ and Chong, CY and Loh, AP and Pang, NL and Peterson, GM and Ng, KC and Huis, A and de Korne, DF, Feasibility of a real-time hand hygiene notification machine learning system in outpatient clinics, The Journal of hospital infection, 100, (2) pp. 183-189. ISSN 0195-6701 (2018) [Refereed Article]

Copyright Statement

Copyright 2018 The Healthcare Infection Society

DOI: doi:10.1016/j.jhin.2018.04.004

Abstract

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.

Item Details

Item Type:Refereed Article
Keywords:Hand hygiene, technology, real-time notification, infection control, automated compliance
Research Division:Medical and Health Sciences
Research Group:Clinical Sciences
Research Field:Infectious Diseases
Objective Division:Health
Objective Group:Clinical Health (Organs, Diseases and Abnormal Conditions)
Objective Field:Infectious Diseases
UTAS Author:Peterson, GM (Professor Gregory Peterson)
ID Code:128623
Year Published:2018
Deposited By:Pharmacy
Deposited On:2018-10-03
Last Modified:2019-02-22
Downloads:0

Repository Staff Only: item control page