[1]朱 光,邓弘林.大数据背景下医院门诊挂号预约爽约行为预测研究[J].医学信息,2020,33(22):13-15，21.[doi:10.3969/j.issn.1006-1959.2020.22.004]
　ZHU Guang,DENG Hong-lin.An Investigation of Predicting Patient Missing Appointment Behavior Under the Big Data Background[J].Medical Information,2020,33(22):13-15，21.[doi:10.3969/j.issn.1006-1959.2020.22.004]
点击复制

大数据背景下医院门诊挂号预约爽约行为预测研究()
分享到：

医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
33卷
期数:
2020年22期
页码:
13-15，21
栏目:
出版日期:
2020-11-15

文章信息/Info

Title:
An Investigation of Predicting Patient Missing Appointment Behavior Under the Big Data Background
文章编号:
1006-1959（2020）22-0013-04
作者:
朱 光; 邓弘林
（1.沧州市中心医院宣传策划部，河北 沧州 061000；2.中山大学管理学院，广东 广州 510000）
Author(s):
ZHU Guang; DENG Hong-lin
(1.Department of Publicity and Planning,Cangzhou Central Hospital, Cangzhou 061000,Hebei,China;2.School of Business,Sun Yat-sen University,Guangzhou 510000,Guangdong,China)
关键词:
预约诊疗; 爽约率; 机器学习; 大数据
Keywords:
Appointment of diagnosis and treatment; Missing-appointmentrate; Machine learning; Big data
分类号:
R197.3
DOI:
10.3969/j.issn.1006-1959.2020.22.004
文献标志码:
B
摘要:
目的 了解目前医院预约诊疗服务中患者爽约行为的现状，探讨和鉴别患者爽约的关键特征，运用这些特征建立机器学习算法模型预测未来患者爽约行为。方法 挖掘2018年河北省某大型三甲医院预约大数据，首先用Stata采取传统Logistic回归找出患者爽约的显著因子，再将数据划分为训练集和预测集，采用SVM、决策树、随机森林和BP神经网络等不同模型学习训练患者爽约行为和特征，检验每种算法对患者爽约预测的准确率。结果 目前医院患者预约爽约率为16.16%，Logistic回归分析显示年龄、性别、预约时间和预约科室是爽约行为的关键性特征；使用这些特征进行机器学习和预测能取得较好效果，SVM、决策树、随机森林和BP神经网络各个算法准确率均超过75%，其中SVM和BP神经网络准确率最高，是该特定情境下的最优算法。结论 我国大型三甲医院预约诊疗服务有待进一步加强，在大数据时代的背景下，机器学习方法可为医院预测并降低爽约率提供强有力支持。
Abstract:
Objectives To understand the current situation of patients’missing-appointment behavior in the appointment service of hospitals; to explore and identify the key features of patients’missing appointment.Use these features to build a machine learning algorithm model to predict future patient missing-appointment behavior. Methods Mining the big data of appointments in a large tertiary hospital in Hebei Province in 2018. First, Stata adopts traditional Logistic regression to find the significant factors of patients’ appointments, and then divides the data into training sets and prediction sets, using SVM, decision tree, random forest and BP Different models, such as neural networks, learn and train patients’ absentee behavior and characteristics, and test the accuracy of each algorithm in predicting patient absenteeism. Results The current appointment rate of hospital patients is 16.16%. Logistic regression analysis shows that age, gender, appointment time and appointment department are the key features of appointment cancellation behavior; using these features for machine learning and prediction can achieve better results, SVM, decision tree accuracy of each algorithm of random forest and BP neural network exceeds 75%. Among them, SVM and BP neural network have the highest accuracy, which is the best algorithm in this specific situation. Conclusion The appointment diagnosis and treatment services of my country’s large tertiary hospitals need to be further strengthened. In the context of the era of big data, machine learning methods can provide strong support for hospitals to predict and reduce the rate of missing-appointment.

参考文献/References:

[1]喻锎.探索精准预约服务建立通畅就医流程--武汉市第一医院预约诊疗服务实践和探索[J].中国医院管理,2019,459(10):2-3.[2]Kogan S,Moskowitz TJ,Niessner M.Fake News:Evidence from Financial Markets[D].SSRN Electronic Journal,2018.[3]黄洛.医院门诊预约挂号爽约的现状及对策[J].现代医院,2019,19(4):63-66.[4]陈默,蔡苗,黄阿红,等.基于K-means聚类与支持向量机的大病患者住院费用影响因素与控制策略研究[J].中国医院管理,2019,39(5):45-47.[5]吴越,徐丛剑,程子桐,等.二值响应模型与决策树在门诊失约行为研究中的应用[J].中国医院管理,2018,38(10):36-38.[6]易焱琪,鞠水,家晓艳,等.浅析BP神经网络技术在医院信息系统中的应用[J].科技创新与生产力,2017(2):107-109.[7]周奇.医院门诊预约系统的优化分析：基于国内某大型医院的研究[D].中国科学技术大学,2017.[8]周萍,冯笑,赵岭,等.医院预约挂号爽约现象的调查分析[J].中医药管理杂志,2018,26(18):29-31.[9]刘玉琦,郝晓刚,马亚飞.某三级医院预约挂号爽约情况及其原因调查[J].武警医学,2018,29(2):117-119.

相似文献/References:

[1]王聪华,吴 帅,廖 娟.医院门诊预约挂号调查分析与对策研究[J].医学信息,2021,34(17):143.[doi:10.3969/j.issn.1006-1959.2021.17.038]
　WANG Cong-hua,WU Shuai,LIAO Juan.Investigation,Analysis and Countermeasures of Appointment Registration in Hospital Outpatient Clinics[J].Medical Information,2021,34(22):143.[doi:10.3969/j.issn.1006-1959.2021.17.038]
[2]袁 方,任海玲,雷 蓝,等.门诊患者对医院多途径预约挂号服务使用现状分析[J].医学信息,2023,36(21):31.[doi:10.3969/j.issn.1006-1959.2023.21.007]
　YUAN Fang,REN Hai-ling,LEI Lan,et al.Analysis on the Status Quo of the Use of Hospital Multi-way Appointment Registration Service by Outpatients[J].Medical Information,2023,36(22):31.[doi:10.3969/j.issn.1006-1959.2023.21.007]

更新日期/Last Update: 1900-01-01