摘要
目的:分析医院数字心电图机临床使用中的安全隐患,探讨医疗设备安全管理模式的应用价值。方法:选取医院在用的46台数字心电图机,按照随机方法将46台设备分为实验组与对照组,每组23台。实验组按照新建立的安全对策进行管理,对照组按照传统模式进行管理。采用单因素方差分析进行两组安全隐患差异性的检验。采用心电图机检定仪对其外观、内定标电压、输入电压范围、耐极化电压、加权系数误差、噪声、共模抑制比、频率响应、时间常数、波形识别能力与幅度-时间参数等10项指标进行计量检定,对其2015-2016年的使用安全问题数据进行统计,从电气安全、性能安全和洁净安全3个维度制定"1+3"安全管理模式。结果:实验组的月平均安全隐患为(1.65±0.982)例,对照组的月平均安全隐患为(6.70±1.820)例,采用单因素方差分析两组安全隐患的差异性,其差异性水平显著,有统计学意义(F=136.799,P<0.01)。结论:数字心电图机管理中采用"1+3"安全管理模式,可有效降低设备的安全隐患,提高临床服务水平,对医疗设备科学管理具有重要意义。
Objective: To analyze the potential safety hazard of used digital ECG machine in hospital so as to discuss the application value of safety management mode of medical equipment. Methods: The data of safety issues of used 46 digital ECG machines from 2015 to 2016 in hospital were statistically analyzed. The "1+3" safety management mode was formulated from three dimensions: electrical safety, performance safety and clean safety. According to the random method, 46 equipments were divided into experimental group and control group, with 23 sets for each group. The experimental group was managed according to the newly established safety countermeasures while the control group was managed according to the traditional way. One-way ANOVA was used to analyze the differences between the two groups. Result: The average monthly security risk in the experimental group and the control group were (1.65±0.982) cases and (6.70±1.820) cases respectively. The result of One-Way ANOVA showed that the differences of security risks between two groups was significant (F=136.799, P〈0.01). Conclusion: The "1+3" safety management mode that adopts digital ECG machine can effectively reduce the potential safety hazard of equipment, and enhance the clinical service level. It has great significance to the scientific management of medical equipment.
作者
吴翔
李信政
刘明艳
李俊
WU Xiang;LI Xin-zheng;LIU Ming-yan(Department of Electrocardiogram,The First People's Hospital of Neijiang,Neijiang 641000,China)
出处
《中国医学装备》
2018年第10期121-123,共3页
China Medical Equipment
关键词
心电图机
安全隐患
数据分析
设备检测
ECG
Safety hazard
Data analysis
Performance verification