基于关联规则的门诊药房布局优化

Optimizing Layouts of Outpatient Pharmacy Based on Association Rules

【目的】随着门诊的日就诊人数逐渐增多,优化门诊药房药品摆放布局,能够有效提高整个药房系统的服务效率。【方法】选择处方数量最多的两个科室的处方数据,应用K-means聚类算法将数据集划分为4个子数据集,使用Apriori算法对4个子数据集进行关联规则挖掘,得到31条药品有效规则和18条药类有效规则。【结果】综合药类和药品有效规则中挖掘出的信息,结合国家药品储存陈列规范,在得到某医院门诊药房的药房管理专家认可的情况下,设计出药类和药品的大致布局。【局限】只提取两个科室的处方数据,用于关联规则分析的处方数据不够完善。【结论】将关联规则方法和K-means聚类算法应用于解决门诊药房的药品陈列布局问题,用数据支撑药品陈列布局设计,并得到药房专家的认可。有利于减轻药剂师的工作强度,缩短患者取药时间,提高整个药房的服务效率。

[Objective] As the number of outpatient visits increases, optimizing the layout of pharmacy drugs can improve its service efficiency. [Methods] Firstly, we chose two departments with the largest number of prescriptions, which were divided into four sub groups with the K-means clustering method. Then, we used Apriori algorithm to explore the association rules among them. Finally, we obtained 31 effective drug layout rules and 18 effective drug class rules. [Results] We designed general layout rules for prescription drugs based on the collected data along with national drug storage and display standards, which were approved by the experts. [Limitations] We only studied prescription records from two departments, which might not yield the best association rules. [Conclusions] The proposed method could reduce the workload of pharmacists and the waiting time of patients, which improve the pharmacy services.