基于大数据关联规则的急救站绩效考核研究

Research on Performance Assement in First-aid Station Based on Big Data and Association Rule

目的:综合考虑绩效考核的主客观评分,通过数据挖掘技术优化急救绩效考核方法,提高急救站的绩效水平。方法:对北京市朝阳区紧急医疗救援中心各下属急救站2018年绩效考核数据进行预处理后,采用数据挖掘算法中的Apriori算法挖掘出影响评委团综合打分的组合关系指标,对下属急救站的绩效考核项权重进行相应调整。结果:通过对绩效考核权重的修改,急救中心2019年的各项重要指标有明显提升。结论:通过Apriori关联规则算法可以优化绩效考核方法,解决传统绩效制度受固定权重与主观评分标准难以量化的问题,为急救站绩效考核指标优化提供依据。

Objective:To improve first-aid station's performance assessment by optimizing performance assessment method using data minging techniques and considering comprehensive acessment score.Methods:After pre-processing the 2018 performance assement data from Beijing Chaoyang first-aid stations which are supervised by Chaoyang center,applying the Apriori algorithm to find influencial factors for overall indicators score,and re-weight those factors accordingly.Results:The important indicators in the first-aid centers have been significantly improved in 2019.Conclusion:Apriori algorithm can avoid the constant-weight and subjective weighting problems in traditional performance assessment methods and provides valuable basis for the weights of factors in optimizing performance assessment.