中国医疗管理科学2024，Vol.14Issue(1) ：81-87.DOI:10.3969/j.issn.2095-7432.2024.01.015

基于数据挖潜的精神科护理不良事件分析

Adverse events in psychiatric nursing:an analysis based on data mining

唐迎雪 王梦佳 张芮 王娟 卢庆华
中国医疗管理科学2024，Vol.14Issue(1) ：81-87.DOI:10.3969/j.issn.2095-7432.2024.01.015
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  • NETL
  • NSTL
  • 维普
  • 万方数据

基于数据挖潜的精神科护理不良事件分析

Adverse events in psychiatric nursing:an analysis based on data mining

唐迎雪 1王梦佳 1张芮 2王娟 3卢庆华2
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作者信息

  • 1. 261000 山东潍坊市,山东第二医科大学
  • 2. 山东省精神卫生中心
  • 3. 济宁医学院
  • 折叠

摘要

目的 基于数据挖潜思路对数据进行回顾性分析,旨在发现精神科护理不良事件的相关因素和变量之间的关联规则,为拟定精神科护理安全管理策略和风险预警机制提供科学依据.方法 运用Apriori算法,以支持度、置信度和提升度为指标,对 2016 年—2021 年某精神病专科医院上报的 684 例护理不良事件的影响因素变量进行关联挖掘.结果 设置最低条件支持度为 5,最小规则置信度为 85%,最大前项数为 3,得到关联规则共 90 条,经专业判断,最终选择 16 条关联.其中,科室、护士班次和当班人力、患者总数、患者年龄、性别和住院天数均与跌倒/坠床不良事件有关联;患者总数、精神症状、住院天数、性别和当班护士年龄与暴力攻击不良事件关联;患者的精神症状与自杀地点有关联.结论 采用数据挖潜技术,可以深入分析精神科护理不良事件相关因素之间的关联规则,发现精神科护理不良事件发生的规律和特点,为针对性地开展精神科护理风险管理提供科学依据;针对不良事件的关联规则拟定风险预警机制,对于保障患者安全、减少精神科护士职业伤害有一定指导意义.

Abstract

Objective To identify the association rules between factors and variables of adverse events(AEs)in psychiatric nursing through data mining,so as to provide a scientific basis for the development of safety management strategies and risk early-warning mechanisms in psychiatric nursing.Methods Factors and variables of 684 AEs in psychiatric nursing in a mental health center from 2016 to 2021 were analyzed using the Apriori algorithm,with support,confidence and lift as indicators.Results With a minimum conditional support of 5,a minimum rule confidence of 85%,and a maximum number of antecedents of 3,and a total of 90 association rules were obtained,and 16 were finally selected after professional judgment.Among them,department,nurse shift/on-duty manpower,total number of patients,patient age,gender,and length of hospital stay were associated with falls/bed falls;total number of patients,psychiatric symptoms,length of hospital stay,gender,and age of on-duty nurses were associated with violent assaults;and psychiatric symptoms were associated with suicide sites.Conclusions The data mining technology can be used to thoroughly analyze the association rules between the factors associated with AEs in psychiatric nursing,discover the rules and characteristics of psychiatric nursing AEs,and provide a scientific basis for the targeted development of psychiatric nursing risk management,which are valuable for ensuring patient safety and reducing occupational injuries of psychiatric nurses.

关键词

精神科/护理不良事件/关联规则/Apriori算法

Key words

Psychiatry/Nursing adverse events/Association rules/Apriori algorithm

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基金项目

山东省重点研发计划(软科学研究计划)(2020RKB14062)

出版年

2024
中国医疗管理科学

中国医疗管理科学

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