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Yanyan Bian* , MD; Yongbo Xiang* , MD; Bingdu Tong, MA; Bin Feng, MD; Xisheng Weng, MD
Department of Orthopedic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College,
Beijing, China
* these authors contributed equally
Corresponding Author:
Xisheng Weng, MD Department of Orthopedic Surgery Peking Union Medical College Hospital Chinese Academy of Medical Science and Peking Union Medical College No 1 Shuaifuyuan, Dongcheng District Beijing, 100073 China Phone: 86 13021159994
Email: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。
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.
(J Med Internet Res 2020;22(5):e16896) doi: 10.2196/16896
KEYWORDS
artificial intelligence; conversational agent; follow-up; cost-effectiveness
Yu-xuan Li1† , Chang-zheng He1† , Yi-chen Liu1† , Peng-yue Zhao1 , Xiao-lei Xu1 , Yu-feng Wang2 , Shao-you Xia1* and Xiao-hui Du1
Abstract
Background:The coronavirus disease 2019 (COVID-19) has been declared a global pandemic by the World Health Organization. Patients with cancer are more likely to incur poor clinical outcomes. Due to the prevailing pandemic, we propose some surgical strategies for gastric cancer patients.
Methods: The ‘COVID-19’ period was defined as occurring between 2020 and 01-20 and 2020-03-20. The enrolled patients were divided into two groups, pre-COVID-19 group (PCG) and COVID-19 group (CG). A total of 109 patients with gastric cancer were enrolled in this study.
Results: The waiting time before admission increased by 4 days in the CG (PCG: 4.5 [IQR: 2, 7.8] vs. CG: 8.0 [IQR: 2, 20]; p = 0.006). More patients had performed chest CT scans besides abdominal CT before admission during the COVID-19 period (PCG: 22 [32%] vs. CG: 30 [73%], p = 0.001). After admission during the COVID period, the waiting time before surgery was longer (PCG: 3[IQR: 2,5] vs. CG: 7[IQR: 5,9]; p < 0.001), more laparoscopic surgeries were performed (PCG: 51[75%] vs. CG: 38[92%], p = 0.021), and hospital stay period after surgery was longer (7[IQR: 6,8] vs.9[IQR:7,11]; p < 0.001). In addition, the total cost of hospitalization increased during this period, (PCG: 9.22[IQR: 7.82,10.97] vs. CG: 10.42[IQR:8.99,12.57]; p = 0.006).
Conclusion: This study provides an opportunity for our surgical colleagues to reflect on their own services and any contingency plans they may have to tackle the COVID-19 crisis.
Keywords: Gastric cancer, Coronavirus disease 2019, COVID-19, Retrospective analysis
Grace F. Chao, MD, MSc, Kathleen Y. Li, MD, MSc, [...], and Chad Ellimoottil, MD, MSc
Additional article information
Associated Data
- Supplementary Materials
Key Points
Question
What were telehealth use patterns across surgical specialties before and during the COVID-19 pandemic?
Findings
In this statewide cohort study that included 4405 surgeons, telehealth use grew substantially during the early pandemic period and declined during the later period; this use varied by surgical specialty. Compared with 2019 visit volume, telehealth salvaged only a small portion of 2020 surgical visits.
Meaning
Telehealth is being used in surgical fields at rates higher than before the pandemic, and its use varies across surgical specialties.
Chaitany Jayprakash Raorane †, Jin‐Hyung Lee † and Jintae Lee *
School of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Korea; 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (C.J.R.); 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (J.‐H.L.) * Correspondence: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。; Tel.: +82‐53‐810‐2533; Fax: +82‐53‐810‐4631 † These authors contributed equally to this work. Received: 23 June 2020; Accepted: 12 August 2020; Published: 14 August 2020
Abstract: Multi‐drug resistant Acinetobacter baumannii is well‐known for its rapid acclimatization in hospital environments. The ability of the bacterium to endure desiccation and starvation on dry surfaces for up to a month results in outbreaks of health care‐associated infections. Previously, indole and its derivatives were shown to inhibit other persistent bacteria. We found that among 16 halogenated indoles, 5‐iodoindole swiftly inhibited A. baumannii growth, constrained biofilm formation and motility, and killed the bacterium as effectively as commercial antibiotics such as ciprofloxacin, colistin, and gentamicin. 5‐Iodoindole treatment was found to induce reactive oxygen species, resulting in loss of plasma membrane integrity and cell shrinkage. In addition, 5‐iodoindole rapidly killed three Escherichia coli strains, Staphylococcus aureus, and the fungus Candida albicans, but did not inhibit the growth of Pseudomonas aeruginosa. This study indicates the mechanism responsible for the activities of 5‐iodoindole warrants additional study to further characterize its bactericidal effects on antibiotic‐resistant A. baumannii and other microbes.
Keywords: Acinetobacter baumannii; antibiotics; biofilm; 5‐iodoindole; membrane damage
