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识别 COVID-19 早期报告不良事件中的行为变化:以色列的门诊护理视角。

Identification of behavioural changes in reporting adverse events early during COVID-19: An ambulatory care perspective in Israel.

机构信息

Industrial Engineering and Management Department, Ariel University, Ariel, Israel.

Daniel D. Burke Chair for Exceptional Faculty, Professor and University Distinguished Teaching Scholar, Management Information Systems, Kansas State University, Manhattan, Kansas, USA.

出版信息

Int J Health Plann Manage. 2023 Sep;38(5):1314-1329. doi: 10.1002/hpm.3654. Epub 2023 May 16.

Abstract

This study examined adverse event reporting centred on three significant dates in the months before the pandemic arrived in Israel. On these dates, broad media coverage exposed citizens and health care providers with indications about the forthcoming pandemic. The current study tracked whether parameters related to reporting adverse medical events provided early indications that a large crisis was unfolding. The method for analysing the data was based on a statistical test called Regression Discontinuity Design, which helped identify parameters related to medical reporting patterns which significantly changed. The examination indicated nurses' reports were unique in relation to others and indicated three phases: (1) upon declaration of the upcoming pandemic, there was a rise in reporting; (2) when the disease was named, there was moderation and maintenance in a steady quantity of reports, and finally, (3) when the first case arrived in Israel, a slight decrease in reporting began. Nurses' behaviours manifested as changes in reporting patterns. In this process of increase, moderation and decrease, it can be concluded that these are three stages that may characterise the beginning of a large event. The research method presented reinforces the need for forming tools by which significant events such as the COVID-19 pandemic can be identified quickly, and aid in proper planning of resources, optimise staffing and maximise utilization of the health systems.

摘要

本研究以大流行抵达以色列之前几个月的三个重要日期为中心,考察了不良事件报告情况。在这些日期,广泛的媒体报道使公民和医疗保健提供者了解到即将发生的大流行的相关信息。本研究旨在追踪与报告不良医疗事件相关的参数是否提供了即将发生重大危机的早期迹象。分析数据的方法基于一种称为回归不连续设计的统计检验,该检验有助于确定与医疗报告模式显著变化相关的参数。检查表明,护士的报告与其他报告有所不同,表明存在三个阶段:(1)宣布即将发生大流行时,报告数量增加;(2)当疾病被命名时,报告数量适度且保持稳定;最后,(3)当首例病例抵达以色列时,报告数量开始略有下降。护士的行为表现为报告模式的变化。在这种增加、适度和减少的过程中,可以得出结论,这些可能是大事件开始的三个阶段。所提出的研究方法强化了制定工具的必要性,以便能够快速识别 COVID-19 大流行等重大事件,并有助于合理规划资源、优化人员配置和最大化利用卫生系统。

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