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Machine learning-based prediction models for patients no-show in online outpatient appointments

Data Science and Management 2021年第2期2卷 45-52页

作者： Guorui Fan Zhaohua Deng Qing Ye Bin Wang School of Medicine and Health Management Tongji Medical CollegeHuazhong University of Science and Technology430074WuhanChina School of Management Huazhong University of Science and Technology430074WuhanChina Department of Information Management Tongji HospitalTongji Medical CollegeHuazhong University of Science and Technology430074WuhanChina Robert C.Vackar College of Business and Entrepreneurship University of Texas Rio Grande ValleyEdinburgTX78539USA

With the development of information and communication technologies,all public tertiary hospitals in China began to use online outpatient appointment ***,the phenomenon of patient no-shows in online outpatient appointments is becoming more *** objective of this study is to design a prediction model for patient no-shows,thereby assisting hospitals in making relevant decisions,and reducing the probability of patient no-show *** used 382,004 original online outpatient appointment records,and divided the data set into a training set(N_(1)=286,503),and a validation set(N_(2)=95,501).We used machine learning algorithms such as logistic regression,k-nearest neighbor(KNN),boosting,decision tree(DT),random forest(RF)and bagging to design prediction models for patient no-show in online outpatient *** patient no-show rate of online outpatient appointment was 11.1%(N=42,224).From the validation set,bagging had the highest area under the ROC curve and AUC value,which was 0.990,followed by random forest and boosting models,which were 0.987 and 0.976,*** contrast,compared with the previous prediction models,the area under ROC and AUC values of the logistic regression,decision tree,and k-nearest neighbors were lower at 0.597,0.499 and 0.843,*** study demonstrates the possibility of using data from multiple sources to predict patient *** prediction model results can provide decision basis for hospitals to reduce medical resource waste,develop effective outpatient appointment policies,and optimize operations.

关键词： Online health Online outpatient appointment patient no-show Prediction model Machine learning

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