引用本文:徐 婷,张 妍,李 莉,等.[cn_title][J].[journal_cn_name],2019,[volume]([issue]):655-658.[点击复制]
Xu Ting,Zhang Yan,Li Li,et al.[en_title][J].[journal_en_name],2019,[volume]([issue]):655-658.[点击复制]
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基于信息化手段探索建立医院辅助用药廉政风险防控预警体系△
徐 婷,张 妍,李 莉,杨 玲,张 辉,周 军,周瑞珏
0
(常州市第一人民医院、苏州大学附属第三医院纪委监察室;医务处;院长室)
摘要:
目的:探索通过信息化手段,建立医院辅助用药廉政风险防控预警体系。方法:我院于2016年10月起通过信息化手段,多个职能科室联动建立医院合理用药廉政风险防控预警体系,比较干预前后全院每季度用药金额前十位及出现异动的辅助用药金额及比例并进行医嘱点评。结果:辅助用药的使用金额及比例明显下降,医嘱点评提示辅助用药使用趋于合理,廉政风险降低。结论:建立基于信息化手段的医院用药廉政风险防控体系,有助于辅助用药的合理使用。
关键词:  辅助用药  信息化  廉政风险  预警
DOI:
基金项目:江苏现代医院管理研究基金三类课题(JSY-3-2018-086)
Establishment of the early warning system of clean government risk prevention and control of hospital adjuvant drug use based on information technology△
Xu Ting,Zhang Yan,Li Li,Yang Ling,Zhang Hui,Zhou Jun,Zhou Ruijue
(Discipline Inspection Commission Supervision Office,First People’s Hospital of Changzhou,Third Affiliated Hospital of Soochow University;Medical Affair Office;President Office)
Abstract:
Objective:to establish an early warning system of clean government risk prevention and control of auxiliary drug use in hospital by means of informatization. Methods:Since October 2016,our hospital established an early warning system for the prevention and control of the risk of clean government in rational drug use in the hospital by means of information technology and the linkage of multiple functional departments. This study compared the use amounts of the top 10 drugs before and after the intervention and compared the proportion of different auxiliary drugs before and after the intervention.The doctor’s orders were commented. Results:The amount and proportion of adjuvant drugs were significantly decreased,and the comments on doctor’s orders indicated that the adjuvant drugs were more reasonable and the risk of clean government was reduced. Conclusions:The establishment clean government risk prevention and control system of hospital drug use based on information means is helpful to the rational use of auxiliary drugs.
Key words:  adjuvant drug  information  clean government risk  early warning

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