Artificial intelligence-assisted system in postoperative follow-up of orthopedic patients: exploratory quantitative and qualitative study
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Abstract
BACKGROUND: Patient 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. OBJECTIVE: The 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. METHODS: The 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. RESULTS: There 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. CONCLUSIONS: The 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.
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From Artificial intelligence in nursing: an integrative review of clinical and operational impacts (2025) · cites this paper as [37]
Bian et al. (37) evaluated an AI-assisted postoperative follow-up system for orthopedic patients, which reduced time spent on follow-ups through automation.
“Bian et al. (37) evaluated an AI-assisted postoperative follow-up system for orthopedic patients, which reduced time spent on follow-ups through automation.”
From Artificial intelligence in nursing: an integrative review of clinical and operational impacts (2025) · cites this paper as [37]
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- Authors
- Bian, Y., Xiang, Y., Tong, B., Feng, B., & Weng, X.
- Affiliations
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Corresponding author
- Weng, Xisheng
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2020-05-26
- Volume
- 22
- Issue
- 5
- Pages
- e16896
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC7284488
- OpenAlex
- W3015682012
- Cited by
- 84
- References
- 18
- Citations by year
- 2026: 12 · 2025: 23 · 2024: 19 · 2023: 14 · 2022: 10 · 2021: 5 · 2018: 1
- FWCI
- 2.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Health Sciences › Health Professions › Emergency Medical Services › Healthcare Operations and Scheduling Optimization
- Keywords
- artificial intelligence, postoperative follow-up, orthopedic patients, chatbots, speech synthesis, cost-effectiveness, follow-up rates, patient feedback, hospital ward management, deep learning, health education, hospital environment, patient satisfaction
- MeSH terms
- Artificial Intelligence, Evaluation Studies as Topic, Follow-Up Studies, Humans, Orthopedics, Postoperative Period, Qualitative Research
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