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降低医院噪音:医疗设备警报管理综述

Reducing hospital noise: a review of medical device alarm management.

作者信息

Konkani Avinash, Oakley Barbara, Bauld Thomas J

机构信息

Department of Industrial and Systems Engineering, Oakland University, Rochester, MI, USA.

出版信息

Biomed Instrum Technol. 2012 Nov-Dec;46(6):478-87. doi: 10.2345/0899-8205-46.6.478.

DOI:10.2345/0899-8205-46.6.478
PMID:23171194
Abstract

Increasing noise in hospital environments, especially in intensive care units (ICUs) and operating rooms (ORs), has created a formidable challenge for both patients and hospital staff. A major contributing factor for the increasing noise levels in these environments is the number of false alarms generated by medical devices. This study focuses on discovering best practices for reducing the number of false clinical alarms in order to increase patient safety and provide a quiet environment for both work and healing. The researchers reviewed Pub Med, Web of Knowledge and Google Scholar sources to obtain original journal research and review articles published through January 2012. This review includes 27 critically important journal articles that address different aspects of medical device alarms management, including the audibility, identification, urgency mapping, and response time of nursing staff and different solutions to such problems. With current technology, the easiest and most direct method for reducing false alarms is to individualize alarm settings for each patient's condition. Promoting an institutional culture change that emphasizes the importance of individualization of alarms is therefore an important goal. Future research should also focus on the development of smart alarms.

摘要

医院环境中不断增加的噪音,尤其是在重症监护病房(ICU)和手术室(OR),给患者和医院工作人员都带来了巨大挑战。这些环境中噪音水平增加的一个主要因素是医疗设备产生的误报数量。本研究专注于发现减少临床误报数量的最佳实践,以提高患者安全,并为工作和康复提供一个安静的环境。研究人员查阅了PubMed、Web of Knowledge和谷歌学术等资源,以获取截至2012年1月发表的原始期刊研究和综述文章。这篇综述包括27篇至关重要的期刊文章,这些文章涉及医疗设备警报管理的不同方面,包括可听性、识别、紧急程度映射以及护理人员的响应时间,以及针对此类问题的不同解决方案。就当前技术而言,减少误报最简便、最直接的方法是根据每个患者的病情个性化设置警报。因此,促进一种强调警报个性化重要性的机构文化变革是一个重要目标。未来的研究还应侧重于智能警报的开发。

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