Artificial Intelligence–Assisted System in Postoperative Follow-up of Orthopedic Patients: Exploratory Quantitative and Qualitative Study
BackgroundPatient follow-up is an essential part of hospital ward management. With the development of deep learning algorithms, individual follow-up assignments might be completed by artificial intelligence (AI). We developed an AI-assisted follow-up conversational agent that can simulate the human...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2020-05-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | http://www.jmir.org/2020/5/e16896/ |
| _version_ | 1819280256503119872 |
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| author | Bian, Yanyan Xiang, Yongbo Tong, Bingdu Feng, Bin Weng, Xisheng |
| author_facet | Bian, Yanyan Xiang, Yongbo Tong, Bingdu Feng, Bin Weng, Xisheng |
| author_sort | Bian, Yanyan |
| collection | DOAJ |
| description | BackgroundPatient follow-up is an essential part of hospital ward management. With the development of deep learning algorithms, individual follow-up assignments might be completed by artificial intelligence (AI). We developed an AI-assisted follow-up conversational agent that can simulate the human voice and select an appropriate follow-up time for quantitative, automatic, and personalized patient follow-up. Patient feedback and voice information could be collected and converted into text data automatically.
ObjectiveThe primary objective of this study was to compare the cost-effectiveness of AI-assisted follow-up to manual follow-up of patients after surgery. The secondary objective was to compare the feedback from AI-assisted follow-up to feedback from manual follow-up.
MethodsThe AI-assisted follow-up system was adopted in the Orthopedic Department of Peking Union Medical College Hospital in April 2019. A total of 270 patients were followed up through this system. Prior to that, 2656 patients were followed up by phone calls manually. Patient characteristics, telephone connection rate, follow-up rate, feedback collection rate, time spent, and feedback composition were compared between the two groups of patients.
ResultsThere was no statistically significant difference in age, gender, or disease between the two groups. There was no significant difference in telephone connection rate (manual: 2478/2656, 93.3%; AI-assisted: 249/270, 92.2%; P=.50) or successful follow-up rate (manual: 2301/2478, 92.9%; AI-assisted: 231/249, 92.8%; P=.96) between the two groups. The time spent on 100 patients in the manual follow-up group was about 9.3 hours. In contrast, the time spent on the AI-assisted follow-up was close to 0 hours. The feedback rate in the AI-assisted follow-up group was higher than that in the manual follow-up group (manual: 68/2656, 2.5%; AI-assisted: 28/270, 10.3%; P<.001). The composition of feedback was different in the two groups. Feedback from the AI-assisted follow-up group mainly included nursing, health education, and hospital environment content, while feedback from the manual follow-up group mostly included medical consultation content.
ConclusionsThe effectiveness of AI-assisted follow-up was not inferior to that of manual follow-up. Human resource costs are saved by AI. AI can help obtain comprehensive feedback from patients, although its depth and pertinence of communication need to be improved. |
| first_indexed | 2024-12-24T00:40:54Z |
| format | Article |
| id | doaj.art-bb351d9e82da4643a3adbbed2f2fcb93 |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-12-24T00:40:54Z |
| publishDate | 2020-05-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-bb351d9e82da4643a3adbbed2f2fcb932022-12-21T17:23:58ZengJMIR PublicationsJournal of Medical Internet Research1438-88712020-05-01225e1689610.2196/16896Artificial Intelligence–Assisted System in Postoperative Follow-up of Orthopedic Patients: Exploratory Quantitative and Qualitative StudyBian, YanyanXiang, YongboTong, BingduFeng, BinWeng, XishengBackgroundPatient follow-up is an essential part of hospital ward management. With the development of deep learning algorithms, individual follow-up assignments might be completed by artificial intelligence (AI). We developed an AI-assisted follow-up conversational agent that can simulate the human voice and select an appropriate follow-up time for quantitative, automatic, and personalized patient follow-up. Patient feedback and voice information could be collected and converted into text data automatically. ObjectiveThe primary objective of this study was to compare the cost-effectiveness of AI-assisted follow-up to manual follow-up of patients after surgery. The secondary objective was to compare the feedback from AI-assisted follow-up to feedback from manual follow-up. MethodsThe AI-assisted follow-up system was adopted in the Orthopedic Department of Peking Union Medical College Hospital in April 2019. A total of 270 patients were followed up through this system. Prior to that, 2656 patients were followed up by phone calls manually. Patient characteristics, telephone connection rate, follow-up rate, feedback collection rate, time spent, and feedback composition were compared between the two groups of patients. ResultsThere was no statistically significant difference in age, gender, or disease between the two groups. There was no significant difference in telephone connection rate (manual: 2478/2656, 93.3%; AI-assisted: 249/270, 92.2%; P=.50) or successful follow-up rate (manual: 2301/2478, 92.9%; AI-assisted: 231/249, 92.8%; P=.96) between the two groups. The time spent on 100 patients in the manual follow-up group was about 9.3 hours. In contrast, the time spent on the AI-assisted follow-up was close to 0 hours. The feedback rate in the AI-assisted follow-up group was higher than that in the manual follow-up group (manual: 68/2656, 2.5%; AI-assisted: 28/270, 10.3%; P<.001). The composition of feedback was different in the two groups. Feedback from the AI-assisted follow-up group mainly included nursing, health education, and hospital environment content, while feedback from the manual follow-up group mostly included medical consultation content. ConclusionsThe effectiveness of AI-assisted follow-up was not inferior to that of manual follow-up. Human resource costs are saved by AI. AI can help obtain comprehensive feedback from patients, although its depth and pertinence of communication need to be improved.http://www.jmir.org/2020/5/e16896/ |
| spellingShingle | Bian, Yanyan Xiang, Yongbo Tong, Bingdu Feng, Bin Weng, Xisheng Artificial Intelligence–Assisted System in Postoperative Follow-up of Orthopedic Patients: Exploratory Quantitative and Qualitative Study Journal of Medical Internet Research |
| title | Artificial Intelligence–Assisted System in Postoperative Follow-up of Orthopedic Patients: Exploratory Quantitative and Qualitative Study |
| title_full | Artificial Intelligence–Assisted System in Postoperative Follow-up of Orthopedic Patients: Exploratory Quantitative and Qualitative Study |
| title_fullStr | Artificial Intelligence–Assisted System in Postoperative Follow-up of Orthopedic Patients: Exploratory Quantitative and Qualitative Study |
| title_full_unstemmed | Artificial Intelligence–Assisted System in Postoperative Follow-up of Orthopedic Patients: Exploratory Quantitative and Qualitative Study |
| title_short | Artificial Intelligence–Assisted System in Postoperative Follow-up of Orthopedic Patients: Exploratory Quantitative and Qualitative Study |
| title_sort | artificial intelligence assisted system in postoperative follow up of orthopedic patients exploratory quantitative and qualitative study |
| url | http://www.jmir.org/2020/5/e16896/ |
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