Volume 11, Issue 2 (2023)
Health Educ Health Promot 2023, 11(2): 245-253 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
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
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
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
Abstract: (1086 Views)
Aims: Electronic health can lead to health preservation and promotion using information and communication technologies to receive and record accurate data, appropriate storage, and retrieval, as well as the health information management approach. The present systematic review aimed to assess the E-health application during the COVID-19.
Information & Methods: The present systematic review was done based on PRISMA protocols. The study data were retrieved using the E-health and COVID-19 keywords in the related studies from August 4, 2021, in PubMed, Scopus, Magiran, and Sid databases. Moreover, the inclusion criteria were original research studies that used E-health to manage patients with COVID-19.
Findings: A total of 10 articles were included in the study, 40% of which focused on the impact of E-health on reducing fear and anxiety caused by COVID-19, 30% on the E-health in early diagnosis and progression of the disease, 10% on the E-health application in the field of prevention, 10% on E-health in the field of disease control, and 10% on E-health for quick investigations of the disease process and access new medical information. The used technologies included virtual training through WhatsApp video calling, Instagram, iGap and Telegram voice and text messaging, artificial intelligence, and data mining techniques.
Conclusion: E-health tools played a prominent role during the COVID-19 in the prevention, diagnosis, control, and fear reduction of coronavirus disease. Various practical strategies such as financing, implementation and legal requirements can be considered to effectively use the capabilities of eHealth tools in disease management.
Information & Methods: The present systematic review was done based on PRISMA protocols. The study data were retrieved using the E-health and COVID-19 keywords in the related studies from August 4, 2021, in PubMed, Scopus, Magiran, and Sid databases. Moreover, the inclusion criteria were original research studies that used E-health to manage patients with COVID-19.
Findings: A total of 10 articles were included in the study, 40% of which focused on the impact of E-health on reducing fear and anxiety caused by COVID-19, 30% on the E-health in early diagnosis and progression of the disease, 10% on the E-health application in the field of prevention, 10% on E-health in the field of disease control, and 10% on E-health for quick investigations of the disease process and access new medical information. The used technologies included virtual training through WhatsApp video calling, Instagram, iGap and Telegram voice and text messaging, artificial intelligence, and data mining techniques.
Conclusion: E-health tools played a prominent role during the COVID-19 in the prevention, diagnosis, control, and fear reduction of coronavirus disease. Various practical strategies such as financing, implementation and legal requirements can be considered to effectively use the capabilities of eHealth tools in disease management.
Keywords: Coronavirus [MeSH], E-health [MeSH], Electronic Health Records [MeSH], Telemedicine [MeSH]
Article Type: Systematic Review |
Subject:
Health Communication
Received: 2023/02/1 | Accepted: 2023/03/22 | Published: 2023/04/16
Received: 2023/02/1 | Accepted: 2023/03/22 | Published: 2023/04/16
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]
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |