Feasibility of a real-time hand hygiene notification machine learning system in outpatient clinics
Feasibility of a real-time hand hygiene notification machine learning system in outpatient clinics
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DOI：
10.1016/j.jhin.2018.04.004
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发表时间：
2018-10-01
影响因子：
6.9
通讯作者：
de Korne, D. F.
中科院分区：
文献类型：
--
作者：
Geilleit, R.;Hen, Z. Q.;de Korne, D. F.
背景资料：已经开发了各种技术来提高住院患者的手部卫生（HH）合规性;然而，机器学习技术在门诊诊所中用于此目的的可行性知之甚少。目的：评估在门诊诊所实施实时HH通知机器学习系统的有效性、用户体验和成本。方法：在我们的混合方法研究中，一个多学科团队共同创建了一个红外引导传感器系统，可以在首次接触患者之前自动通知临床医生进行HH。通过比较基线时的HH依从性（无通知）与持续至HH执行（干预I）或持续15 s的通知（干预II）的实时听觉通知来测量通知技术效果。在每日简报和半结构化访谈中收集用户体验。该系统的实施成本进行了计算和比较，目前的观察auditing programme.Findings：平均基线HH性能第一次患者接触前为53.8%。使用持续到HH执行的实时听觉通知，总体HH性能增加到100%（P < 0.001）。听觉提示最长持续时间为15 s时，HH性能为80.4%（P < 0.001）。用户强调了实时通知的相关性，并为原型中实施的技术可行性改进做出了贡献。机器学习系统的年度运行成本估计为46%，低于观察auditing programme.Conclusion：机器学习技术，使实时HH通知提供了一个有前途的成本效益的方法来改善和监测HH，值得进一步发展门诊设置。(C)2018年，卫生保健感染协会。由爱思唯尔有限公司出版。保留所有权利。
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. (C) 2018 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.