Intelligent Medicine
Volume 01 · Issue 01 · 2021
Intell Med
- Sections
- Perspective & Comments
- Guideline & Standard
- Inaugural Editorial
- Research Article
- Prevention and Control for COVID-19
- Review
- WHO Digital Health
With the application of Internet of Things, big data, cloud computing, artificial intelligence, and other cutting-edge technologies, China’s medical informatization is developing rapidly. In this paper, we summaried the role of information technology in healthcare sector’s battle against the coronavirus disease 2019 (COVID-19) from the perspectives of early warning and monitoring, screening and diagnosis, medical treatment and scientific research, analyzes the bottlenecks of the development of information technology in the post-COVID-19 era, and puts forward feasible suggestions for further promoting the construction of medical informatization from the perspectives of sharing, convenience, and safety.
The incidence and mortality of colon cancer in China are increasing each year. At present, treatment selection for colon cancer patients mainly depends on imaging results, which require a large number of radiologists to interpret. In China, there is a shortage and uneven distribution of experienced radiologists, which leads to delays and bias in the evaluation of imaging data. Based on these considerations, the Colorectal Surgery Group of the Surgery Branch of the Chinese Medical Association in collaboration with experts at Beihang University has independently developed an artificial intelligence (AI)-based recognition system for the preoperative determination of colon cancer stage to partially replace the work of and relieve the pressure on radiologists. These guidelines aim to standardize the use of AI-based recognition systems in the preoperative staging of colon cancer and guide their clinical application.
在21世纪的头20年里圣世纪以来，随着经济、科学技术的飞速发展，全球医疗卫生界取得了显著的进步。然而，全球医疗资源短缺和区域医疗服务供应不平衡仍然是主要问题。作为拥有14亿人口的世界上人口最多的国家，中国在医学和医疗保健领域面临着巨大的挑战。人口老龄化使慢性病发病率显著增加，保健需求上升，供需关系升级，医疗资源配置不合理。与此同时，欧美发达国家也面临着巨大的医疗保健挑战，包括医疗服务质量、急救服务及时性和医疗保健成本效益。新冠肺炎疫情彻底暴露了新发传染病和突发公共卫生事件防控手段的不足。在这种背景下，新兴的数字和智能技术已成为一种新的有前途的方法，有可能改善大流行后的全球医疗服务。
The use of digital health technologies was an integral part to China’s early response to coronavirus disease 2019 (COVID-19). Existing literatures have analyzed and discussed implemented digital health innovations from the perspective of technologies, whereas how policy mechanisms contributed to the formulation of the digital health landscape for COVID-19 was overlooked. This study aimed to examine the contexts and key mechanisms in China’s rapid mobilization of digital health interventions in response to COVID-19, and to document and share lessons learned.
Policy documents were identified and retrieved from government portals and recognized media outlets. Data on digital health interventions were collected through three consecutive surveys administered between 23 January 2020 and 31 March 2020 by China Academy of Information and Communication Technology (CAICT) affiliated to the Ministry of Industry and Information Technology (MIIT). Participants were member companies of the Internet Health alliance established by MIIT and the National Health Commission (NHC) in June 2016. Self-report digital interventions focusing on social and economic recovery were excluded. Two hundred and sixty-six unique digital health interventions meeting our criteria were extracted from 175 narratives on digital health interventions submitted by 116 participating companies. Thematic analysis was conducted to describe the scope and priority of policies advocating for the use of digital health technologies and the implementation pattern of digital health interventions. Data limitations precluded an evaluation of the impact of digital health interventions over a longer time frame.
Between January and March 2020, national policy directives promoting the use of digital technologies for the containment of COVID-19 collectively advocated for use cases in emergency planning and preparedness, public health response, and clinical services. Interventions to strengthen clinical services were mentioned more than the other two themes (n = 15, 62.5% (15/24)). Using digital technologies for public health response was mentioned much less than clinical services (n = 5, 20.8% (5/24)). Emergency planning and preparedness was least mentioned (n = 4, 16.7% (4/24)). Interventions in support of clinical services disproportionately favored healthcare facilities in less resource-constraint settings. Digital health interventions shared the same pattern of distribution. More digital health technologies were implemented in clinical services (n = 103, 38.7% (103/266)) than that in public health response (n = 91, 34.2% (91/266)). Emergency planning and preparedness had the least self-reported digital health interventions (n = 72, 27.1% (72/266)). We further identified case studies under each theme in which the wide use of digital health technologies highlighted contextual factors and key enabling mechanisms.
The contextual factors and key enabling mechanisms through the use of policy instruments to promote digital health interventions for COVID-19 in China include pathway of policy directives influencing the private sector using a decentralized system, the booming digital health landscape before COVID-19, agility of the public sector in introducing regulatory flexibilities and incentives to mobilize the private sector.
The ongoing coronavirus disease 2019 (COVID-19) pandemic has put radiologists at a higher risk of infection during the computer tomography (CT) examination for the patients. To help settling these problems, we adopted a remote-enabled and automated contactless imaging workflow for CT examination by the combination of intelligent guided robot and automatic positioning technology to reduce the potential exposure of radiologists to 2019 novel coronavirus (2019-nCoV) infection and to increase the examination efficiency, patient scanning accuracy and better image quality in chest CT imaging.
From February 10 to April 12, 2020, adult COVID-19 patients underwent chest CT examinations on a CT scanner using the same scan protocol except with the conventional imaging workflow (CW group) or an automatic contactless imaging workflow (AW group) in Wuhan Leishenshan Hospital (China) were retrospectively and prospectively enrolled in this study. The total examination time in two groups was recorded and compared. The patient compliance of breath holding, positioning accuracy, image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and compared between the two groups.
Compared with the CW group, the total positioning time of the AW group was reduced ((118.0 ± 20.0) s vs. (129.0 ± 29.0) s, P = 0.001), the proportion of scanning accuracy was higher (98% vs. 93%), and the lung length had a significant difference ((0.90±1.24) cm vs. (1.16±1.49) cm, P = 0.009). For the lesions located in the pulmonary centrilobular and subpleural regions, the image noise in the AW group was significantly lower than that in the CW group (centrilobular region: (140.4 ± 78.6) HU vs. (153.8 ± 72.7) HU, P = 0.028; subpleural region: (140.6 ± 80.8) HU vs. (159.4 ± 82.7) HU, P = 0.010). For the lesions located in the peripheral, centrilobular and subpleural regions, SNR was significantly higher in the AW group than in the CW group (centrilobular region: 6.6 ± 4.3 vs. 4.9 ± 3.7, P = 0.006; subpleural region: 6.4 ± 4.4 vs. 4.8 ± 4.0, P < 0.001).
The automatic contactless imaging workflow using intelligent guided robot and automatic positioning technology allows for reducing the examination time and improving the patient’s compliance of breath holding, positioning accuracy and image quality in chest CT imaging.
No DataDuring the highly infectious pandemic of coronavirus disease 2019 (COVID-19), artificial intelligence (AI) has provided support in addressing challenges and accelerating achievements in controlling this public health crisis. It has been applied in fields varying from outbreak forecasting to patient management and drug/vaccine development. In this paper, we specifically review the current status of AI-based approaches for patient management. Limitations and challenges still exist, and further needs are highlighted.
Coronavirus disease 2019 (COVID-19) made a huge effect globally. With the assistance of mixed reality (MR) technology, complicated clinical works became easier to carry out and the condition had been greatly improved with high-tech advantages such as improved convenience, better understanding and communication, higher security, and medical resource saving. This study aimed to introduce one kind of MR application in the fight against COVID-19 and anticipate more feasible smart healthcare applications to enhance our strength for the final victory.
In recent years, noncontact crewless operations have become prominent in the field of environmental disinfection. Robots that automatically disinfect the air and surfaces of hospital environments can help reduce the human resources spent on environmental cleaning and disinfection and minimize the risk of occupational exposure for staff. These robots also facilitate informatized management of environmental disinfection, reduce costs, and increase the efficiency of disinfection efforts.
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