医疗不良事件报告体系评述

医疗不良事件报告体系对于促进患者安全、提高医疗质量具有重要作用,但各国的报告体系模式有所不同.借鉴国外报告体系的成功经验,为建立和完善适合我国国情的医疗不良事件报告体系提供理论依据.     

医疗不良事件报告系统利用现状调查

目的了解我国医疗不良事件报告系统的利用现状。方法采用分层随机抽样的方法,调查山东省6个样本市30家二级、三级医院对原卫生部和中国医院协会报告系统的利用上报情况。结果有41.38%的医院表示利用过原卫生部的报告系统,13.79%的医院表示原卫生部的2个报告系统都利用过,只有10.34%的表示原卫生部的2个报告系统和中国医院协会的报告系统都用过。报告的数量也十分有限,基本都是在个位数。结论现有的不良事件报告系统利用率不足,可以通过完善报告系统和反馈机制、加强政策执行力等方式,提高不良事件报告系统利用率和不良事件报告率。     

不良事件报告系统医务人员使用影响因素分析

目的了解本院医务人员主动使用不良事件报告系统的现状,为完善不良事件报告系统提供依据。方法采用自行设计的问卷,对本院747名医务人员进行无记名调查。结果医务人员对主动上报不良事件和对不良事件报告系统的使用均不够满意;64.41%的人员认为本院不良事件上报率<50%;不愿意上报不良事件中19.93%的因为工作繁忙,65.30%的担心处罚或责备。仅19.74%的人员登陆过不良事件上报系统;39.49%的人员认为系统应改为匿名上报;42.56%的人员认为系统操作繁琐。结论医务人员工作繁忙和担心处罚或责备,是影响不良事件上报率的主要因素;加大宣传培训和改进不良事件上报系统,是提高不良事件上报系统使用率的关键因素。     

手术室非惩罚性护理不良事件报告制度的建立

手术室护理不良事件发生率影响因素较多,建立非惩罚性护理不良事件报告制度是非常有效的控制不良事件的通过在本院手术室建立非惩罚性护理不良事件报告制度,分析上报的护理不良事件发生的根本原因并提出修正方案及预防措施,提升护理质量,完善护理流程及管理制度。     

不良事件报告系统在精神科护理管理中的应用

目的探析在精神科护理管理中不良事件报告系统的应用价值。方法将2017年3月-2018年3月该科实施不良事件报告系统前选取的54例患者纳入A组;将2018年5月-2019年5月实施不良事件报告系统后选择的58例患者纳入B组。比较两组不良事件发生状况及护理质量与护理满意度。结果 B组不良事件总发生率为5.17%,显著低于A组的18.52%(P<0.05);B组护理质量、护理满意度评分明显高于A组(P<0.05)。结论不良事件报告系统在精神科护理管理中的应用价值较为显著,值得推广。     

我国医疗不良事件报告系统研究与应用现状述评

医疗不良事件报告是防范医疗不良事件重复发生、提高医疗质量的一个重要措施.从我国医疗不良事件报告系统的政策和制度入手,对我国现有医疗不良事件报告系统进行概述和分析,总结其特点与缺陷,并对完善我国医疗不良事件报告系统提出了展望.     

Safety monitoring in the Vaccine Adverse Event Reporting System (VAERS)

The Centers for Disease Control and Prevention (CDC) and the U.S. Food and Drug Administration (FDA) conduct post-licensure vaccine safety monitoring using the Vaccine Adverse Event Reporting System (VAERS), a spontaneous (or passive) reporting system. This means that after a vaccine is approved, CDC and FDA continue to monitor safety while it is distributed in the marketplace for use by collecting and analyzing spontaneous reports of adverse events that occur in persons following vaccination. Various methods and statistical techniques are used to analyze VAERS data, which CDC and FDA use to guide further safety evaluations and inform decisions around vaccine recommendations and regulatory action. VAERS data must be interpreted with caution due to the inherent limitations of passive surveillance. VAERS is primarily a safety signal detection and hypothesis generating system. Generally, VAERS data cannot be used to determine if a vaccine caused an adverse event. VAERS data interpreted alone or out of context can lead to erroneous conclusions about cause and effect as well as the risk of adverse events occurring following vaccination. CDC makes VAERS data available to the public and readily accessible online.We describe fundamental vaccine safety concepts, provide an overview of VAERS for healthcare professionals who provide vaccinations and might want to report or better understand a vaccine adverse event, and explain how CDC and FDA analyze VAERS data. We also describe strengths and limitations, and address common misconceptions about VAERS. Information in this review will be helpful for healthcare professionals counseling patients, parents, and others on vaccine safety and benefit-risk balance of vaccination.     

军队药品不良反应监测管理系统在我院的应用

目的：为了更好、更准确地记录和管理ADR信息。方法：通过军队药品不良反应监测管理系统与HIS的数据交换,实现ADR数据的连续自动采集、网络实时传输,给出分析评价和监测预警。结果：ADR系统已完全覆盖了以前的手工流程。结论：通过应用军队药品不良反应监测管理系统,加强了某院药品不良反应监测和管理工作,提高了监测工作效率和质量。     

Post-licensure surveillance of trivalent adjuvanted influenza vaccine (aIIV3; Fluad), Vaccine Adverse Event Reporting System (VAERS), United States,July 2016–June 2018

Background

Trivalent adjuvanted influenza vaccine (aIIV3; Fluad®) was approved in the United States (U.S.) in 2015 for adults aged ≥65?years and has been in use since the 2016–17 influenza season.

Mixture drug‐count response model for the high‐dimensional drug combinatory effect on myopathy

Drug‐drug interactions (DDIs) are a common cause of adverse drug events (ADEs). The electronic medical record (EMR) database and the FDA's adverse event reporting system (FAERS) database are the major data sources for mining and testing the ADE associated DDI signals. Most DDI data mining methods focus on pair‐wise drug interactions, and methods to detect high‐dimensional DDIs in medical databases are lacking. In this paper, we propose 2 novel mixture drug‐count response models for detecting high‐dimensional drug combinations that induce myopathy. The “count” indicates the number of drugs in a combination. One model is called fixed probability mixture drug‐count response model with a maximum risk threshold (FMDRM‐MRT). The other model is called count‐dependent probability mixture drug‐count response model with a maximum risk threshold (CMDRM‐MRT), in which the mixture probability is count dependent. Compared with the previous mixture drug‐count response model (MDRM) developed by our group, these 2 new models show a better likelihood in detecting high‐dimensional drug combinatory effects on myopathy. CMDRM‐MRT identified and validated (54; 374; 637; 442; 131) 2‐way to 6‐way drug interactions, respectively, which induce myopathy in both EMR and FAERS databases. We further demonstrate FAERS data capture much higher maximum myopathy risk than EMR data do. The consistency of 2 mixture models' parameters and local false discovery rate estimates are evaluated through statistical simulation studies.