Machine learning-based prediction models for patients no-show in online outpatient appointments
With the development of information and communication technologies, all public tertiary hospitals in China began to use online outpatient appointment systems. However, the phenomenon of patient no-shows in online outpatient appointments is becoming more serious. The objective of this study is to des...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2021-06-01
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| Series: | Data Science and Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666764921000175 |
| _version_ | 1828086136454512640 |
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| author | Guorui Fan Zhaohua Deng Qing Ye Bin Wang |
| author_facet | Guorui Fan Zhaohua Deng Qing Ye Bin Wang |
| author_sort | Guorui Fan |
| collection | DOAJ |
| description | With the development of information and communication technologies, all public tertiary hospitals in China began to use online outpatient appointment systems. However, the phenomenon of patient no-shows in online outpatient appointments is becoming more serious. The 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 behavior. We used 382,004 original online outpatient appointment records, and divided the data set into a training set (N1 = 286,503), and a validation set (N2 = 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 appointments. The 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, respectively. In 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, respectively. This study demonstrates the possibility of using data from multiple sources to predict patient no-shows. The prediction model results can provide decision basis for hospitals to reduce medical resource waste, develop effective outpatient appointment policies, and optimize operations. |
| first_indexed | 2024-04-11T04:51:07Z |
| format | Article |
| id | doaj.art-771a69f224864c08b1fc23c30abc9ffc |
| institution | Directory Open Access Journal |
| issn | 2666-7649 |
| language | English |
| last_indexed | 2024-04-11T04:51:07Z |
| publishDate | 2021-06-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Data Science and Management |
| spelling | doaj.art-771a69f224864c08b1fc23c30abc9ffc2022-12-27T04:38:25ZengKeAi Communications Co. Ltd.Data Science and Management2666-76492021-06-0124552Machine learning-based prediction models for patients no-show in online outpatient appointmentsGuorui Fan0Zhaohua Deng1Qing Ye2Bin Wang3School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, 430074, Wuhan, ChinaSchool of Management, Huazhong University of Science and Technology, 430074, Wuhan, China; Corresponding author.School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, 430074, Wuhan, China; Department of Information Management, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430074, Wuhan, ChinaRobert C. Vackar College of Business and Entrepreneurship, University of Texas Rio Grande Valley, Edinburg, TX78539, USAWith the development of information and communication technologies, all public tertiary hospitals in China began to use online outpatient appointment systems. However, the phenomenon of patient no-shows in online outpatient appointments is becoming more serious. The 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 behavior. We used 382,004 original online outpatient appointment records, and divided the data set into a training set (N1 = 286,503), and a validation set (N2 = 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 appointments. The 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, respectively. In 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, respectively. This study demonstrates the possibility of using data from multiple sources to predict patient no-shows. The prediction model results can provide decision basis for hospitals to reduce medical resource waste, develop effective outpatient appointment policies, and optimize operations.http://www.sciencedirect.com/science/article/pii/S2666764921000175Online healthOnline outpatient appointmentPatient no-showPrediction modelMachine learning |
| spellingShingle | Guorui Fan Zhaohua Deng Qing Ye Bin Wang Machine learning-based prediction models for patients no-show in online outpatient appointments Data Science and Management Online health Online outpatient appointment Patient no-show Prediction model Machine learning |
| title | Machine learning-based prediction models for patients no-show in online outpatient appointments |
| title_full | Machine learning-based prediction models for patients no-show in online outpatient appointments |
| title_fullStr | Machine learning-based prediction models for patients no-show in online outpatient appointments |
| title_full_unstemmed | Machine learning-based prediction models for patients no-show in online outpatient appointments |
| title_short | Machine learning-based prediction models for patients no-show in online outpatient appointments |
| title_sort | machine learning based prediction models for patients no show in online outpatient appointments |
| topic | Online health Online outpatient appointment Patient no-show Prediction model Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666764921000175 |
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