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一种用于互联医疗企业中精确患者监测的算法策略。

An algorithm strategy for precise patient monitoring in a connected healthcare enterprise.

作者信息

Hu Xiao

机构信息

Department of Physiological Nursing, Department of Neurological Surgery, Bakar Computational Health Sciences Institue, UCB-UCSF Joint Bioengieering Graduate Program, University of California, San Francisco, San Francisco, CA 94122 USA.

出版信息

NPJ Digit Med. 2019 Apr 30;2:30. doi: 10.1038/s41746-019-0107-z. eCollection 2019.

Abstract

This perspective paper describes the building elements for realizing a precise patient monitoring algorithm to fundamentally address the alarm fatigue problem. Alarm fatigue is well recognized but no solution has been widely successful. Physiologic patient monitors are responsible for the lion's share of alarms at the bedside, most of which are either false or non-actionable. Algorithms on patient monitors lack precision because they fail to leverage multivariate relationship among variables monitored, to integrate rich patient clinical information from electronic health record system, and to utilize temporal patterns in data streams. Therefore, a solution to patient monitor alarm fatigue is to open the black-box of patient monitors to integrate physiologic data with clinical data from EHR under a four-element algorithm strategy to be described in this paper. This strategy will be presented in this paper in the context of its current status as described in our prior publications.

摘要

这篇观点论文描述了实现精确患者监测算法的构建要素,以从根本上解决警报疲劳问题。警报疲劳已得到广泛认可,但尚未有广泛成功的解决方案。生理患者监测仪产生了床边大部分警报,其中大多数要么是误报,要么是无需采取行动的警报。患者监测仪上的算法缺乏精确性,因为它们未能利用所监测变量之间的多变量关系,未能整合来自电子健康记录系统的丰富患者临床信息,也未能利用数据流中的时间模式。因此,解决患者监测仪警报疲劳的一个办法是打开患者监测仪的黑匣子,在本文所述的四要素算法策略下,将生理数据与来自电子健康记录的临床数据整合起来。本文将结合我们之前出版物中描述的当前状况来介绍这一策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b12/6550269/574f543036c9/41746_2019_107_Fig1_HTML.jpg

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