摘要
目的通过数据科学的方法研究门诊患者的就诊时间序列及其影响因素,探索提高就诊效率的方法。方法利用python语言在Anconda 1.9.7下对某三甲专科医院2019年门诊进行分析,统计方法以p<0.05为有统计学意义,机器学习采用scikit-learn 0.22.1。结果患者就诊人数在一天内不同时间段的变化量有统计学意义,一周内及不同月份变化无统计学意义。患者就诊耗时在三种时间序列上均有统计学意义的变化。患者就诊耗时受多种因素的影响,随机森林得到的指标及其重要性指数分别为年龄(0.169)、当天就诊人数(0.140)、既往一年就诊次数(0.127)和就诊时刻(0.124)。结论患者就诊时间受较多因素的影响,在缺乏较大投入的情况下可以根据数据科学的结果进行门诊就诊流程改进,以期提高就诊效率。
Objective To study the outpatient visit time and its influencing factors by the method of data science,and to explore the ways to improve the efficiency of outpatient visits.Methods All the records of outpatient services in 2019 in a tertiary specialized hospital were analyzed by python under Anaconda 1.9.7.p<0.05 was considered statistically significant,and scikit-learn 0.22.1 was used for machine learning.Results The volume change among different periods within a day were statistically significant,but not among days within a week or months within a year.The time spent by the patients for outpatient visit did not change significantly in the above three time ranges.The time spent by the patients for outpatient visit was affected by several factors,including age(0.169),outpatient volume of the day(0.140),number of visits in the past year(0.127),and periods of visit(0.124).Conclusion The time spent for outpatient visit is affected by several factors.The efficiency of outpatient visits can be improved by optimizing the procedures of outpatient visits based on data science in the absence of large investment.
作者
颜勇卿
徐军
鲁秀明
YAN Yong-qing;XU Jun;LU Xiu-ming(Hwa Mei Hospital,University of Chinese Academy of Sciences;Ningbo Institute for Life and Health Industry,University of Chinese Academy of Sciences;Ningbo Women and Children’s Hospital)
出处
《医院管理论坛》
2021年第5期34-38,55,共6页
Hospital Management Forum
基金
浙江省医药卫生科技计划,编号:2018KY698。
关键词
门诊管理
流程
大数据
机器学习
Outpatient management
Procedure
Big data
Machine Learning