文献精选
Francis Lovecchio1 · Grant J. Riew2 · Dino Samartzis3,4 · Philip K. Louie5 · Niccole Germscheid6 · Howard S. An3,4 ·
Jason Pui Yin Cheung7 · Norman Chutkan8 · Gary Michael Mallow3,4 · Marko H. Neva9 · Frank M. Phillips3,4 ·
Daniel M. Sciubba10 · Mohammad El‑Sharkawi11 · Marcelo Valacco12 · Michael H. McCarthy13 · Melvin C. Makhni2 ·
Sravisht Iyer1
1 Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA
2 Department of Orthopaedic Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
3 Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
4 The International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
5 Neuroscience Institute, Virginia Mason Medical Center, Seattle, WA, USA
6 Research Department, AO Spine International, Davos, Switzerland
7 Department of Orthopaedics & Traumatology, The University of Hong Kong, Hong Kong SAR, China
8 Department of Orthopaedic Surgery, University of Arizona College of Medicine, Phoenix, AZ, USA
9 Department of Orthopaedic and Trauma Surgery, Tampere University Hospital, Tampere, Finland
10 Department of Neurosurgery, Baltimore, MD, USA, John Hopkins University, Baltimore, MD, USA
11 Department of Orthopaedic and Trauma Surgery, Assiut University Medical School, Assiut, Egypt
12 Department of Orthopaedics, Churruca Hospital de Buenos Aires, Buenos Aires, Argentina
13 Indiana Spine Group, Carmel, IN, USA
Received: 22 September 2020 / Accepted: 27 October 2020 / Published online: 22 November 2020
© The Author(s) 2020
Abstract
Purpose To utilize data from a global spine surgeon survey to elucidate (1) overall confidence in the telemedicine evaluation and (2) determinants of provider confidence.
Methods Members of AO Spine International were sent a survey encompassing participant’s experience with, perception of,and comparison of telemedicine to in-person visits. The survey was designed through a Delphi approach, with four rounds of
question review by the multi-disciplinary authors. Data were stratified by provider age, experience, telemedicine platform, trust in telemedicine, and specialty.
Results Four hundred and eighty-five surgeons participated in the survey. The global effort included respondents from Africa (19.9%), Asia Pacific (19.7%), Europe (24.3%), North America (9.4%), and South America (26.6%). Providers felt that physical exam-based tasks (e.g., provocative testing, assessing neurologic deficits/myelopathy, etc.) were inferior to inperson exams, while communication-based aspects (e.g., history taking, imaging review, etc.) were equivalent. Participants who performed greater than 50 visits were more likely to believe telemedicine was at least equivalent to in-person visits in the ability to make an accurate diagnosis (OR 2.37, 95% C.I. 1.03–5.43). Compared to in-person encounters, video (versus phone only) visits were associated with increased confidence in the ability of telemedicine to formulate and communicate a treatment plan (OR 3.88, 95% C.I. 1.71–8.84).
Conclusion Spine surgeons are confident in the ability of telemedicine to communicate with patients, but are concerned about its capacity to accurately make physical exam-based diagnoses. Future research should concentrate on standardizing the remote examination and the development of appropriate use criteria in order to increase provider confidence in telemedicine technology.
Keywords Telemedicine · Spine surgery · Examination · International · Survey
Sonu Bhaskar, Sian Bradley, [...], and Maciej Banach Additional article information
Abstract
Technological innovations such as artificial intelligence and robotics may be of potential use in telemedicine and in building capacity to respond to future pandemics beyond the current COVID-19 era. Our international consortium of interdisciplinary experts in clinical medicine, health policy, and telemedicine have identified gaps in uptake and implementation of telemedicine or telehealth across geographics and medical specialties. This paper discusses various artificial intelligence and robotics-assisted telemedicine or telehealth applications during COVID-19 and presents an alternative artificial intelligence assisted telemedicine framework to accelerate the rapid deployment of telemedicine and improve access to quality and cost-effective healthcare. We postulate that the artificial intelligence assisted telemedicine framework would be indispensable in creating futuristic and resilient health systems that can support communities amidst pandemics.
Keywords: telehealth, digital medicine, pandemic (COVID-19), robotics, telemedicine, artificial intelligence, coronavirus disease 2019 (COVID-19)
Caleb Schroeder ⁎
Mary Lanning Healthcare, 715 N Kansas Ave., Suite 205, Hastings, NE, 68901
article info
Article history:
Received 7 February 2019
Received in revised form 5 June 2019
Accepted 17 June 2019
Available online 12 July 2019
Background: Telemedicine has had limited implementation for general surgery. The purpose of this study was to evaluate telemedicine for the initial evaluation of patients in the clinic and hospital settings.
Methods: Synchronous telemedicine consults were conducted by a single surgeon to a rural hospital and clinic. Reasons for consult, adequacy of consult, days saved by telemedicine consult compared to standard practice, correlation of telemedicine and in-person physical exam, and number of patients who required procedures were evaluated.
Results: On average, patients were evaluated 7.4 days more rapidly than if the consult had been done by our standard practice. Telemedicine was adequate for all patients in this study.
Conclusions: This is the first study using telemedicine for the initial consult of general surgery patients in the hospitalized and clinic setting in North America. The physical exam remains an important component of the general surgery evaluation, and special attention must be considered when structuring the telemedicine program. Telemedicine is an effective and expedient way to provide consultation for general surgery patients. Further study is needed to determine which general surgery issues are not amendable to telemedicine consultation, and to determine other surgical specialties that could utilize telemedicine in their practice.
© 2019 The Author. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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
