Volume 11, Issue 2 (2023)                   Health Educ Health Promot 2023, 11(2): 245-253 | Back to browse issues page


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Jalali S, Bahador F, Ameri F, Dastani M, Hajipourtalebi A, Sabahi A. A Systematic Review on the Use of E-health for COVID-19 Pandemic Management. Health Educ Health Promot 2023; 11 (2) :245-253
URL: http://hehp.modares.ac.ir/article-5-67223-en.html
1- Department of Health Information Technology, Ferdows School of Health and Allied Medical Sciences, Birjand University of Medical Sciences, Birjand, Iran
2- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
3- “Student Research Committee” and “Department of Health Information Technology, School of Paramedical Sciences”, Mashhad University of Medical Sciences, Mashhad, Iran
4- Department of Statistics and Information Technology, Gonabad University of Medical Sciences, Gonabad, Iran
5- Department of Health Information Technology, Army University of Medical Sciences, Tehran, Iran
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Introduction
The coronavirus disease 2019 (COVID-19), first reported in Wuhan, China, in late 2019, posed a serious global challenge to public and individual health [1, 2]. According to literature and evidence, COVID-19 is a highly contagious viral illness rapidly spreading worldwide [3-5]. The World Health Organization (WHO) declared the COVID-19 outbreak a global pandemic on March 11, 2020 [6]. The disease has rapidly spread, so that a total of 213,752,662 cases were affected by COVID-19, and 4,459,381 deaths were reported to the WHO by August 26. In Iran, 4,796,377 cases were affected by COVID-19, and 104,022 deaths were reported to the WHO from January 3, 2020, to August 26, 2021 [7].
The COVID-19 pandemic has resulted in heavy casualties in terms of disease burden, as well as mortality and morbidity worldwide [8, 9]. This pandemic challenged the world health system and rapidly spread to other economic and social spheres [10].
Iranian society was not spared from this pandemic and its challenges, and hospitals also faced financial difficulties [11]. The costs of medical centers have increased due to factors such as the allocation of some hospitals as reception centers for patients with COVID-19, the duration of therapy, the follow-up care after patients' discharge, and the construction of hospice centers [12]. Other reasons for their income decline are the cancellation of elective surgery, the provision of personal protective equipment, the preparation of welfare facilities for medical staff, and the requirements for consideration of various aspects of public health and public education [13].
This disease has not only affected people's health but also caused social, economic, and political losses [14]. Accordingly, researchers from around the world are focusing on virus identification, treatment, and vaccine development. However, applying some technologies and systems to combat the emergence of the disease, stop its spread, and disease management is of great importance [15].
The E-health programs can reduce the spread of COVID-19, save the lives of people and healthcare providers, and significantly help better the management of epidemics worldwide [16]. E-health is the application of Information and Communication Technologies (ICT) for health, and these technologies are increasingly used due to the capacity of E-health programs to provide healthcare services in many remote and deprived communities where access to healthcare services is difficult [17]. According to the WHO reports, the E-health tool is the most important program for transforming the health system in the 21st century [18].
The E-health tool is an electronic device or monitoring system used by physicians in healthcare or individuals to monitor or improve their health. It refers to online and offline computer-based applications as well [19]. These virtual platforms help physicians easily diagnose the early symptoms of COVID-19 before the patient arrives at the hospital [15]. Al-Ruzzieh et al., in their systematic review entitled “Study of the role of E-health in the control and management of COVID-19”, concluded that E-health is an alternative to education, data analysis, and safe healthcare through improved coordination and secure communication, contributing the control and management of COVID-19 [20]. Bitar and Alismail, in their SWIFT-review study, suggested that the application of E-health tools, telehealth, and/or telemedicine is required to provide healthcare services to patients with chronic diseases during the COVID-19 pandemic and even in the future and post-COVID-19 crisis [21].
According to the results of the study by Doraiswamy et al., the widespread use of telemedicine during the COVID-19 pandemic is required to manage an extensive range of non-communicable and contagious diseases, including COVID-19, as well as medical education [22]. In addition, Mohammadzadeh et al., in a study entitled “An overview of the applications of information technology in the management of COVID-19”, found that the application of distance health technologies such as telemedicine has been more tangible to follow social distance. The results of this study also showed that screening systems and rapid diagnosis with the help of artificial intelligence are useful due to the similarity of the symptoms of COVID-19 with other respiratory diseases [23].
Since E-health is widely used in the healthcare system and its application advantages in the management and provision of healthcare are significant, the characteristics of the Iranian healthcare structure necessitate the development of this technology, particularly during the COVID-19 pandemic. Therefore, the present systematic review aimed to investigate the impact of the application of E-health tools in the management of COVID-19 disease in Iran.

Information and Methods
Information sources and search strategy
The present study is a systematic review to apply E-health tools in COVID-19 management in Iran. For this purpose, SID, Magiran, and Irandoc databases were searched to retrieve Persian articles, as well as PubMed and Scopus databases were used to retrieve English articles. Data retrieval and extraction steps were performed based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) model [24]. The resources in the above-mentioned scientific databases were designed based on the search strategy by combining two groups of keywords related to the research topic, including keywords describing E-health and COVID-19 disease. The keywords used to search for articles are listed in Table 1.
Inclusion criteria
The original articles regarding the application of E-health tools in the management of COVID-19, published from December 2019 to August 4, 2021, were included in the study.
Exclusion criteria
The articles published in a period other than or unrelated to the COVID-19 pandemic were excluded from the study. The papers published in languages other than Persian and English were also excluded from the investigation. Other types of articles and studies, including reviews, short communications, letters to the editor, case reports, and technical reports, were excluded as well.
Study selection and data extraction
Following the selection of studies based on the inclusion and exclusion criteria, data were collected using a data extraction form following the study objectives. The titles, abstracts, and full manuscripts were independently reviewed by two researchers, and if there was a discrepancy in the articles, they were referred to another person. The data extraction form consisted of eight parts, including the name of the first author and year of publication, the study objective, target population, methodology, impact of E-health tool on the COVID-19 management, applied technologies, duration of technology application, and conclusion. Following the data collection using the data extraction form, the data were analyzed using the content analysis method. Afterward, the results were summarized and reported based on the study objectives in the form of tables, diagrams, and figures. The retrieved articles were entered into the resource management software (Endnote).

Table 1) Search strategy in scientific databases


Findings
Number of studies
In the initial review of five databases, a total of 4,127 articles were retrieved and entered into the software. Ten articles ultimately remained after removing duplicate, irrelevant items based on the evaluation of the title, abstract, and full text. The search strategy of these articles is shown in Figure 1. Among ten studies, five articles were clinical trials (Four quasi-experimental and one experimental), four articles were developmental, and one paper was a descriptive-applied study.


Figure 1) Diagram of the article selection process based on PRISMA workflow

Objectives of studies
40% of articles (n=4) mainly focused on the impact of E-health tools on reducing fear and anxiety caused by COVID-19, 10% of articles (n=1) on the impact of E-health tools on reducing fear and anxiety associated with prevention of COVID-19 and adherence to a treatment regimen. Moreover, 30% of the articles (n=3) analyzed the impact of E-health tool on the diagnosis of COVID-19, 10% of the articles (n=1) considered the impact of E-health tool in suppressing the spread and control of the disease, and 10% (n=1) reviewed the system for quick response to questions related to COVID-19 and increasing the speed of access of medical staff to medical, therapeutic, and diagnostic data using E-health.
Technologies applied in E-health and the duration of their usability
To implement E-health tools, cyberspace technologies, artificial intelligence algorithms, and data mining techniques were used in 50% (n=5), 40% (n=4), and 10% (n=1) of articles, respectively. The technologies used in cyberspace in 40% of articles (n=4) included voice, text message, and video calling in the social media platform WhatsApp, Telegram, Instagram, as well as iGap, and no special platform was mentioned for 10% of articles (n=1).
The artificial intelligence algorithms were used in the articles to model and design systems for determining the pulmonary involvement of patients with COVID-19, predicting COVID-19 disease, suppressing and controlling the pandemic, and quick responding to questions related to COVID-19 for diagnostic and therapeutic purposes. Moreover, data mining techniques were used for the timely and effective diagnosis of COVID-19 disease.
Furthermore, the intervention duration using E-health services varied between 2-8 weeks (Table 2).

Table 2) Major and descriptive features of E-health tool in literature






Discussion
The present systematic review aimed to study the impact of the E-health application in the management of COVID-19 in Iran. The results indicated that the E-health tool application has a significant impact on the management of COVID-19 disease, so that the application of the E-health tool has an effective role in reducing fear and anxiety caused by COVID-19, increasing adherence to treatment, suppressing the spread and control of disease, and helping to diagnose and preventing behaviors. The application of interactive tools of cyberspace and the web, artificial intelligence, and data mining have been the most significant technologies applied in E-health.
In general, telemedicine services, mobile health, e-learning, social media, health data analysis, big data, and electronic health records are among the important areas of E-health [20, 35].
According to the results of the present study, in half of the studies, cyberspace and web-based technologies were used to manage the COVID-19 crisis. Since cyberspace and social networks have many opportunities and threats, it is necessary to understand cyberspace properly and use it correctly [36]. Therefore, experts' continuous evaluation of cyberspace and social networks, particularly during the COVID-19 pandemic, seems necessary due to their ubiquity and strong presence in human life. On the other hand, it is also recommended to pay attention to the national internet network, strengthen the intra-infrastructure of cyberspace, and support internal messengers.
According to the results of a review study by Kalhori et al. [37] in 2021 on digital health strategies to control the COVID-19 pandemic in countries with a high prevalence of the disease, although Italy is one of the countries with the highest prevalence of COVID-19, E-health technologies are not significant to prevent and manage the COVID-19 pandemic in the country. Their study also reported that half of Italy's population has a very low level of digital literacy. Since E-health literacy is required to make better use of E-health technologies [38], it is important to address E-health literacy to use these technologies appropriately.
Several states and healthcare institutions apply social media tools to promote patient care, patient education, professional networking, and organizational promotion [39]. The WHO has used WhatsApp and Facebook to send COVID-19 data to billions of people in many languages, including English, Arabic, and Spanish [20]. The application of cyberspace, such as social networks, has been welcomed by the people due to its widespread use in today's societies, as well as its interactive and instant messaging features [40]. Therefore, in the current situation where social networks play a pivotal role in managing COVID-19, their content, function, and role in the management of COVID-19 should be further studied and evaluated [41].
According to the results of a study by Greenhalgh et al. [42] on video consultations for COVID-19, E-health technologies such as video counseling may be useful for patients with COVID-19 in cases where people need to speak or are anxious and have mild symptoms, which is in line with the results of the present study; i.e., E-health has been effective in reducing fear and anxiety caused by COVID-19 disease in 40% of cases in the present study.
Video counseling allows many people, particularly suspicious patients and susceptible individuals, to stay safe at home and consult with their physicians through virtual platforms [20].
Huang et al. confirmed the effectiveness of online monitoring in managing COVID-19. Video conferencing and telecommunications also play a key role in preventing the spread of COVID-19 by providing social distance [43, 44].
Al-Ruzzieh et al. [20], in a study on the role of E-health in improving control and management of COVID-19 pandemic, found that E-health plays a pivotal role in strengthening efforts to control and manage COVID-19. Moreover, the results of a study conducted by Amiri [45] on the role of E-health at the beginning of the COVID-19 crisis revealed that the application of different E-health technologies is effective during the COVID-19 pandemic, and machine learning algorithms and artificial intelligence methods can be used to diagnose COVID-19 disease, which is consistent with the results of the present study.
Decision Support Systems (DSS) can efficiently contribute to the early diagnosis, prevention of disease progression, and reduction of costs [46]. These systems can purposefully provide an intelligent approach for users (Healthcare professionals, patients, and caregivers) to monitor, manage, and improve patients’ health with critical information. In addition, decision support systems have the potential to empower patients with a deeper awareness of their condition [47].
Deep learning algorithms are among the most successful artificial intelligence techniques and are effective tools to assist radiologists in analyzing large volumes of X-ray images that can be significant for the efficient detection and screening of COVID-19 [48].
China's experience in managing COVID-19 can be helpful as it has successfully controlled the Coronavirus. In China, a variety of approaches are being used to manage the COVID-19 pandemic, ranging from web-based and mobile-based systems to cloud-based systems, decision support systems, and intelligent systems. Some of the most important digital tools used in this country are drones, robots, mobile apps, educational websites and media, video conferencing, intelligent infection detectors, intelligent patient trackers, and distance medical systems. The E-health program called Health Code is extensively employed to show the health status of individuals in this country as well [49, 50].
The results of the present study found the researchers’ consideration to the application of health information technologies, mainly during the COVID-19 pandemic, and awareness of the widespread use of these technologies. Therefore, it is suggested that health policymakers consider a variety of practical strategies, including financing, implementation, and legal requirements for applying these technologies to use E-health capabilities in disease management more effectively.

Conclusion
Practical management of the COVID-19 pandemic requires new technologies that can support management at different stages of the disease. E-health technologies effectively support organizations and communities during the COVID-19 pandemic. These technologies play a key role in managing the COVID-19 pandemic with the capability of quickly and extensively distributing information, tracking patients, and creating virtual locations for consultation and daily visits. The E-health technologies will also be useful tools to rapidly share information, prevent the disease spread, promote healthy behaviors, feel social belonging, and help to reduce the psychological burden of COVID-19 disease.

Acknowledgements: The authors of the study thank Birjand University of Medical Sciences for their cooperation in conducting the study.
Ethical Permission: Ethical approval was received from the Research Ethics Committee of Birjand University of Medical Sciences. (Ethical number: IR.BUMS.REC.1401.064).
Conflict of Interests: The authors declare no conflict of interest in the current study.
Authors’ Contribution: Jalali S (First Author), Main Researcher/Discussion Writer (20%); Bahador F (Second Author), Methodologist/Statistical Analyst (15%); Ameri F (Third Author), Introduction Writer/Methodologist/Main Researcher (20%); Dastani M (Fourth Author), Methodologist/Assistant Researcher (15%); Hajipour Talebi A (Fifth Author), Methodologist/Assistant Researcher (10%); Sabahi A (Sixth Author), Methodologist/Main Researcher/Statistical Analyst (20%)
Funding: The authors received no financial support for the conduct of the study.

 
Article Type: Systematic Review | Subject: Health Communication
Received: 2023/02/1 | Accepted: 2023/03/22 | Published: 2023/04/16
* Corresponding Author Address: Department of Health Information Technology, Ferdows School of Health and Allied Medical Sciences, Birjand University of Medical Sciences, Shahid Motahari Street, Birjand, Iran. Postal Code: 9717853577 (sabahiazam858@gmail.com)

References
1. Li H, Liu SM, Yu XH, Tang SL, Tang CK. Coronavirus disease 2019 (COVID-19): Current status and future perspectives. Int J Antimicrob Agents. 2020;55(5):105951. [Link] [DOI:10.1016/j.ijantimicag.2020.105951]
2. Wu YC, Chen CS, Chan YJ. The outbreak of COVID-19: An overview. J Chin Med Assoc. 2020;83(3):217-20. [Link] [DOI:10.1097/JCMA.0000000000000270]
3. Cascella M, Rajnik M, Aleem A, Dulebohn SC, Di Napoli R. Features, evaluation, and treatment of coronavirus (COVID-19). Florida: Statpearls; 2022. [Link]
4. Harapan H, Itoh N, Yufika A, Winardi W, Keam S, Te H, et al. Coronavirus disease 2019 (COVID-19): A literature review. J Infect Public Health. 2020;13(5):667-73. [Link] [DOI:10.1016/j.jiph.2020.03.019]
5. Hu B, Guo H, Zhou P, Shi ZL. Characteristics of SARS-CoV-2 and COVID-19. Nat Rev Microbiol. 2021;19(3):141-54. [Link] [DOI:10.1038/s41579-020-00459-7]
6. Gül Ü. COVID-19 and dermatology. Turk J Med Sci. 2020;50(8):1751-9. [Link] [DOI:10.3906/sag-2005-182]
7. Lee H, Noh EB, Park SJ, Nam HK, Lee TH, Lee GR, et al. COVID-19 vaccine perception in South Korea: Web crawling approach. JMIR Public Health Surveill. 2021;7(9):e31409. [Link] [DOI:10.2196/31409]
8. Lucia VC, Kelekar A, Afonso NM. COVID-19 vaccine hesitancy among medical students. J Public Health (Oxf). 2021;43(3):445-9. [Link] [DOI:10.1093/pubmed/fdaa230]
9. Pires SM, Wyper G, Wengler A, Peñalvo JL, Haneef R, Moran D, et al. Burden of disease of COVID-19: Strengthening the collaboration for national studies. Front Public Health. 2022:1614. [Link] [DOI:10.3389/fpubh.2022.907012]
10. Chakraborty I, Maity P. COVID-19 outbreak: Migration, effects on society, global environment and prevention. Sci Total Environm. 2020;728:138882. [Link] [DOI:10.1016/j.scitotenv.2020.138882]
11. Sajadi H. Identifying and analyzing the resilience components of Iranian society in the face of COVID-19 outbreak crisis. Soft Power Stud. 2020;9(2):91-117.[Persian] [Link]
12. Sheinson D, Dang J, Shah A, Meng Y, Elsea D, Kowal S. A cost- effectiveness framework for COVID-19 treatments for hospitalized patients in the United States. Adv Ther. 2021;38:1811-31. [Link] [DOI:10.1007/s12325-021-01654-5]
13. Roshanzadeh M, Jamalinik M, Hasheminik M, Tajabadi A. Stigma of Covid-19: The basic challenge in health economics. Iran Occupational Health J. 2020;17(1):137-41. [Link]
14. Yang BX, Xia L, Huang R, Chen P, Luo D, Liu Q, et al. Relationship between eHealth literacy and psychological status during COVID-19 pandemic: A survey of Chinese residents. J Nurs Manag. 2021;29(4):805-12. [Link] [DOI:10.1111/jonm.13221]
15. Alonso SG, Marques G, Barrachina I, Garcia Zapirain B, Arambarri J, Salvador JC, et al. Telemedicine and E-health research solutions in literature for combatting COVID-19: A systematic review. Health Technol (Berl). 2021;11(2):257-66. [Link] [DOI:10.1007/s12553-021-00529-7]
16. Budd J, Miller BS, Manning EM, Lampos V, Zhuang M, Edelstein M, et al. Digital technologies in the public-health response to COVID-19. Nat Med. 2020;26(8):1183-92. [Link] [DOI:10.1038/s41591-020-1011-4]
17. Sabahi A, Ahmadian L, Mirzaee M. Communicating laboratory results through a web site: Patients' priorities and viewpoints. J Clin Lab Anal. 2018;32(6):e22422. [Link] [DOI:10.1002/jcla.22422]
18. Herrera Peco I, Núñez CR, Jiménez Gómez B, Romero Magdalena CS, De Gracia EB. COVID-19 y vacunación: Análisis del papel de las instituciones públicas en la difusión de información a través de Twitter. Rev Esp Salud Pública. 2021;95(16):16. [Link]
19. Kampmeijer R, Pavlova M, Tambor M, Golinowska S, Groot W. The use of E-health and m-health tools in health promotion and primary prevention among older adults: A systematic literature review. BMC Health Serv Res. 2016;16(5):467-79. [Link] [DOI:10.1186/s12913-016-1522-3]
20. Al-Ruzzieh MA, Ayaad O, Qaddumi B. The role of E-health in improving control and management of COVID 19 outbreak: Current perspectives. Int J Adolesc Med Health. 2020;34(4):139-45. [Link] [DOI:10.1515/ijamh-2020-0072]
21. Bitar H, Alismail S. The role of eHealth, telehealth, and telemedicine for chronic disease patients during COVID-19 pandemic: A rapid systematic review. Digit Health. 2021;7:20552076211009396. [Link] [DOI:10.1177/20552076211009396]
22. Doraiswamy S, Abraham A, Mamtani R, Cheema S. Use of telehealth during the COVID-19 pandemic: Scoping review. J Med Internet Res. 2020;22(12):e24087. [Link] [DOI:10.2196/24087]
23. Mohammadzadeh Z, Maserat E, Kariminezhad R. Application of information technology models, approaches and tools in Covid-19 management: Rapid review. Depiction Health. 2021;12(1):77-95. [Persian] [Link] [DOI:10.34172/doh.2021.09]
24. Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009;151(4):264-9. [Link] [DOI:10.7326/0003-4819-151-4-200908180-00135]
25. Shomaliahmadabadi M, Barkhordari Ahmadabadi A. The effectiveness of case virtual reality therapy on corona anxiety. Rooyesh. 2020;9(7):163-70. [Persian] [Link]
26. Jani S, Mikaeili N, Rahimi P. The effectiveness of internet-delivered cognitive behaviour therapy on reducing corona-related anxiety in Parsabad health care workers. Inform Commun Technol Educ Sci. 2020;10(40):129-45. [Persian] [Link]
27. Shabani MM. The effectiveness of distance education of the educational package for the prevention of the disease of Covid-19 (Virus (SARS-CoV-2) on reducing the fear and anxiety of the Covid-19 disease (New Corona virus 2019) in the virtual class of English language learners-pilot study. J Iran Anesthesiol Special Care Association. 2019;42(4):58-67.[Persian] [Link]
28. Karsaz A. Evaluation of lung involvement in patients with coronavirus disease from chest CT images using multi-objective self-adaptive differential evolution approach. J Control. 2021;14(5):1-14. [Persian] [Link] [DOI:10.52547/joc.14.5.1]
29. Kamarzarrin M. Modeling of self-assessment system of COVID-19 disease diagnosis using Type-2 Sugeno fuzzy inference system. J Control. 2021;14(5):49-57. [Persian] [Link] [DOI:10.52547/joc.14.5.49]
30. Mohammadzadeh A. The effectiveness of electronic health care and pharmacy monitoring program to prevent CoVID-19 (SARS-CoV-2 virus) and reduce of corona disease anxiety after bypass surgery- a pilot study. Q J Nurs Manag (IJNV). 2019;8(3):26-34. [Persian] [Link]
31. Mehra AHA, Shafieirad M, Abbasi Z, Zamani I, Aarabi Z. Fuzzy sliding mode controller design and analysis of an SQEIAR epidemic model for COVID-19 to determine the quarantine rate. J Control. 2021;14(5):59-70. [Persian] [Link] [DOI:10.52547/joc.14.5.59]
32. Mirzaei H, Shahramian I, Khosravi A, Mansouri A. Evaluation of the effect of education based on BASNEF model on reducing death anxiety in diabetic patients with Coronavirus. J Diabetes Nurs 2021. 9(1):1330-8. [Persian] [Link]
33. Yazdinejad M, Kamalo H, Mojahedi E, Kalvani A. Design of Covid 19 question answer system using Brett-Squad model. Sci Eng Elite. 2021;6(1):161-71. [Persian] [Link]
34. Nopour R, Shanbehzadeh M, Kazemi Arpanahi H. Proposing an effective technological solution for the early diagnosis of COVID-19: A data-driven machine learning study. J Modern Med Inform Sci. 2021;7(1):68-78. [Persian] [Link] [DOI:10.52547/jmis.7.1.68]
35. Bokolo AJ. Application of telemedicine and ehealth technology for clinical services in response to COVID-19 pandemic. Health Technol. 2021;11(2):359-66. [Link] [DOI:10.1007/s12553-020-00516-4]
36. Barrett Maitland N, Lynch J. Social media, ethics and the privacy paradox. London: Intechopen; 2020. [Link] [DOI:10.5772/intechopen.90906]
37. Kalhori SRN, Bahaadinbeigy K, Deldar K, Gholamzadeh M, Hajesmaeel-Gohari S, Ayyoubzadeh SM. Digital health solutions to control the COVID-19 pandemic in countries with high disease prevalence: Literature review. J Med Internet Res. 2021;23(3):e19473. [Link] [DOI:10.2196/19473]
38. Witten NA, Humphry J. The electronic health literacy and utilization of technology for health in a remote Hawaiian community: Lana'i. Hawaii J Med Public Health. 2018;77(3):51. [Link]
39. Ventola CL. Social media and health care professionals: Benefits, risks, and best practices. Pharm Ther. 2014;39(7):491. [Link]
40. Cortellazzo L, Bruni E, Zampieri R. The role of leadership in a digitalized world: A review. Front Psychol. 2019:1938. [Link] [DOI:10.3389/fpsyg.2019.01938]
41. Hung M, Lauren E, Hon ES, Birmingham WC, Xu J, Su S, et al. Social network analysis of COVID-19 sentiments: Application of artificial intelligence. J Med Internet Res. 2020;22(8):e22590. [Link] [DOI:10.2196/22590]
42. Greenhalgh T, Wherton J, Shaw S, Morrison C. Video consultations for covid-19. BMJ. 2020;68:m998. [Link] [DOI:10.1136/bmj.m998]
43. Karl KA, Peluchette JV, Aghakhani N. Virtual work meetings during the COVID-19 pandemic: The good, bad, and ugly. Small Group Res. 2021;53(3):343-65. [Link] [DOI:10.1177/10464964211015286]
44. Huang S, Xiao Y, Yan L, Deng J, He M, Lu J, et al. Implications for online management: Two cases with COVID-19. Telemed E Health. 2020;26(4):487-94. [Link] [DOI:10.1089/tmj.2020.0066]
45. Amiri P. The role of electronic health during the Covid-19 crisis: A systematic review of literatures. J Health Biomed Inform. 2020;6(4):358-67. [Persian] [Link]
46. Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digit Med. 2020;3:17. [Linkvv] [DOI:10.1038/s41746-020-0221-y]
47. Souza Pereira L, Ouhbi S, Pombo N. Quality-in-use characteristics for clinical decision support system assessment. Comput Methods Programs Biomed. 2021;207:106169. [Link] [DOI:10.1016/j.cmpb.2021.106169]
48. Zhang J, Xie Y, Li Y, Shen C, Xia Y. Covid-19 screening on chest x-ray images using deep learning based anomaly detection. ArXiv Med. 2020;27. [Link]
49. Ye Q, Zhou J, Wu H. Using information technology to manage the COVID-19 pandemic: Development of a technical framework based on practical experience in China. JMIR Med Inform. 2020;8(6):e19515. [Link] [DOI:10.2196/19515]
50. Gong M, Liu L, Sun X, Yang Y, Wang S, Zhu H. Cloud-based system for effective surveillance and control of COVID-19: Useful experiences from Hubei, China. J Med Internet Res. 2020;22(4):e18948. [Link] [DOI:10.2196/18948]

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